You searched for subject:(Swarm Intelligence)
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Western Carolina University
1.
Proffitt, Matthew R.
Optimization of swarm robotic constellation communication
for object detection and event recognition.
Degree: 2011, Western Carolina University
URL: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874
► Swarm robotics research describes the study of how a group of relatively simple physically embodied agents can, through their interaction collectively accomplish tasks which are…
(more)
▼ Swarm robotics research describes the study of how a
group of relatively simple physically embodied agents can, through
their interaction collectively accomplish tasks which are far
beyond the capabilities of a single agent. This self organizing but
decentralized form of
intelligence requires that all members are
autonomous and act upon their available information. From this
information they are able to decide their behavior and take the
appropriate action. A global behavior can then be witnessed that is
derived from the local behaviors of each agent. The presented
research introduces the novel method for optimizing the
communication and the processing of communicated data for the
purpose of detecting large scale meta object or event, denoted as
meta event, which are unquantifiable through a single robotic
agent. The ability of a
swarm of robotic agents to cover a
relatively large physical environment and their ability to detect
changes or anomalies within the environment is especially
advantageous for the detection of objects and the recognition of
events such as oil spills, hurricanes, and large scale security
monitoring. In contrast a single robot, even with much greater
capabilities, could not explore or cover multiple areas of the same
environment simultaneously. Many previous
swarm behaviors have been
developed focusing on the rules governing the local agent to agent
behaviors of separation, alignment, and cohesion. By effectively
optimizing these simple behaviors in coordination, through
cooperative and competitive actions based on a chosen local
behavior, it is possible to achieve an optimized global emergent
behavior of locating a meta object or event. From the local to
global relationship an optimized control algorithm was developed
following the basic rules of
swarm behavior for the purpose of meta
event detection and recognition. Results of this optimized control
algorithm are presented and compared with other work in the field
of
swarm robotics.; Communication, Coordination, Detection,
Optimization, Robotics,
Swarm
Advisors/Committee Members: Brian Howell (advisor).
Subjects/Keywords: Robotics; Swarm intelligence
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APA ·
Chicago ·
MLA ·
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APA (6th Edition):
Proffitt, M. R. (2011). Optimization of swarm robotic constellation communication
for object detection and event recognition. (Masters Thesis). Western Carolina University. Retrieved from http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874
Chicago Manual of Style (16th Edition):
Proffitt, Matthew R. “Optimization of swarm robotic constellation communication
for object detection and event recognition.” 2011. Masters Thesis, Western Carolina University. Accessed December 11, 2019.
http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874.
MLA Handbook (7th Edition):
Proffitt, Matthew R. “Optimization of swarm robotic constellation communication
for object detection and event recognition.” 2011. Web. 11 Dec 2019.
Vancouver:
Proffitt MR. Optimization of swarm robotic constellation communication
for object detection and event recognition. [Internet] [Masters thesis]. Western Carolina University; 2011. [cited 2019 Dec 11].
Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874.
Council of Science Editors:
Proffitt MR. Optimization of swarm robotic constellation communication
for object detection and event recognition. [Masters Thesis]. Western Carolina University; 2011. Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874

Baylor University
2.
Yu, Albert R.
Optimizing multi-agent dynamics for underwater tactical applications.
Degree: Engineering., 2011, Baylor University
URL: http://hdl.handle.net/2104/8181
► Large groups of autonomous agents, or swarms, can exhibit complex emergent behaviors that are difficult to predict and characterize from their low-level interactions. These emergent…
(more)
▼ Large groups of autonomous agents, or swarms, can exhibit complex emergent behaviors that are difficult to predict and characterize from their low-level interactions. These emergent behaviors can have hidden implications for the performance of the
swarm should the operational theater be perturbed. Thus, designing the optimal rules of operation for coordinating these multi-agent systems in order to accomplish a given task often requires simulations or expensive implementations. This thesis project examines
swarm dynamics and the use of inversion to optimize the rules of operation of a large group of autonomous agents in order to accomplish missions of tactical relevance: specifically missions concerning underwater frequency-based standing patrols and point-defense between two competing swarms. Modified genetic algorithms and particle
swarm optimization are utilized in the inversion process, producing various competing tactical responses and patrol behaviors.
Swarm inversion is shown to yield effective and often creative solutions for guiding swarms of autonomous agents.
Advisors/Committee Members: Marks, Robert J (advisor).
Subjects/Keywords: Swarm intelligence.;
Multi-agent systems.;
Swarm inversion.
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APA ·
Chicago ·
MLA ·
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to Zotero / EndNote / Reference
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APA (6th Edition):
Yu, A. R. (2011). Optimizing multi-agent dynamics for underwater tactical applications.
(Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/8181
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Yu, Albert R. “Optimizing multi-agent dynamics for underwater tactical applications.
” 2011. Thesis, Baylor University. Accessed December 11, 2019.
http://hdl.handle.net/2104/8181.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Yu, Albert R. “Optimizing multi-agent dynamics for underwater tactical applications.
” 2011. Web. 11 Dec 2019.
Vancouver:
Yu AR. Optimizing multi-agent dynamics for underwater tactical applications.
[Internet] [Thesis]. Baylor University; 2011. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/2104/8181.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Yu AR. Optimizing multi-agent dynamics for underwater tactical applications.
[Thesis]. Baylor University; 2011. Available from: http://hdl.handle.net/2104/8181
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Windsor
3.
Zhang, Miao.
Digital Filter Design Using Improved Teaching-Learning-Based Optimization.
Degree: PhD, Electrical and Computer Engineering, 2019, University of Windsor
URL: https://scholar.uwindsor.ca/etd/7856
► Digital filters are an important part of digital signal processing systems. Digital filters are divided into finite impulse response (FIR) digital filters and infinite impulse…
(more)
▼ Digital filters are an important part of digital signal processing systems. Digital filters are divided into finite impulse response (FIR) digital filters and infinite impulse response (IIR) digital filters according to the length of their impulse responses. An FIR digital filter is easier to implement than an IIR digital filter because of its linear phase and stability properties. In terms of the stability of an IIR digital filter, the poles generated in the denominator are
subject to stability constraints. In addition, a digital filter can be categorized as one-dimensional or multi-dimensional digital filters according to the dimensions of the signal to be processed. However, for the design of IIR digital filters, traditional design methods have the disadvantages of easy to fall into a local optimum and slow convergence.
The Teaching-Learning-Based optimization (TLBO) algorithm has been proven beneficial in a wide range of engineering applications. To this end, this dissertation focusses on using TLBO and its improved algorithms to design five types of digital filters, which include linear phase FIR digital filters, multiobjective general FIR digital filters, multiobjective IIR digital filters, two-dimensional (2-D) linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters. Among them, linear phase FIR digital filters, 2-D linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters use single-objective type of TLBO algorithms to optimize; multiobjective general FIR digital filters use multiobjective non-dominated TLBO (MOTLBO) algorithm to optimize; and multiobjective IIR digital filters use MOTLBO with Euclidean distance to optimize. The design results of the five types of filter designs are compared to those obtained by other state-of-the-art design methods. In this dissertation, two major improvements are proposed to enhance the performance of the standard TLBO algorithm. The first improvement is to apply a gradient-based learning to replace the TLBO learner phase to reduce approximation error(s) and CPU time without sacrificing design accuracy for linear phase FIR digital filter design. The second improvement is to incorporate Manhattan distance to simplify the procedure of the multiobjective non-dominated TLBO (MOTLBO) algorithm for general FIR digital filter design. The design results obtained by the two improvements have demonstrated their efficiency and effectiveness.
Advisors/Committee Members: Kwan, H.K..
Subjects/Keywords: digital filters; swarm intelligence algorithms
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhang, M. (2019). Digital Filter Design Using Improved Teaching-Learning-Based Optimization. (Doctoral Dissertation). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/7856
Chicago Manual of Style (16th Edition):
Zhang, Miao. “Digital Filter Design Using Improved Teaching-Learning-Based Optimization.” 2019. Doctoral Dissertation, University of Windsor. Accessed December 11, 2019.
https://scholar.uwindsor.ca/etd/7856.
MLA Handbook (7th Edition):
Zhang, Miao. “Digital Filter Design Using Improved Teaching-Learning-Based Optimization.” 2019. Web. 11 Dec 2019.
Vancouver:
Zhang M. Digital Filter Design Using Improved Teaching-Learning-Based Optimization. [Internet] [Doctoral dissertation]. University of Windsor; 2019. [cited 2019 Dec 11].
Available from: https://scholar.uwindsor.ca/etd/7856.
Council of Science Editors:
Zhang M. Digital Filter Design Using Improved Teaching-Learning-Based Optimization. [Doctoral Dissertation]. University of Windsor; 2019. Available from: https://scholar.uwindsor.ca/etd/7856

University of Saskatchewan
4.
Moallem, Azin.
Using swarm intelligence for distributed job scheduling on the grid.
Degree: 2009, University of Saskatchewan
URL: http://hdl.handle.net/10388/etd-04132009-123250
► With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. Grids are playing an important…
(more)
▼ With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. Grids are playing an important and growing role in today networks. The huge amount of computations a Grid can fulfill in a specific time cannot be done by the best super computers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized by a good load balancing algorithm. The purpose of such algorithms is to make sure all nodes are equally involved in Grid computations. This research proposes two new distributed
swarm intelligence inspired load balancing algorithms. One is based on ant colony optimization and is called AntZ, the other one is based on particle
swarm optimization and is called ParticleZ. Distributed load balancing does not incorporate a single point of failure in the system. In the AntZ algorithm, an ant is invoked in response to submitting a job to the Grid and this ant surfs the network to find the best resource to deliver the job to. In the ParticleZ algorithm, each node plays a role as a particle and moves toward other particles by sharing its workload among them. We will be simulating our proposed approaches using a Grid simulation toolkit (GridSim) dedicated to Grid simulations. The performance of the algorithms will be evaluated using several performance criteria (e.g. makespan and load balancing level). A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches will also be provided. Experimental results show the proposed algorithms (AntZ and ParticleZ) can perform very well in a Grid environment. In particular, the use of particle
swarm optimization, which has not been addressed in the literature, can yield better performance results in many scenarios than the ant colony approach.
Advisors/Committee Members: Ludwig, Simone A..
Subjects/Keywords: Ant colony; Swarm intelligence; Grid; particle Swarm; Load balancing
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Moallem, A. (2009). Using swarm intelligence for distributed job scheduling on the grid. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/etd-04132009-123250
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Moallem, Azin. “Using swarm intelligence for distributed job scheduling on the grid.” 2009. Thesis, University of Saskatchewan. Accessed December 11, 2019.
http://hdl.handle.net/10388/etd-04132009-123250.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Moallem, Azin. “Using swarm intelligence for distributed job scheduling on the grid.” 2009. Web. 11 Dec 2019.
Vancouver:
Moallem A. Using swarm intelligence for distributed job scheduling on the grid. [Internet] [Thesis]. University of Saskatchewan; 2009. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/10388/etd-04132009-123250.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Moallem A. Using swarm intelligence for distributed job scheduling on the grid. [Thesis]. University of Saskatchewan; 2009. Available from: http://hdl.handle.net/10388/etd-04132009-123250
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
5.
Talukder, Satyobroto.
Mathematicle Modelling and Applications of Particle Swarm Optimization.
Degree: 2011, , School of Engineering
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671
► Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or…
(more)
▼ Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or industry which is not involved in solving optimization problems. A variety of optimization techniques compete for the best solution. Particle Swarm Optimization (PSO) is a relatively new, modern, and powerful method of optimization that has been empirically shown to perform well on many of these optimization problems. It is widely used to find the global optimum solution in a complex search space. This thesis aims at providing a review and discussion of the most established results on PSO algorithm as well as exposing the most active research topics that can give initiative for future work and help the practitioner improve better result with little effort. This paper introduces a theoretical idea and detailed explanation of the PSO algorithm, the advantages and disadvantages, the effects and judicious selection of the various parameters. Moreover, this thesis discusses a study of boundary conditions with the invisible wall technique, controlling the convergence behaviors of PSO, discrete-valued problems, multi-objective PSO, and applications of PSO. Finally, this paper presents some kinds of improved versions as well as recent progress in the development of the PSO, and the future research issues are also given.
Subjects/Keywords: Optimization; swarm intelligence; particle swarm; social network; convergence; stagnation; multi-objective.
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Talukder, S. (2011). Mathematicle Modelling and Applications of Particle Swarm Optimization. (Thesis). , School of Engineering. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Talukder, Satyobroto. “Mathematicle Modelling and Applications of Particle Swarm Optimization.” 2011. Thesis, , School of Engineering. Accessed December 11, 2019.
http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Talukder, Satyobroto. “Mathematicle Modelling and Applications of Particle Swarm Optimization.” 2011. Web. 11 Dec 2019.
Vancouver:
Talukder S. Mathematicle Modelling and Applications of Particle Swarm Optimization. [Internet] [Thesis]. , School of Engineering; 2011. [cited 2019 Dec 11].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Talukder S. Mathematicle Modelling and Applications of Particle Swarm Optimization. [Thesis]. , School of Engineering; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU
6.
PRATHYUSHA, YERRA.
UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization.
Degree: Master, Computer Science and Engineering, 2018, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344
► The air pollution has become a major ecological issue. The surpassed pollution levels can be controlled by searching the pollution source. An environmental monitoring UAVs…
(more)
▼ The air pollution has become a major ecological issue. The surpassed pollution levels can be controlled by searching the pollution source. An environmental monitoring UAVs can address this issue. The challenge here is how UAVs collaboratively navigate towards pollution source under realistic pollution distribution. In this thesis, we proposed a novel methodology by using the collaborative
intelligence learned from Golden shiners schooling fish. We adopted shiners collective
intelligence with the particle
swarm optimization (PSO). We used a Gaussian plume model for depicting the pollution distribution. Furthermore, our proposed method incorporates path planning and collision-avoidance for UAV group navigation.
For path planning, we simulated obstacle rich 3D environment. The proposed methodology generates collision-free paths successfully. For group navigation of UAVs, the simulated environment includes a Gaussian plume model which considers several atmospheric constraints like temperature, wind speed, etc. The UAVs can successfully reach the pollution source with accuracy using the proposed methodology. Moreover, we can construct the unknown distribution by plotting the sensed pollution values by UAVs.
Advisors/Committee Members: Chung-Nan Lee (committee member), Chung-Nan Lee (chair), Chia-Ping Chen (chair), Ming-Chao Chiang (chair), Kuo-Sheng Cheng (chair), Tzung-Pei Hong (chair).
Subjects/Keywords: Swarm intelligence; UAV; Particle Swarm Optimization (PSO); Navigation algorithm; Path planning
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MLA ·
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to Zotero / EndNote / Reference
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APA (6th Edition):
PRATHYUSHA, Y. (2018). UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
PRATHYUSHA, YERRA. “UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization.” 2018. Thesis, NSYSU. Accessed December 11, 2019.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
PRATHYUSHA, YERRA. “UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization.” 2018. Web. 11 Dec 2019.
Vancouver:
PRATHYUSHA Y. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 Dec 11].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
PRATHYUSHA Y. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Vanderbilt University
7.
Cody, Jason Robert.
Discrete Consensus Decisions in Human-Collective Teams.
Degree: PhD, Computer Science, 2018, Vanderbilt University
URL: http://etd.library.vanderbilt.edu/available/etd-04032018-164817/
;
► Robotic collectives are large decentralized robot groups of more than fifty individuals that coordinate using interactions inspired by social insect behaviors. Collectives make group decisions…
(more)
▼ Robotic collectives are large decentralized robot groups of more than fifty individuals that coordinate using interactions inspired by social insect behaviors. Collectives make group decisions that are facilitated by information pooling within a shared decision space, similar to an insect colony. Biological collectives must frequently choose the best option from a finite set of options and execute an action based on that choice. Discrete collective consensus achievement algorithms have enabled robotic collectives to make decisions, but most research does not consider scenarios in which the collective must ignore biasing features within the environment. Whether the collective decides between occupation sites, routes, or future actions, the environmental features (e.g., distance between a resource and the collective) alter robot interactions and bias collective decisions towards options that are the easiest to find, evaluate, and reach, but may not be the optimal choice. Robotic collectives that must ignore biasing environmental features during decision making are likely to be inaccurate and inefficient. Robotic collectives do not have centralized control; thus, they are challenged to synchronize the initiation and execution of the chosen actions, which is critical to future collectives that must respond to the environment and complete complex tasks. Discrete collective consensus achievement strategies have only recently been considered in the field of Human-
Swarm Interaction, which has largely focused on enabling humans to control artificial swarms. Typically, swarms are comprised of agents that interact according to a protocol that causes a desired emergent behavior, such as flocking. Most Human-
Swarm Interaction research assumes the human has near-perfect knowledge of the
swarm and global communication with the
swarm's agents. Robotic collectives have the potential to share decision making functions with humans; however, methods of human interaction with collective discrete consensus strategies have not been designed or evaluated.
This dissertation develops a new algorithm influenced by biologically inspired discrete consensus achievement strategies in order to enable robotic collectives to choose and implement the best actions, despite the presence of environmental bias. The new model enables future human-collective teams to make decisions when the human does not have perfect global knowledge. Further, human-collective interaction mechanisms are developed in order to facilitate collaborative decisions between a human and a simulated robotic collective. The robotic collective model is implemented and evaluated for its ability to act independently and as a part of a human-collective team in trials featuring the human supervision of multiple targeting collectives.
Advisors/Committee Members: Julie A. Adams (chair), Yevgeniy Vorobeychik (committee member), Maithilee Kunda (committee member), Jennifer S. Trueblood (committee member), Alexander S. Mentis (committee member).
Subjects/Keywords: multi-agent systems; swarm intelligence; collective decision making; human-swarm interaction
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Cody, J. R. (2018). Discrete Consensus Decisions in Human-Collective Teams. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-04032018-164817/ ;
Chicago Manual of Style (16th Edition):
Cody, Jason Robert. “Discrete Consensus Decisions in Human-Collective Teams.” 2018. Doctoral Dissertation, Vanderbilt University. Accessed December 11, 2019.
http://etd.library.vanderbilt.edu/available/etd-04032018-164817/ ;.
MLA Handbook (7th Edition):
Cody, Jason Robert. “Discrete Consensus Decisions in Human-Collective Teams.” 2018. Web. 11 Dec 2019.
Vancouver:
Cody JR. Discrete Consensus Decisions in Human-Collective Teams. [Internet] [Doctoral dissertation]. Vanderbilt University; 2018. [cited 2019 Dec 11].
Available from: http://etd.library.vanderbilt.edu/available/etd-04032018-164817/ ;.
Council of Science Editors:
Cody JR. Discrete Consensus Decisions in Human-Collective Teams. [Doctoral Dissertation]. Vanderbilt University; 2018. Available from: http://etd.library.vanderbilt.edu/available/etd-04032018-164817/ ;

The Ohio State University
8.
Gazi, Veysel.
Stability analysis of swarms.
Degree: PhD, Graduate School, 2002, The Ohio State University
URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482
Subjects/Keywords: Engineering; Swarm intelligence
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Gazi, V. (2002). Stability analysis of swarms. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482
Chicago Manual of Style (16th Edition):
Gazi, Veysel. “Stability analysis of swarms.” 2002. Doctoral Dissertation, The Ohio State University. Accessed December 11, 2019.
http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482.
MLA Handbook (7th Edition):
Gazi, Veysel. “Stability analysis of swarms.” 2002. Web. 11 Dec 2019.
Vancouver:
Gazi V. Stability analysis of swarms. [Internet] [Doctoral dissertation]. The Ohio State University; 2002. [cited 2019 Dec 11].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482.
Council of Science Editors:
Gazi V. Stability analysis of swarms. [Doctoral Dissertation]. The Ohio State University; 2002. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482

Univerzitet u Beogradu
9.
Bačanin-Džakula, Nebojša V. , 1983-.
Unapređenje hibridizacijom metaheuristika inteligencije
rojeva za rešavanje problema globalne optimizacije.
Degree: Matematički fakultet, 2016, Univerzitet u Beogradu
URL: https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get
► Računarstvo - Veštačka inteligencija / Computer Science - Artificial Intelligence
Te²ki optimizacioni problemi, nere²ivi u prihvatljivom vremenu izvr²avanja deterministi £kim matemati£kim metodama, uspe²no se poslednjih…
(more)
▼ Računarstvo - Veštačka inteligencija / Computer
Science - Artificial Intelligence
Te²ki optimizacioni problemi, nere²ivi u
prihvatljivom vremenu izvr²avanja deterministi £kim matemati£kim
metodama, uspe²no se poslednjih godina re²avaju populacionim
stohasti£kim metaheuristikama, meu kojima istaknutu klasu
predstavljaju algoritmi inteligencije rojeva. U ovom radu razmatra
se unapreenje metaheuristika inteligencije rojeva pomo¢u
hibridizacije. Analizom postoje¢ih metaheuristika u odreenim
slu£ajevima uo£eni su nedostaci i slabosti u mehanizmima pretrage
prostora re²enja koji pre svega proisti£u iz samog matemati£kog
modela kojim se simulira proces iz prirode kao i iz nedovoljno
usklaenog balansa izmeu intenzikacije i diversikacije. U radu je
ispitivano da li se postoje¢i algoritmi inteligencije rojeva za
globalnu optimizaciju mogu unaprediti (u smislu dobijanja boljih
rezultata, brºe konvergencije, ve¢e robustnosti) hibridizacijom sa
drugim algoritmima. Razvijeno je i implementirano vi²e
hibridizovanih metaheuristika inteligencije rojeva. S obzirom da
dobri hibridi ne nastaju slu£ajnom kombinacijom pojedinih
funkcionalnih elemenata i procedura razli£itih algoritama, ve¢ su
oni utemeljeni na sveobuhvatnom izu£avanju na£ina na koji algoritmi
koji se hibridizuju funkcioni²u, kreiranju hibridnih pristupa
prethodila je detaljna analiza prednosti i nedostataka posmatranih
algoritma kako bi se napravila najbolja kombinacija koja nedostatke
jednih neutrali²e prednostima drugih pristupa. Razvijeni hibridni
algoritmi verikovani su testiranjima na standardnim skupovima test
funkcija za globalnu optimizaciju sa ograni£enjima i bez
ograni£enja, kao i na poznatim prakti£nim problemima. Uporeivanjem
sa najboljim poznatim algoritmima iz literature pokazan je kvalitet
razvijenih hibrida, £ime je potvrena i osnovna hipoteza ovog rada
da se algoritmi inteligencije rojeva mogu uspe²no unaprediti
hibridizacijom.
Advisors/Committee Members: Tuba, Milan, 1952-.
Subjects/Keywords: swarm intelligence metaheuristics; hybrid algorithms;
global optimization
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Bačanin-Džakula, Nebojša V. , 1. (2016). Unapređenje hibridizacijom metaheuristika inteligencije
rojeva za rešavanje problema globalne optimizacije. (Thesis). Univerzitet u Beogradu. Retrieved from https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Bačanin-Džakula, Nebojša V. , 1983-. “Unapređenje hibridizacijom metaheuristika inteligencije
rojeva za rešavanje problema globalne optimizacije.” 2016. Thesis, Univerzitet u Beogradu. Accessed December 11, 2019.
https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Bačanin-Džakula, Nebojša V. , 1983-. “Unapređenje hibridizacijom metaheuristika inteligencije
rojeva za rešavanje problema globalne optimizacije.” 2016. Web. 11 Dec 2019.
Vancouver:
Bačanin-Džakula, Nebojša V. 1. Unapređenje hibridizacijom metaheuristika inteligencije
rojeva za rešavanje problema globalne optimizacije. [Internet] [Thesis]. Univerzitet u Beogradu; 2016. [cited 2019 Dec 11].
Available from: https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Bačanin-Džakula, Nebojša V. 1. Unapređenje hibridizacijom metaheuristika inteligencije
rojeva za rešavanje problema globalne optimizacije. [Thesis]. Univerzitet u Beogradu; 2016. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University
10.
Potanapalli, Kranti Kumar.
Coactive learning for multi-robot search and coverage.
Degree: MS, Computer Science, 2013, Oregon State University
URL: http://hdl.handle.net/1957/45107
► We investigate a search and coverage planning problem, where an area of interest has to be explored by a number of vehicles, given a fixed…
(more)
▼ We investigate a search and coverage planning problem, where an area of interest has to be explored by a number of vehicles, given a fixed time budget. A good coverage plan has a low probability of a target remaining unobserved. We introduce a formal problem statement, suggest a greedy algorithm to solve the problem, and show experimental results on a number of simulated coverage problems. Our work offers three main contributions. First, we propose an offline planning algorithm that, given some prior knowledge about the target probability in an environment, surveys the area to find the targets as fast as possible while minimizing the energy used. The planning algorithm plans targets to visit and paths to follow for multiple robots, which may have different performance characteristics such as speed, power, and sensor quality. Our second main contribution is to integrate our planning algorithm in the framework of coactive learning, where the system learns the cost function of an in situ human expert, who edits and improves the solutions generated by the system. Our third contribution is an empirical evaluation of the system and a comparison to a state-of-the-art system with provable performance gaurantees on a simulator. The results show that our system yields comparable performance to the state-of-the-art system while respecting hard budget constraints and running orders of magnitude faster.
Advisors/Committee Members: Tadepalli, Prasad (advisor), Todorovic, Sinisa (committee member).
Subjects/Keywords: Coactive Learning; Swarm intelligence – Mathematical models
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Potanapalli, K. K. (2013). Coactive learning for multi-robot search and coverage. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/45107
Chicago Manual of Style (16th Edition):
Potanapalli, Kranti Kumar. “Coactive learning for multi-robot search and coverage.” 2013. Masters Thesis, Oregon State University. Accessed December 11, 2019.
http://hdl.handle.net/1957/45107.
MLA Handbook (7th Edition):
Potanapalli, Kranti Kumar. “Coactive learning for multi-robot search and coverage.” 2013. Web. 11 Dec 2019.
Vancouver:
Potanapalli KK. Coactive learning for multi-robot search and coverage. [Internet] [Masters thesis]. Oregon State University; 2013. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/1957/45107.
Council of Science Editors:
Potanapalli KK. Coactive learning for multi-robot search and coverage. [Masters Thesis]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/45107
11.
Sarker, Md Omar Faruque.
Self-regulated multi-robot task allocation.
Degree: PhD, 2010, University of South Wales
URL: https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749689
► To deploy a large group of autonomous robots in dynamic multi-tasking environments, a suitable multi-robot task-allocation (MRTA) solution is required. This must be scalable to…
(more)
▼ To deploy a large group of autonomous robots in dynamic multi-tasking environments, a suitable multi-robot task-allocation (MRTA) solution is required. This must be scalable to variable number of robots and tasks. Recent studies show that biology-inspired self-organized approaches can effectively handle task-allocation in large multi-robot systems. However most existing MRTA approaches have overlooked the role of different communication and sensing strategies found in selfregulated biological societies. This dissertation proposes to solve the MRTA problem using a set of previously published generic rules for division of labour derived from the observation of ant,human and robotic social systems. The concrete form of these rules, the attractive field model (AFM), provides sufficient abstraction to local communication and sensing which is uncommon in existing MRTA solutions. This dissertation validates the effectiveness of AFM to address MRTA using two bio-inspired communication and sensing strategies: "global sensing - no communication" and "local sensing - local communication". The former is realized using a centralized communication system and the latter is emulated under a peer-topeer local communication scheme. They are applied in a manufacturing shop-floor scenario using 16 e-puck robots. A robotic interpretation of AFM is presented that maps the generic parameters of AFM to the properties of a manufacturing shopfloor. A flexible multi-robot control architecture, hybrid event-driven architecture on D-Bus, has been outlined which uses the state-of-the-art D-Bus interprocess communication to integrate heterogeneous software components. Based-on the organization of task-allocation, communication and interaction among robots, a novel taxonomy of MRTA solutions has been proposed to remove the ambiguities found in existing MRTA solutions. Besides, a set of domainindependent metrics, e.g., plasticity, task-specialization and energy usage, has been formalized to compare the performances of the above two strategies. The presented comparisons extend our general understanding of the role of information exchange strategies to achieve the distributed task-allocations among various social groups.
Subjects/Keywords: Swarm intelligence; Robotics; Attractive field model (AFM)
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sarker, M. O. F. (2010). Self-regulated multi-robot task allocation. (Doctoral Dissertation). University of South Wales. Retrieved from https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749689
Chicago Manual of Style (16th Edition):
Sarker, Md Omar Faruque. “Self-regulated multi-robot task allocation.” 2010. Doctoral Dissertation, University of South Wales. Accessed December 11, 2019.
https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749689.
MLA Handbook (7th Edition):
Sarker, Md Omar Faruque. “Self-regulated multi-robot task allocation.” 2010. Web. 11 Dec 2019.
Vancouver:
Sarker MOF. Self-regulated multi-robot task allocation. [Internet] [Doctoral dissertation]. University of South Wales; 2010. [cited 2019 Dec 11].
Available from: https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749689.
Council of Science Editors:
Sarker MOF. Self-regulated multi-robot task allocation. [Doctoral Dissertation]. University of South Wales; 2010. Available from: https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749689

Brno University of Technology
12.
Winklerová, Zdenka.
Inteligence skupiny
.
Degree: 2015, Brno University of Technology
URL: http://hdl.handle.net/11012/63276
► Záměrem disertační práce je aplikovaný výzkum skupinové ( kolektivní ) inteligence . K prokázání použitelnosti inteligence skupiny je zkoumán algoritmus na bázi roje částic (…
(more)
▼ Záměrem disertační práce je aplikovaný výzkum skupinové ( kolektivní ) inteligence . K prokázání použitelnosti inteligence skupiny je zkoumán algoritmus na bázi roje částic ( Particle
Swarm Optimization PSO ), v němž je problém inteligence skupiny převeden na matematickou optimalizaci, kdy roj částic ( particle
swarm ) hledá globální optimum ve vymezeném prostoru problému a prohledávání je řízeno podle předem nadefinované účelové funkce ( objective function ), která zastupuje řešený problém. Byla navržena a experimentálně ověřena strategie prohledávání, v níž částice průběžně přizpůsobují své chování charakteristikám prostoru řešeného problému, a bylo experimentálně zjištěno, jak se vliv řídící účelové funkce zastupující řešený problém projevuje v chování částic. Výsledky experimentování s navrženou strategií prohledávání byly porovnány s výsledky experimentů s referenční verzí algoritmu PSO . Experimenty ukázaly, že klasické prohledávání, kde jedinou podmínkou je stabilní trajektorie, po níž se částice pohybuje v prostoru řešeného problému, a kde je ve výsledku eliminován vliv řídící účelové funkce, může selhat a že dynamická stabilita trajektorií částic sama o sobě není ukazatelem prohledávacích schopností algoritmu ani konvergence algoritmu ke správnému, globálnímu řešení. Byl navržen způsob prohledávání prostoru řešeného problému, v němž algoritmus PSO reguluje stabilitu algoritmu průběžným přizpůsobováním chování částic charakteristikám prostoru problému. Navržený algoritmus usměrňoval vývoj prohledávání prostoru problému tak, že vzrostla pravděpodobnost úspěšnosti řešení.; The intention of the dissertation is the applied research of the collective ( group ) (
swarm )
intelligence . To demonstrate the applicability of the collective
intelligence, the Particle
Swarm Optimization ( PSO ) algorithm has been studied in which the problem of the collective
intelligence is transferred to mathematical optimization in which the particle
swarm searches for a global optimum within the defined problem space, and the searching is controlled according to the pre-defined objective function which represents the solved problem. A new search strategy has been designed and experimentally tested in which the particles continuously adjust their behaviour according to the characteristics of the problem space, and it has been experimentally discovered how the impact of the objective function representing a solved problem manifests itself in the behaviour of the particles. The results of the experiments with the proposed search strategy have been compared to the results of the experiments with the reference version of the PSO algorithm. Experiments have shown that the classical reference solution, where the only condition is a stable trajectory along which the particle moves in the problem space, and where the influence of a control objective function is ultimately eliminated, may fail, and that the dynamic stability of the trajectory of the particle itself is not an indicator of the searching ability nor the convergence of the…
Advisors/Committee Members: Zbořil, František (advisor).
Subjects/Keywords: Kolektivní inteligence;
inteligence skupiny;
rojová inteligence;
optimalizace rojem částic;
Collective intelligence;
group intelligence;
swarm intelligence;
particle swarm optimization.
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Winklerová, Z. (2015). Inteligence skupiny
. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/63276
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Winklerová, Zdenka. “Inteligence skupiny
.” 2015. Thesis, Brno University of Technology. Accessed December 11, 2019.
http://hdl.handle.net/11012/63276.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Winklerová, Zdenka. “Inteligence skupiny
.” 2015. Web. 11 Dec 2019.
Vancouver:
Winklerová Z. Inteligence skupiny
. [Internet] [Thesis]. Brno University of Technology; 2015. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/11012/63276.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Winklerová Z. Inteligence skupiny
. [Thesis]. Brno University of Technology; 2015. Available from: http://hdl.handle.net/11012/63276
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
13.
Kelman, Alexander.
Utilizing Swarm Intelligence Algorithms for Pathfinding in Games.
Degree: Informatics, 2017, University of Skövde
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636
► The Ant Colony Optimization and Particle Swarm Optimization are two Swarm Intelligence algorithms often utilized for optimization. Swarm Intelligence relies on agents that possess…
(more)
▼ The Ant Colony Optimization and Particle Swarm Optimization are two Swarm Intelligence algorithms often utilized for optimization. Swarm Intelligence relies on agents that possess fragmented knowledge, a concept not often utilized in games. The aim of this study is to research whether there are any benefits to using these Swarm Intelligence algorithms in comparison to standard algorithms such as A* for pathfinding in a game. Games often consist of dynamic environments with mobile agents, as such all experiments were conducted with dynamic destinations. Algorithms were measured on the length of their path and the time taken to calculate that path. The algorithms were implemented with minor modifications to allow them to better function in a grid based environment. The Ant Colony Optimization was modified in regards to how pheromone was distributed in the dynamic environment to better allow the algorithm to path towards a mobile target. Whereas the Particle Swarm Optimization was given set start positions and velocity in order to increase initial search space and modifications to increase particle diversity. The results obtained from the experimentation showcased that the Swarm Intelligence algorithms were capable of performing to great results in terms of calculation speed, they were however not able to obtain the same path optimality as A*. The algorithms' implementation can be improved but show potential to be useful in games.
Subjects/Keywords: Swarm Intelligence; Pathfinding; Ant Colony Optimization; Particle Swarm Optimization; A*; Computer Sciences; Datavetenskap (datalogi)
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kelman, A. (2017). Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. (Thesis). University of Skövde. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Kelman, Alexander. “Utilizing Swarm Intelligence Algorithms for Pathfinding in Games.” 2017. Thesis, University of Skövde. Accessed December 11, 2019.
http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Kelman, Alexander. “Utilizing Swarm Intelligence Algorithms for Pathfinding in Games.” 2017. Web. 11 Dec 2019.
Vancouver:
Kelman A. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. [Internet] [Thesis]. University of Skövde; 2017. [cited 2019 Dec 11].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Kelman A. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. [Thesis]. University of Skövde; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
14.
Magg, Sven.
Self-Organised Task Differentiation in Homogeneous and Heterogeneous Groups of Autonomous Agents
.
Degree: 2012, University of Hertfordshire
URL: http://hdl.handle.net/2299/9038
► The field of swarm robotics has been growing fast over the last few years. Using a swarm of simple and cheap robots has advantages in…
(more)
▼ The field of swarm robotics has been growing fast over the last few years. Using a swarm
of simple and cheap robots has advantages in various tasks. Apart from performance gains
on tasks that allow for parallel execution, simple robots can also be smaller, enabling them
to reach areas that can not be accessed by a larger, more complex robot. Their ability to
cooperate means they can execute complex tasks while offering self-organised adaptation to
changing environments and robustness due to redundancy.
In order to keep individual robots simple, a control algorithm has to keep expensive
communication to a minimum and has to be able to act on little information to keep the
amount of sensors down. The number of sensors and actuators can be reduced even more
when necessary capabilities are spread out over different agents that then combine them by
cooperating. Self-organised differentiation within these heterogeneous groups has to take
the individual abilities of agents into account to improve group performance.
In this thesis it is shown that a homogeneous group of versatile agents can not be easily
replaced by a heterogeneous group, by separating the abilities of the versatile agents into
several specialists. It is shown that no composition of those specialists produces the same
outcome as a homogeneous group on a clustering task. In the second part of this work,
an adaptation mechanism for a group of foragers introduced by Labella et al. (2004) is
analysed in more detail. It does not require communication and needs only the information
on individual success or failure. The algorithm leads to self-organised regulation of group
activity depending on object availability in the environment by adjusting resting times in a
base. A possible variation of this algorithm is introduced which replaces the probabilistic
mechanism with which agents determine to leave the base. It is demonstrated that a direct
calculation of the resting times does not lead to differences in terms of differentiation and
speed of adaptation.
After investigating effects of different parameters on the system, it is shown that there
is no efficiency increase in static environments with constant object density when using a
homogeneous group of agents. Efficiency gains can nevertheless be achieved in dynamic
environments. The algorithm was also reported to lead to higher activity of agents which
have higher performance. It is shown that this leads to efficiency gains in heterogeneous
groups in static and dynamic environments.
Subjects/Keywords: self-organisation;
differentiation;
talk allocation;
adaptive behaviour;
swarm intelligence;
multi-agent-systems;
swarm robotics
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Magg, S. (2012). Self-Organised Task Differentiation in Homogeneous and Heterogeneous Groups of Autonomous Agents
. (Thesis). University of Hertfordshire. Retrieved from http://hdl.handle.net/2299/9038
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Magg, Sven. “Self-Organised Task Differentiation in Homogeneous and Heterogeneous Groups of Autonomous Agents
.” 2012. Thesis, University of Hertfordshire. Accessed December 11, 2019.
http://hdl.handle.net/2299/9038.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Magg, Sven. “Self-Organised Task Differentiation in Homogeneous and Heterogeneous Groups of Autonomous Agents
.” 2012. Web. 11 Dec 2019.
Vancouver:
Magg S. Self-Organised Task Differentiation in Homogeneous and Heterogeneous Groups of Autonomous Agents
. [Internet] [Thesis]. University of Hertfordshire; 2012. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/2299/9038.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Magg S. Self-Organised Task Differentiation in Homogeneous and Heterogeneous Groups of Autonomous Agents
. [Thesis]. University of Hertfordshire; 2012. Available from: http://hdl.handle.net/2299/9038
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Georgia
15.
Oruganti Venkata, Sanjay Sarma.
A swarm engineering framework for microtubule self-organization.
Degree: MS, Engineering, 2017, University of Georgia
URL: http://hdl.handle.net/10724/37465
► Microtubules are highly dynamic polymers distributed in the cytoplasm of a biological cell. Alpha and beta tubulins combine to form these tubules through polymerization, controlled…
(more)
▼ Microtubules are highly dynamic polymers distributed in the cytoplasm of a biological cell. Alpha and beta tubulins combine to form these tubules through polymerization, controlled by the concentrations of GTPs and MAPs. These play a crucial role in many intra cellular processes, predominantly in mitosis, organelle transport and cell locomotion. Current research in this area is primarily focused on understanding these exclusive behaviors of organization of tubules and their association with different MAPs through organized laboratory experiments. However, the intriguing
intelligence behind these tiny machines resulting in complex self-organizing structures is largely unexplored. Understanding this can support researchers in validating many hypotheses in quicker and cost-effective ways. On these lines, we propose a novel
swarm engineering framework in modeling rules for these systems, by convolving the principles of design with
swarm intelligence. The proposed rules were simulated on a game engine and this approach demonstrated self-organization of rings and protofilaments.
Advisors/Committee Members: Ramana Pidaparti.
Subjects/Keywords: Microtubules; Microtubule Associated Proteins; Self-Organization; Swarm Engineering; Swarm Intelligence; Game Engine; Protofilaments
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Oruganti Venkata, S. S. (2017). A swarm engineering framework for microtubule self-organization. (Masters Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37465
Chicago Manual of Style (16th Edition):
Oruganti Venkata, Sanjay Sarma. “A swarm engineering framework for microtubule self-organization.” 2017. Masters Thesis, University of Georgia. Accessed December 11, 2019.
http://hdl.handle.net/10724/37465.
MLA Handbook (7th Edition):
Oruganti Venkata, Sanjay Sarma. “A swarm engineering framework for microtubule self-organization.” 2017. Web. 11 Dec 2019.
Vancouver:
Oruganti Venkata SS. A swarm engineering framework for microtubule self-organization. [Internet] [Masters thesis]. University of Georgia; 2017. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/10724/37465.
Council of Science Editors:
Oruganti Venkata SS. A swarm engineering framework for microtubule self-organization. [Masters Thesis]. University of Georgia; 2017. Available from: http://hdl.handle.net/10724/37465

De Montfort University
16.
Al-Obaidi, Mohanad.
ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence.
Degree: PhD, 2010, De Montfort University
URL: http://hdl.handle.net/2086/5187
► Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm…
(more)
▼ Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military. These application domains have a number of characteristics that challenge the algorithmic design of WSNs. Firstly, mobility has a negative effect on the quality of the wireless communication and the performance of networking protocols. Nevertheless, it has been shown that mobility can enhance the functionality of the network by exploiting the movement patterns of mobile objects. Secondly, the heterogeneity of devices in a WSN has to be taken into account for increasing the network performance and lifetime. Thirdly, the WSN services should ideally assist the user in an unobtrusive and transparent way. Fourthly, energy-efficiency and scalability are of primary importance to prevent the network performance degradation. This thesis contributes toward the design of a new hybrid optimization algorithm; ENAMS (Energy optimizatioN Algorithm for Mobile Sensor networks) which is based on the Evolutionary Computation and Swarm Intelligence to increase the life time of mobile wireless sensor networks. The presented algorithm is suitable for large scale mobile sensor networks and provides a robust and energy- efficient communication mechanism by dividing the sensor-nodes into clusters, where the number of clusters is not predefined and the sensors within each cluster are not necessary to be distributed in the same density. The presented algorithm enables the sensor nodes to move as swarms within the search space while keeping optimum distances between the sensors. To verify the objectives of the proposed algorithm, the LEGO-NXT MIND-STORMS robots are used to act as particles in a moving swarm keeping the optimum distances while tracking each other within the permitted distance range in the search space.
Subjects/Keywords: 600; genetic algorithms; clustering; evolutionary computation; sensor networks; swarm intelligence; energy optimization; particle swarm optimization
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Al-Obaidi, M. (2010). ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence. (Doctoral Dissertation). De Montfort University. Retrieved from http://hdl.handle.net/2086/5187
Chicago Manual of Style (16th Edition):
Al-Obaidi, Mohanad. “ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence.” 2010. Doctoral Dissertation, De Montfort University. Accessed December 11, 2019.
http://hdl.handle.net/2086/5187.
MLA Handbook (7th Edition):
Al-Obaidi, Mohanad. “ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence.” 2010. Web. 11 Dec 2019.
Vancouver:
Al-Obaidi M. ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence. [Internet] [Doctoral dissertation]. De Montfort University; 2010. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/2086/5187.
Council of Science Editors:
Al-Obaidi M. ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence. [Doctoral Dissertation]. De Montfort University; 2010. Available from: http://hdl.handle.net/2086/5187

University of New Mexico
17.
Lu, Qi.
An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms.
Degree: Department of Computer Science, 2019, University of New Mexico
URL: https://digitalrepository.unm.edu/cs_etds/100
► Searching and collecting multiple resources from large unmapped environments is an important challenge. It is particularly difficult given limited time, a large search area…
(more)
▼ Searching and collecting multiple resources from large unmapped environments is an important challenge. It is particularly difficult given limited time, a large search area and incomplete data about the environment. This search task is an abstraction of many real-world applications such as search and rescue, hazardous material clean-up, and space exploration. The collective foraging behavior of robot swarms is an effective approach for this task. In our work, individual robots have limited sensing and communication range (like ants), but they are organized and work together to complete foraging tasks collectively. An efficient foraging algorithm coordinates robots to search and collect as many resources as possible in the least amount of time. In the foraging algorithms we study, robots act independently with little or no central control.
As the
swarm size and arena size increase (e.g., thousands of robots searching over the surface of Mars or ocean), the foraging performance per robot decreases. Generally, larger robot swarms produce more inter-robot collisions, and in
swarm robot foraging, larger search arenas result in larger travel distances causing the phenomenon of diminishing returns. The foraging performance per robot (measured as a number of collected resources per unit time) is sublinear with the arena size and the
swarm size.
Our goal is to design a scale-invariant foraging robot
swarm. In other words, the foraging performance per robot should be nearly constant as the arena size and the
swarm size increase. We address these problems with the Multiple-Place Foraging Algorithm (MPFA), which uses multiple collection zones distributed throughout the search area. Robots start from randomly assigned home collection zones but always return to the closest collection zones with found resources. We simulate the foraging behavior of robot swarms in the robot simulator ARGoS and employ a Genetic Algorithm (GA) to discover different optimized foraging strategies as
swarm sizes and the number of resources is scaled up. In our experiments, the MPFA always produces higher foraging rates, fewer collisions, and lower travel and search time than the Central-Place Foraging Algorithm (CPFA). To make the MPFA more adaptable, we introduce dynamic depots that move to the centroid of recently collected resources, minimizing transport times when resources are clustered in heterogeneous distributions.
Finally, we extend the MPFA with a bio-inspired hierarchical branching transportation network. We demonstrate a scale-invariant
swarm foraging algorithm that ensures that each robot finds and delivers resources to a central collection zone at the same rate, regardless of the size of the
swarm or the search area. Dispersed mobile depots aggregate locally foraged resources and transport them to a central place via a hierarchical branching transportation network. This approach is inspired by ubiquitous fractal branching networks such as animal cardiovascular networks that deliver resources to cells and determine…
Advisors/Committee Members: Melanie E. Moses, Carlo Pinciroli, Stephanie Forrest, Joshua P. Hecker.
Subjects/Keywords: Swarm Robotics; Swarm Intelligence; Bio-Inspired Robot Swarm; Autonomous Robot; Foraging Robots; Multi-agent Systems; Robotics
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lu, Q. (2019). An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms. (Doctoral Dissertation). University of New Mexico. Retrieved from https://digitalrepository.unm.edu/cs_etds/100
Chicago Manual of Style (16th Edition):
Lu, Qi. “An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms.” 2019. Doctoral Dissertation, University of New Mexico. Accessed December 11, 2019.
https://digitalrepository.unm.edu/cs_etds/100.
MLA Handbook (7th Edition):
Lu, Qi. “An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms.” 2019. Web. 11 Dec 2019.
Vancouver:
Lu Q. An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms. [Internet] [Doctoral dissertation]. University of New Mexico; 2019. [cited 2019 Dec 11].
Available from: https://digitalrepository.unm.edu/cs_etds/100.
Council of Science Editors:
Lu Q. An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms. [Doctoral Dissertation]. University of New Mexico; 2019. Available from: https://digitalrepository.unm.edu/cs_etds/100

University of Pretoria
18.
[No author].
Particle swarm optimisation in dynamically changing
environments - an empirical study
.
Degree: 2012, University of Pretoria
URL: http://upetd.up.ac.za/thesis/available/etd-06262012-124432/
► Real-world optimisation problems often are of a dynamic nature. Recently, much research has been done to apply particle swarm optimisation (PSO) to dynamic environments (DE).…
(more)
▼ Real-world optimisation problems often are of a
dynamic nature. Recently, much research has been done to apply
particle
swarm optimisation (PSO) to dynamic environments (DE).
However, these research efforts generally focused on optimising one
variation of the PSO algorithm for one type of DE. The aim of this
work is to develop a more comprehensive view of PSO for DEs. This
thesis studies different schemes of characterising and taxonomising
DEs, performance measures used to quantify the performance of
optimisation algorithms applied to DEs, various adaptations of PSO
to apply PSO to DEs, and the effectiveness of these approaches on
different DE types. The standard PSO algorithm has shown
limitations when applied to DEs. To overcome these limitations, the
standard PSO can be modi ed using personal best reevaluation,
change detection and response, diversity maintenance, or
swarm
sub-division and parallel tracking of optima. To investigate the
strengths and weaknesses of these approaches, a representative
sample of algorithms, namely, the standard PSO, re-evaluating PSO,
reinitialising PSO, atomic PSO (APSO), quantum
swarm optimisation
(QSO), multi-
swarm, and self-adapting multi-
swarm (SAMS), are
empirically analysed. These algorithms are analysed on a range of
DE test cases, and their ability to detect and track optima are
evaluated using performance measures designed for DEs. The
experiments show that QSO, multi-
swarm and reinitialising PSO
provide the best results. However, the most effective approach to
use depends on the dimensionality, modality and type of the DEs, as
well as on the objective of the algorithm. A number of observations
are also made regarding the behaviour of the swarms, and the
influence of certain control parameters of the algorithms
evaluated. Copyright
Advisors/Committee Members: Engelbrecht, Andries P (advisor).
Subjects/Keywords: Atomic pso;
Charged pso;
Self-adapting multi-swarm;
Re-evaluating pso;
Particle swarm optimisation (pso);
Dynamically changing environment;
Quantum swarm optimisation;
Reinitialising pso;
Computational intelligence;
Multi-swarm;
UCTD
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
author], [. (2012). Particle swarm optimisation in dynamically changing
environments - an empirical study
. (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-06262012-124432/
Chicago Manual of Style (16th Edition):
author], [No. “Particle swarm optimisation in dynamically changing
environments - an empirical study
.” 2012. Masters Thesis, University of Pretoria. Accessed December 11, 2019.
http://upetd.up.ac.za/thesis/available/etd-06262012-124432/.
MLA Handbook (7th Edition):
author], [No. “Particle swarm optimisation in dynamically changing
environments - an empirical study
.” 2012. Web. 11 Dec 2019.
Vancouver:
author] [. Particle swarm optimisation in dynamically changing
environments - an empirical study
. [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2019 Dec 11].
Available from: http://upetd.up.ac.za/thesis/available/etd-06262012-124432/.
Council of Science Editors:
author] [. Particle swarm optimisation in dynamically changing
environments - an empirical study
. [Masters Thesis]. University of Pretoria; 2012. Available from: http://upetd.up.ac.za/thesis/available/etd-06262012-124432/

University of Pretoria
19.
Duhain, Julien Georges Omer
Louis.
Particle swarm
optimisation in dynamically changing environments - an empirical
study.
Degree: Computer Science, 2012, University of Pretoria
URL: http://hdl.handle.net/2263/25875
► Real-world optimisation problems often are of a dynamic nature. Recently, much research has been done to apply particle swarm optimisation (PSO) to dynamic environments (DE).…
(more)
▼ Real-world optimisation problems often are of a dynamic
nature. Recently, much research has been done to apply particle
swarm optimisation (PSO) to dynamic environments (DE). However,
these research efforts generally focused on optimising one
variation of the PSO algorithm for one type of DE. The aim of this
work is to develop a more comprehensive view of PSO for DEs. This
thesis studies different schemes of characterising and taxonomising
DEs, performance measures used to quantify the performance of
optimisation algorithms applied to DEs, various adaptations of PSO
to apply PSO to DEs, and the effectiveness of these approaches on
different DE types. The standard PSO algorithm has shown
limitations when applied to DEs. To overcome these limitations, the
standard PSO can be modi ed using personal best reevaluation,
change detection and response, diversity maintenance, or
swarm
sub-division and parallel tracking of optima. To investigate the
strengths and weaknesses of these approaches, a representative
sample of algorithms, namely, the standard PSO, re-evaluating PSO,
reinitialising PSO, atomic PSO (APSO), quantum
swarm optimisation
(QSO), multi-
swarm, and self-adapting multi-
swarm (SAMS), are
empirically analysed. These algorithms are analysed on a range of
DE test cases, and their ability to detect and track optima are
evaluated using performance measures designed for DEs. The
experiments show that QSO, multi-
swarm and reinitialising PSO
provide the best results. However, the most effective approach to
use depends on the dimensionality, modality and type of the DEs, as
well as on the objective of the algorithm. A number of observations
are also made regarding the behaviour of the swarms, and the
influence of certain control parameters of the algorithms
evaluated. Copyright
Advisors/Committee Members: Engelbrecht, Andries P. (advisor).
Subjects/Keywords: Atomic
PSO; Charged
PSO; Self-adapting
multi-swarm; Re-evaluating
PSO; Particle swarm
optimisation (PSO); Dynamically
changing environment; Quantum swarm
optimisation; Reinitialising
PSO; Computational
intelligence;
Multi-swarm;
UCTD
Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Duhain, J. G. O. (2012). Particle swarm
optimisation in dynamically changing environments - an empirical
study. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/25875
Chicago Manual of Style (16th Edition):
Duhain, Julien Georges Omer. “Particle swarm
optimisation in dynamically changing environments - an empirical
study.” 2012. Masters Thesis, University of Pretoria. Accessed December 11, 2019.
http://hdl.handle.net/2263/25875.
MLA Handbook (7th Edition):
Duhain, Julien Georges Omer. “Particle swarm
optimisation in dynamically changing environments - an empirical
study.” 2012. Web. 11 Dec 2019.
Vancouver:
Duhain JGO. Particle swarm
optimisation in dynamically changing environments - an empirical
study. [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/2263/25875.
Council of Science Editors:
Duhain JGO. Particle swarm
optimisation in dynamically changing environments - an empirical
study. [Masters Thesis]. University of Pretoria; 2012. Available from: http://hdl.handle.net/2263/25875

University of Saskatchewan
20.
Guha, Tapashree.
Investigation of service selection algorithms for grid services.
Degree: 2009, University of Saskatchewan
URL: http://hdl.handle.net/10388/etd-09112009-201250
► Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also…
(more)
▼ Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle
Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle
Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters.
Advisors/Committee Members: Ludwig, Simone, Wu, Fang Xiang, McQuillan, Ian, Keil, Mark.
Subjects/Keywords: Evolutionary Algorithms; Grid Computing; Service Selection; Swarm Intelligence; Artificial Intelligence; Assignment Problem
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Guha, T. (2009). Investigation of service selection algorithms for grid services. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/etd-09112009-201250
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Guha, Tapashree. “Investigation of service selection algorithms for grid services.” 2009. Thesis, University of Saskatchewan. Accessed December 11, 2019.
http://hdl.handle.net/10388/etd-09112009-201250.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Guha, Tapashree. “Investigation of service selection algorithms for grid services.” 2009. Web. 11 Dec 2019.
Vancouver:
Guha T. Investigation of service selection algorithms for grid services. [Internet] [Thesis]. University of Saskatchewan; 2009. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/10388/etd-09112009-201250.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Guha T. Investigation of service selection algorithms for grid services. [Thesis]. University of Saskatchewan; 2009. Available from: http://hdl.handle.net/10388/etd-09112009-201250
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
21.
Sun, Yanxia.
Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation.
Degree: Docteur es, Information Scientifique et Technique, 2011, Université Paris-Est
URL: http://www.theses.fr/2011PEST1049
► Optimisation Swarm Particle (PSO) est basé sur une métaphore de l'interaction sociale […] en ajustant les trajectoires des vecteurs individuels, appelés «particules» conceptualisées comme des…
(more)
▼ Optimisation Swarm Particle (PSO) est basé sur une métaphore de l'interaction sociale […] en ajustant les trajectoires des vecteurs individuels, appelés «particules» conceptualisées comme des points se déplaçant dans un espace multidimensionnel. Le poids aléatoire des paramètres de contrôle est utilisé pour provoquer les particules à aller stochastiquement vers une région ayant plus de succès dans un espace tridimensionnel. Les particules itératives ajustent leur vitesse et leur direction en fonction de leurs personnels et des meilleures positions dans l'essaim. PSO a été appliquée avec succès pour optimiser une large gamme de problèmes. Cependant, les algorithmes standard PSO sont facilement piégés dans les points locaux suboptimaux lorsqu'il est appliqué à des problèmes avec de nombreux extrema locaux ou avec des contraintes. Cette thèse présente plusieurs algorithmes / techniques pour améliorer la capacité de l'OPS recherche mondiale: 1) Deux nouveaux algorithmes chaotiques de particules essaim d'optimisation, d'avoir une chaotiques Hopfield Neural Network (HNN) la structure, sont proposées. L'utilisation d'un système chaotique pour déterminer les poids des particules aide des algorithmes OSP pour échapper à des extrema locaux et de trouver l'optimum global. 2) Pour les algorithmes existants OSP, la relation et l'influence compter que sur les composants correspondants dimensions de l'essaim de particules. Pour montrer la relation intérieure entre les différentes composantes d'une particule, les réseaux de neurones peuvent être utilisés pour modéliser les projections d'ordre du problème d'optimisation, et une optimisation des intérieurs entièrement connecté essaim de particules est proposé à cet effet. 3) En raison de la complexité des contraintes, une solution déterministe générale est souvent difficile à trouver. Par conséquent, une particule détendue contrainte optimisation par essaim algorithme est proposé. Cette méthode améliore la capacité de recherche de l'OSP. 4) Pour améliorer les performances de l'optimisation par essaim de particules, une méthode adaptative de particules essaim d'optimisation basée sur les tests d'hypothèses sont proposées. Cette méthode applique un test d'hypothèse pour déterminer si le piège des particules dans un minimum local ou non. 5) Afin de renforcer la capacité du MPSO de recherche globale, une approche adaptative multi-objectif l'optimisation par essaim de particules (MOPSO) est proposé. Les résultats de simulation et d'analyse confirment l'efficacité des algorithmes proposés / techniques par rapport à l'autre état d'algorithmes
Particle Swarm Optimisation (PSO) is based on a metaphor of social interaction such as birds flocking or fish schooling to search a space by adjusting the trajectories of individual vectors, called “particles” conceptualized as moving points in a multidimensional space. The random weights of the control parameters are used to cause the particles to stochastically move towards a successful region in a higher dimensional space. Particles iteratively…
Advisors/Committee Members: Siarry, Patrick (thesis director).
Subjects/Keywords: Optimisation par essaim de particules; Le chaos; Intelligence; Particle swarm optimization; Chaos; Intelligence
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sun, Y. (2011). Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation. (Doctoral Dissertation). Université Paris-Est. Retrieved from http://www.theses.fr/2011PEST1049
Chicago Manual of Style (16th Edition):
Sun, Yanxia. “Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation.” 2011. Doctoral Dissertation, Université Paris-Est. Accessed December 11, 2019.
http://www.theses.fr/2011PEST1049.
MLA Handbook (7th Edition):
Sun, Yanxia. “Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation.” 2011. Web. 11 Dec 2019.
Vancouver:
Sun Y. Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation. [Internet] [Doctoral dissertation]. Université Paris-Est; 2011. [cited 2019 Dec 11].
Available from: http://www.theses.fr/2011PEST1049.
Council of Science Editors:
Sun Y. Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation. [Doctoral Dissertation]. Université Paris-Est; 2011. Available from: http://www.theses.fr/2011PEST1049
22.
Steyven, Andreas Siegfried Wilhelm.
A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics.
Degree: PhD, 2017, Edinburgh Napier University
URL: http://researchrepository.napier.ac.uk/Output/1253630
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125
► This thesis investigates several aspects of environment-driven adaptation in simulated evolutionary swarm robotics. It is centred around a specific algorithm for distributed embodied evolution called…
(more)
▼ This thesis investigates several aspects of environment-driven adaptation in simulated evolutionary swarm robotics. It is centred around a specific algorithm for distributed embodied evolution called mEDEA. Firstly, mEDEA is extended with an explicit relative fitness measure while still maintaining the distributed nature of the algorithm. Two ways of using the relative fitness are investigated: influencing the spreading of genomes and performing an explicit genome selection. Both methods lead to an improvement in the swarm's abilityto maintain energy over longer periods. Secondly, a communication energy model is derived and introduced into the simulator to investigate the influence of accounting for the costs of communication in the distributed evolutionary algorithm where communication is a key component. Thirdly, a method is introduced that relates environmental conditions to a measure of the swarm's behaviour in a 3-dimensional map to study the environment's influence on the emergence of behaviours at the individual and swarm level. Interesting regions for further experimentation are identified in which algorithm specific characteristics show effect and can be explored. Finally, a novel individual learning method is developed and used to investigate how the most effective balance between evolutionary and lifetime-adaptation mechanisms is influenced by aspects of the environment a swarm operates in. The results show a clearlink between the effectiveness of different adaptation mechanisms and environmental conditions, specifically the rate of change and the availability of learning opportunities.
Subjects/Keywords: 006.3; Computer science; swarm robotics; artificial intelligence; 006.3 Artificial intelligence; QA75 Electronic computers. Computer science
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Steyven, A. S. W. (2017). A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics. (Doctoral Dissertation). Edinburgh Napier University. Retrieved from http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125
Chicago Manual of Style (16th Edition):
Steyven, Andreas Siegfried Wilhelm. “A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics.” 2017. Doctoral Dissertation, Edinburgh Napier University. Accessed December 11, 2019.
http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125.
MLA Handbook (7th Edition):
Steyven, Andreas Siegfried Wilhelm. “A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics.” 2017. Web. 11 Dec 2019.
Vancouver:
Steyven ASW. A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics. [Internet] [Doctoral dissertation]. Edinburgh Napier University; 2017. [cited 2019 Dec 11].
Available from: http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125.
Council of Science Editors:
Steyven ASW. A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics. [Doctoral Dissertation]. Edinburgh Napier University; 2017. Available from: http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125

Anna University
23.
Nagarani, S.
Swarm intelligence based dynamic subcarrier bit and power
allocation for ofdma relay networks; -.
Degree: mathematics, 2013, Anna University
URL: http://shodhganga.inflibnet.ac.in/handle/10603/27015
► Orthogonal Frequency Division Multiplexing OFDM is a newlinepromising and a reliable solution for future wireless communication networks newlineOFDM provides a high performance physical layer and…
(more)
▼ Orthogonal Frequency Division Multiplexing OFDM is
a newlinepromising and a reliable solution for future wireless
communication networks newlineOFDM provides a high performance
physical layer and medium access newlinecontrol due to its
capability to combat Inter Symbol Interference ISI and
newlinemultipath fading Moreover multiuser diversity can be
exploited by OFDM newlinewhich is inherent in the multiuser
wireless network via dynamic subcarrier newlineallocation
newlineThe toughest concern in OFDM system is the problem of
subcarrier newlineand power allocation in a multiuser network to
reduce the transmitted power newlineand maximize the total data
rate or a utility function of data rate of users newlineResource
allocation in OFDM has become one of the active areas newlineof
research which refers to assigning subcarrier to consumer and
choosing the newlinepower level and the modulation approach on the
assigned subcarrier with the newlinegoal of satisfying individual
consumer Quality of Service QS necessities newlineIn an Orthogonal
Frequency Division Multiple Access OFDMA newlinerelay network with
a single source a single relay and a single destination the
newlineinstantaneous rate of the source is increased by power
allocation approach newlineIn an OFDMA relay network with a single
source multiple relays newlineand multiple destinations the sum
rate of the relay network is increased by newlineheuristic
subchannel allocation approach newlineIn an OFDMA relay network
with multiple sources multiple newlinerelays and a single
destination the sum rate per subcarrier is increased by
newlinesubcarrier allocation approach newlineThe opening in
relaying and OFDMA approach also provide certain newlineexciting
challenges due to the increased dynamics degree of freedom and
newlineresource reuse and difficulties present in resource
allocation and interference newlinemanagement This reality
emphasizes the significance and necessity of newlinedynamic and
intelligent resource allocation approach with efficient spectrum
newlineutilization The fundamental resources in OFDM are Subchannel
and Power newlineSubchannels experience frequency selective fading
which takes different newlinevalues for different users newline
newline
-
Advisors/Committee Members: Seshaiah, C.
Subjects/Keywords: Bit; Dynamic Subcarrier; Ofdma; Power Allocation; Relay Networks; Swarm Intelligence
Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Nagarani, S. (2013). Swarm intelligence based dynamic subcarrier bit and power
allocation for ofdma relay networks; -. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/27015
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Nagarani, S. “Swarm intelligence based dynamic subcarrier bit and power
allocation for ofdma relay networks; -.” 2013. Thesis, Anna University. Accessed December 11, 2019.
http://shodhganga.inflibnet.ac.in/handle/10603/27015.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Nagarani, S. “Swarm intelligence based dynamic subcarrier bit and power
allocation for ofdma relay networks; -.” 2013. Web. 11 Dec 2019.
Vancouver:
Nagarani S. Swarm intelligence based dynamic subcarrier bit and power
allocation for ofdma relay networks; -. [Internet] [Thesis]. Anna University; 2013. [cited 2019 Dec 11].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/27015.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Nagarani S. Swarm intelligence based dynamic subcarrier bit and power
allocation for ofdma relay networks; -. [Thesis]. Anna University; 2013. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/27015
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
24.
Sudholt, Dirk.
Computational complexity of
evolutionary algorithms, hybridizations, and swarm
intelligence.
Degree: 2008, Technische Universität Dortmund
URL: http://hdl.handle.net/2003/25954
► Bio-inspired randomized search heuristics such as evolutionary algorithms, hybridizations with local search, and swarm intelligence are very popular among practitioners as they can be applied…
(more)
▼ Bio-inspired randomized search
heuristics such as evolutionary algorithms, hybridizations with
local search, and
swarm intelligence are very popular among
practitioners as they can be applied in case the problem is not
well understood or when there is not enough knowledge, time, or
expertise to design problem-specific algorithms. Evolutionary
algorithms simulate the natural evolution of species by iteratively
applying evolutionary operators such as mutation, recombination,
and selection to a set of solutions for a given problem. A recent
trend is to hybridize evolutionary algorithms with local search to
refine newly constructed solutions by hill climbing.
Swarm
intelligence comprises ant colony optimization as well as particle
swarm optimization. These modern search paradigms rely on the
collective
intelligence of many single agents to find good
solutions for the problem at hand. Many empirical studies
demonstrate the usefulness of these heuristics for a large variety
of problems, but a thorough understanding is still far away. We
regard these algorithms from the perspective of theoretical
computer science and analyze the random time these heuristics need
to optimize pseudo-Boolean problems. This is done in a
mathematically rigorous sense, using tools known from the analysis
of randomized algorithms, and it leads to asymptotic bounds on
their computational complexity. This approach has been followed
successfully for evolutionary algorithms, but the theory of hybrid
algorithms and
swarm intelligence is still in its very infancy. Our
results shed light on the asymptotic performance of these
heuristics, increase our understanding of their dynamic behavior,
and contribute to a rigorous theoretical foundation of randomized
search heuristics.
Advisors/Committee Members: Jansen, Thomas.
Subjects/Keywords: Computational complexity;
evolutionary algorithms; hybridization; probabilistic analysis;
swarm intelligence; theory; 004
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sudholt, D. (2008). Computational complexity of
evolutionary algorithms, hybridizations, and swarm
intelligence. (Thesis). Technische Universität Dortmund. Retrieved from http://hdl.handle.net/2003/25954
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Sudholt, Dirk. “Computational complexity of
evolutionary algorithms, hybridizations, and swarm
intelligence.” 2008. Thesis, Technische Universität Dortmund. Accessed December 11, 2019.
http://hdl.handle.net/2003/25954.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sudholt, Dirk. “Computational complexity of
evolutionary algorithms, hybridizations, and swarm
intelligence.” 2008. Web. 11 Dec 2019.
Vancouver:
Sudholt D. Computational complexity of
evolutionary algorithms, hybridizations, and swarm
intelligence. [Internet] [Thesis]. Technische Universität Dortmund; 2008. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/2003/25954.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sudholt D. Computational complexity of
evolutionary algorithms, hybridizations, and swarm
intelligence. [Thesis]. Technische Universität Dortmund; 2008. Available from: http://hdl.handle.net/2003/25954
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Pretoria
25.
Rakitianskaia, A.S. (Anastassia
Sergeevna).
Using particle
swarm optimisation to train feedforward neural networks in dynamic
environments.
Degree: Computer Science, 2012, University of Pretoria
URL: http://hdl.handle.net/2263/28618
► The feedforward neural network (NN) is a mathematical model capable of representing any non-linear relationship between input and output data. It has been succesfully applied…
(more)
▼ The feedforward neural network (NN) is a mathematical
model capable of representing any non-linear relationship between
input and output data. It has been succesfully applied to a wide
variety of classification and function approximation problems.
Various neural network training algorithms were developed,
including the particle
swarm optimiser (PSO), which was shown to
outperform the standard back propagation training algorithm on a
selection of problems. However, it was usually assumed that the
environment in which a NN operates is static. Such an assumption is
often not valid for real life problems, and the training algorithms
have to be adapted accordingly. Various dynamic versions of the PSO
have already been developed. This work investigates the
applicability of dynamic PSO algorithms to NN training in dynamic
environments, and compares the performance of dynamic PSO
algorithms to the performance of back propagation. Three popular
dynamic PSO variants are considered. The extent of adaptive
properties of back propagation and dynamic PSO under different
kinds of dynamic environments is determined. Dynamic PSO is shown
to be a viable alternative to back propagation, especially under
the environments exhibiting infrequent gradual changes. Copyright
2011, University of Pretoria. All rights reserved. The copyright in
this work vests in the University of Pretoria. No part of this work
may be reproduced or transmitted in any form or by any means,
without the prior written permission of the University of Pretoria.
Please cite as follows: Rakitianskaia, A 2011, Using particle
swarm
optimisation to train feedforward neural networks in dynamic
environments, MSc dissertation, University of Pretoria, Pretoria,
viewed yymmdd <
http://upetd.up.ac.za/thesis/available/etd-02132012-233212 / >
C12/4/406/gm
Advisors/Committee Members: Engelbrecht, Andries P. (advisor).
Subjects/Keywords: Computational
intelligence; Particle swarm
optimization (PSO); Concept
drift; Neural
networks;
UCTD
Record Details
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Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Rakitianskaia, A. S. (. (2012). Using particle
swarm optimisation to train feedforward neural networks in dynamic
environments. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/28618
Chicago Manual of Style (16th Edition):
Rakitianskaia, A S (Anastassia. “Using particle
swarm optimisation to train feedforward neural networks in dynamic
environments.” 2012. Masters Thesis, University of Pretoria. Accessed December 11, 2019.
http://hdl.handle.net/2263/28618.
MLA Handbook (7th Edition):
Rakitianskaia, A S (Anastassia. “Using particle
swarm optimisation to train feedforward neural networks in dynamic
environments.” 2012. Web. 11 Dec 2019.
Vancouver:
Rakitianskaia AS(. Using particle
swarm optimisation to train feedforward neural networks in dynamic
environments. [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/2263/28618.
Council of Science Editors:
Rakitianskaia AS(. Using particle
swarm optimisation to train feedforward neural networks in dynamic
environments. [Masters Thesis]. University of Pretoria; 2012. Available from: http://hdl.handle.net/2263/28618

University of Pretoria
26.
Neethling, Charles
Marais.
Using SetPSO to
determine RNA secondary structure.
Degree: Computer Science, 2009, University of Pretoria
URL: http://hdl.handle.net/2263/29202
► RNA secondary structure prediction is an important field in Bioinformatics. A number of different approaches have been developed to simplify the determination of RNA molecule…
(more)
▼ RNA secondary structure prediction is an important field
in Bioinformatics. A number of different approaches have been
developed to simplify the determination of RNA molecule structures.
RNA is a nucleic acid found in living organisms which fulfils a
number of important roles in living cells. Knowledge of its
structure is crucial in the understanding of its function.
Determining RNA secondary structure computationally, rather than by
physical means, has the advantage of being a quicker and cheaper
method. This dissertation introduces a new Set-based Particle
Swarm
Optimisation algorithm, known as SetPSO for short, to optimise the
structure of an RNA molecule, using an advanced thermodynamic
model. Structure prediction is modelled as an energy minimisation
problem. Particle
swarm optimisation is a simple but effective
stochastic optimisation technique developed by Kennedy and
Eberhart. This simple technique was adapted to work with variable
length particles which consist of a set of elements rather than a
vector of real numbers. The effectiveness of this structure
prediction approach was compared to that of a dynamic programming
algorithm called mfold. It was found that SetPSO can be used as a
combinatorial optimisation technique which can be applied to the
problem of RNA secondary structure prediction. This research also
included an investigation into the behaviour of the new SetPSO
optimisation algorithm. Further study needs to be conducted to
evaluate the performance of SetPSO on different combinatorial and
set-based optimisation problems.
Advisors/Committee Members: Engelbrecht, Andries P. (advisor).
Subjects/Keywords: Rna; Secondary
structure;
Setpso;
Combinatorial; Computational
intelligence; Particle swarm
optimiser;
UCTD
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Neethling, C. (2009). Using SetPSO to
determine RNA secondary structure. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/29202
Chicago Manual of Style (16th Edition):
Neethling, Charles. “Using SetPSO to
determine RNA secondary structure.” 2009. Masters Thesis, University of Pretoria. Accessed December 11, 2019.
http://hdl.handle.net/2263/29202.
MLA Handbook (7th Edition):
Neethling, Charles. “Using SetPSO to
determine RNA secondary structure.” 2009. Web. 11 Dec 2019.
Vancouver:
Neethling C. Using SetPSO to
determine RNA secondary structure. [Internet] [Masters thesis]. University of Pretoria; 2009. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/2263/29202.
Council of Science Editors:
Neethling C. Using SetPSO to
determine RNA secondary structure. [Masters Thesis]. University of Pretoria; 2009. Available from: http://hdl.handle.net/2263/29202

University of Pretoria
27.
Messerschmidt, Leon.
Using particle
swarm optimization to evolve two-player game agents.
Degree: Computer Science, 2007, University of Pretoria
URL: http://hdl.handle.net/2263/23980
► Computer game-playing agents are almost as old as computers themselves, and people have been developing agents since the 1950's. Unfortunately the techniques for game-playing agents…
(more)
▼ Computer game-playing agents are almost as old as
computers themselves, and people have been developing agents since
the 1950's. Unfortunately the techniques for game-playing agents
have remained basically the same for almost half a century – an
eternity in computer time. Recently developed approaches have shown
that it is possible to develop game playing agents with the help of
learning algorithms. This study is based on the concept of
algorithms that learn how to play board games from zero initial
knowledge about playing strategies. A coevolutionary approach,
where a neural network is used to assess desirability of leaf nodes
in a game tree, and evolutionary algorithms are used to train
neural networks in competition, is overviewed. This thesis then
presents an alternative approach in which particle
swarm
optimization (PSO) is used to train the neural networks. Different
variations of the PSO are implemented and compared. The results of
the PSO approaches are also compared with that of an evolutionary
programming approach. The performance of the PSO algorithms is
investigated for different values of the PSO control parameters.
This study shows that the PSO approach can be applied successfully
to train game-playing agents.
Advisors/Committee Members: Fogel, D.B. (advisor), Engelbrecht, Andries P. (coadvisor).
Subjects/Keywords: Swarm
intelligence; Computer
games;
UCTD
Record Details
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Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Messerschmidt, L. (2007). Using particle
swarm optimization to evolve two-player game agents. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/23980
Chicago Manual of Style (16th Edition):
Messerschmidt, Leon. “Using particle
swarm optimization to evolve two-player game agents.” 2007. Masters Thesis, University of Pretoria. Accessed December 11, 2019.
http://hdl.handle.net/2263/23980.
MLA Handbook (7th Edition):
Messerschmidt, Leon. “Using particle
swarm optimization to evolve two-player game agents.” 2007. Web. 11 Dec 2019.
Vancouver:
Messerschmidt L. Using particle
swarm optimization to evolve two-player game agents. [Internet] [Masters thesis]. University of Pretoria; 2007. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/2263/23980.
Council of Science Editors:
Messerschmidt L. Using particle
swarm optimization to evolve two-player game agents. [Masters Thesis]. University of Pretoria; 2007. Available from: http://hdl.handle.net/2263/23980

University of Toledo
28.
Vaddhireddy, Jyothirmye.
A Novel Swarm Intelligence based IWD Algorithm for Routing
in MANETs.
Degree: MSin Electrical Engineering, Electrical Engineering, 2011, University of Toledo
URL: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321589580
► In this thesis a new routing protocol for Mobile Ad-Hoc Networks (MANETs) has been developed and simulated. The protocol is named IWDHocNet. With the…
(more)
▼ In this thesis a new routing protocol for
Mobile Ad-Hoc Networks (MANETs) has been developed and simulated.
The protocol is named IWDHocNet. With the explosion of technology,
the networks are becoming increasingly diverse and heterogeneous.
MANETs do not require a fixed infrastructure whereas simple
wireless networks require an infrastructure and access points
connected to a backbone. In MANETs all the nodes act as routers and
participate in discovery and maintenance of routes. These features
of MANET pose extra challenges for routing. IWDHocNet addresses the
challenges of MANET. IWDHocNet protocol takes its inspiration from
how the
swarm of water drops moves through the rivers to find the
optimum path. The protocol was simulated in NS-2
simulator under a variety of network conditions by varying the node
mobility and data traffic. The performance of the protocol was
compared with two other established routing protocols such as AODV
and DSDV. The comparisons were made based on three performance
metrics – packet delivery ratio, average end-to-end delay and
average routing load. We have found when the
mobility of network is not very fast, the performance of the
network is comparable to DSDV and AODV. However, for dynamic
network with highly mobile nodes, AODV outperformed IWDHocNet,
although the performance was still comparable with DSDV in some
situations. IWDHocNet is proactive in its current
form. For future work, it is proposed to adapt the IWDHocNet
routing protocol to be reactive like AODV as it may improve the
performance.
Advisors/Committee Members: Kaur, Devinder (Committee Chair).
Subjects/Keywords: Electrical Engineering; MANETs; Intelligent Water Drops; Swarm Intelligence; Routing
Record Details
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Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Vaddhireddy, J. (2011). A Novel Swarm Intelligence based IWD Algorithm for Routing
in MANETs. (Masters Thesis). University of Toledo. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321589580
Chicago Manual of Style (16th Edition):
Vaddhireddy, Jyothirmye. “A Novel Swarm Intelligence based IWD Algorithm for Routing
in MANETs.” 2011. Masters Thesis, University of Toledo. Accessed December 11, 2019.
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321589580.
MLA Handbook (7th Edition):
Vaddhireddy, Jyothirmye. “A Novel Swarm Intelligence based IWD Algorithm for Routing
in MANETs.” 2011. Web. 11 Dec 2019.
Vancouver:
Vaddhireddy J. A Novel Swarm Intelligence based IWD Algorithm for Routing
in MANETs. [Internet] [Masters thesis]. University of Toledo; 2011. [cited 2019 Dec 11].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321589580.
Council of Science Editors:
Vaddhireddy J. A Novel Swarm Intelligence based IWD Algorithm for Routing
in MANETs. [Masters Thesis]. University of Toledo; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321589580

University of Pretoria
29.
[No author].
Using particle swarm optimization to evolve two-player
game agents
.
Degree: 2007, University of Pretoria
URL: http://upetd.up.ac.za/thesis/available/etd-04172007-083117/
► Computer game-playing agents are almost as old as computers themselves, and people have been developing agents since the 1950's. Unfortunately the techniques for game-playing agents…
(more)
▼ Computer game-playing agents are almost as old as
computers themselves, and people have been developing agents since
the 1950's. Unfortunately the techniques for game-playing agents
have remained basically the same for almost half a century – an
eternity in computer time. Recently developed approaches have shown
that it is possible to develop game playing agents with the help of
learning algorithms. This study is based on the concept of
algorithms that learn how to play board games from zero initial
knowledge about playing strategies. A coevolutionary approach,
where a neural network is used to assess desirability of leaf nodes
in a game tree, and evolutionary algorithms are used to train
neural networks in competition, is overviewed. This thesis then
presents an alternative approach in which particle
swarm
optimization (PSO) is used to train the neural networks. Different
variations of the PSO are implemented and compared. The results of
the PSO approaches are also compared with that of an evolutionary
programming approach. The performance of the PSO algorithms is
investigated for different values of the PSO control parameters.
This study shows that the PSO approach can be applied successfully
to train game-playing agents.
Advisors/Committee Members: Fogel, D.B (advisor), Engelbrecht, Andries P (advisor).
Subjects/Keywords: Swarm intelligence;
Computer games;
UCTD
Record Details
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Share »
Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
author], [. (2007). Using particle swarm optimization to evolve two-player
game agents
. (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-04172007-083117/
Chicago Manual of Style (16th Edition):
author], [No. “Using particle swarm optimization to evolve two-player
game agents
.” 2007. Masters Thesis, University of Pretoria. Accessed December 11, 2019.
http://upetd.up.ac.za/thesis/available/etd-04172007-083117/.
MLA Handbook (7th Edition):
author], [No. “Using particle swarm optimization to evolve two-player
game agents
.” 2007. Web. 11 Dec 2019.
Vancouver:
author] [. Using particle swarm optimization to evolve two-player
game agents
. [Internet] [Masters thesis]. University of Pretoria; 2007. [cited 2019 Dec 11].
Available from: http://upetd.up.ac.za/thesis/available/etd-04172007-083117/.
Council of Science Editors:
author] [. Using particle swarm optimization to evolve two-player
game agents
. [Masters Thesis]. University of Pretoria; 2007. Available from: http://upetd.up.ac.za/thesis/available/etd-04172007-083117/

University of Pretoria
30.
Rakitianskaia, A.S. (Anastassia
Sergeevna).
Using particle swarm optimisation to train feedforward
neural networks in dynamic environments
.
Degree: 2012, University of Pretoria
URL: http://upetd.up.ac.za/thesis/available/etd-02132012-233212/
► The feedforward neural network (NN) is a mathematical model capable of representing any non-linear relationship between input and output data. It has been succesfully applied…
(more)
▼ The feedforward neural network (NN) is a
mathematical model capable of representing any non-linear
relationship between input and output data. It has been succesfully
applied to a wide variety of classification and function
approximation problems. Various neural network training algorithms
were developed, including the particle
swarm optimiser (PSO), which
was shown to outperform the standard back propagation training
algorithm on a selection of problems. However, it was usually
assumed that the environment in which a NN operates is static. Such
an assumption is often not valid for real life problems, and the
training algorithms have to be adapted accordingly. Various dynamic
versions of the PSO have already been developed. This work
investigates the applicability of dynamic PSO algorithms to NN
training in dynamic environments, and compares the performance of
dynamic PSO algorithms to the performance of back propagation.
Three popular dynamic PSO variants are considered. The extent of
adaptive properties of back propagation and dynamic PSO under
different kinds of dynamic environments is determined. Dynamic PSO
is shown to be a viable alternative to back propagation, especially
under the environments exhibiting infrequent gradual changes.
Copyright 2011, University of Pretoria. All rights reserved. The
copyright in this work vests in the University of Pretoria. No part
of this work may be reproduced or transmitted in any form or by any
means, without the prior written permission of the University of
Pretoria. Please cite as follows: Rakitianskaia, A 2011, Using
particle
swarm optimisation to train feedforward neural networks in
dynamic environments, MSc dissertation, University of Pretoria,
Pretoria, viewed yymmdd <
http://upetd.up.ac.za/thesis/available/etd-02132012-233212 / >
C12/4/406/gm
Advisors/Committee Members: Engelbrecht, Andries P (advisor).
Subjects/Keywords: Computational intelligence;
Particle swarm optimization (PSO);
Concept drift;
Neural networks;
UCTD
Record Details
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Share »
Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Rakitianskaia, A. S. (. (2012). Using particle swarm optimisation to train feedforward
neural networks in dynamic environments
. (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-02132012-233212/
Chicago Manual of Style (16th Edition):
Rakitianskaia, A S (Anastassia. “Using particle swarm optimisation to train feedforward
neural networks in dynamic environments
.” 2012. Masters Thesis, University of Pretoria. Accessed December 11, 2019.
http://upetd.up.ac.za/thesis/available/etd-02132012-233212/.
MLA Handbook (7th Edition):
Rakitianskaia, A S (Anastassia. “Using particle swarm optimisation to train feedforward
neural networks in dynamic environments
.” 2012. Web. 11 Dec 2019.
Vancouver:
Rakitianskaia AS(. Using particle swarm optimisation to train feedforward
neural networks in dynamic environments
. [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2019 Dec 11].
Available from: http://upetd.up.ac.za/thesis/available/etd-02132012-233212/.
Council of Science Editors:
Rakitianskaia AS(. Using particle swarm optimisation to train feedforward
neural networks in dynamic environments
. [Masters Thesis]. University of Pretoria; 2012. Available from: http://upetd.up.ac.za/thesis/available/etd-02132012-233212/
◁ [1] [2] [3] [4] [5] [6] [7] [8] ▶
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