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 (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 January 26, 2021.
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. 26 Jan 2021.
Vancouver:
Proffitt MR. Optimization of swarm robotic constellation communication
for object detection and event recognition. [Internet] [Masters thesis]. Western Carolina University; 2011. [cited 2021 Jan 26].
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
2.
NC DOCKS at Western Carolina University; Proffitt, Matthew R.
Optimization of swarm robotic constellation communication for object detection and event recognition.
Degree: 2011, NC Docks
URL: http://libres.uncg.edu/ir/wcu/f/Proffitt2011.pdf
► 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.
Subjects/Keywords: Robotics; Swarm intelligence
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APA (6th Edition):
NC DOCKS at Western Carolina University; Proffitt, M. R. (2011). Optimization of swarm robotic constellation communication for object detection and event recognition. (Thesis). NC Docks. Retrieved from http://libres.uncg.edu/ir/wcu/f/Proffitt2011.pdf
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):
NC DOCKS at Western Carolina University; Proffitt, Matthew R. “Optimization of swarm robotic constellation communication for object detection and event recognition.” 2011. Thesis, NC Docks. Accessed January 26, 2021.
http://libres.uncg.edu/ir/wcu/f/Proffitt2011.pdf.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
NC DOCKS at Western Carolina University; Proffitt, Matthew R. “Optimization of swarm robotic constellation communication for object detection and event recognition.” 2011. Web. 26 Jan 2021.
Vancouver:
NC DOCKS at Western Carolina University; Proffitt MR. Optimization of swarm robotic constellation communication for object detection and event recognition. [Internet] [Thesis]. NC Docks; 2011. [cited 2021 Jan 26].
Available from: http://libres.uncg.edu/ir/wcu/f/Proffitt2011.pdf.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
NC DOCKS at Western Carolina University; Proffitt MR. Optimization of swarm robotic constellation communication for object detection and event recognition. [Thesis]. NC Docks; 2011. Available from: http://libres.uncg.edu/ir/wcu/f/Proffitt2011.pdf
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Baylor University
3.
Yu, Albert Reynold, 1984-.
Optimizing multi-agent dynamics for underwater tactical applications.
Degree: M.S.E.C.E., 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 (6th Edition):
Yu, Albert Reynold, 1. (2011). Optimizing multi-agent dynamics for underwater tactical applications. (Masters Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/8181
Chicago Manual of Style (16th Edition):
Yu, Albert Reynold, 1984-. “Optimizing multi-agent dynamics for underwater tactical applications.” 2011. Masters Thesis, Baylor University. Accessed January 26, 2021.
http://hdl.handle.net/2104/8181.
MLA Handbook (7th Edition):
Yu, Albert Reynold, 1984-. “Optimizing multi-agent dynamics for underwater tactical applications.” 2011. Web. 26 Jan 2021.
Vancouver:
Yu, Albert Reynold 1. Optimizing multi-agent dynamics for underwater tactical applications. [Internet] [Masters thesis]. Baylor University; 2011. [cited 2021 Jan 26].
Available from: http://hdl.handle.net/2104/8181.
Council of Science Editors:
Yu, Albert Reynold 1. Optimizing multi-agent dynamics for underwater tactical applications. [Masters Thesis]. Baylor University; 2011. Available from: http://hdl.handle.net/2104/8181
University of Delaware
4.
Sun, Yu.
Modeling and analyzing large swarms with covert leaders.
Degree: PhD, University of Delaware, Department of Mathematical Sciences, 2015, University of Delaware
URL: http://udspace.udel.edu/handle/19716/17195
► Swarm dynamics is the study of collections of agents that interact with one another without central control. We would like to build a model to…
(more)
▼ Swarm dynamics is the study of collections of agents that interact with one another without central control. We would like to build a model to simulate the behavior of swarms with covert leaders. Then based on the model, we would like to know the stability of the system, the collective decision of the swarms when there is knowledge confliction, and the way to find the covert leaders when we observe a group of individuals in motion. In Chapter 1, we extend the covert leadership model in large swarms. A leader is a member of the
swarm that acts upon information in addition to what is provided by local interactions. A covert leader is a leader that is treated no differently than others in the
swarm, so leaders and followers participate equally in whatever interaction model is used. We focus our efforts on the behaviors driven by the three-zone swarming model and present a new nonlinear model in which leaders will respond more strongly to additional information when the
swarm is less dense. Similarly, leaders in dense regions behave more like followers. In Chapter 2, we perform linear stability analysis on the model. The result is the same as the leaderless model, which says that the growth or decay of perturbations in an infinite, uniform
swarm depends on the strength of attraction relative to repulsion and orientation. It tells us that we could inject additional information into the system without changing the stability criteria. We verify our analysis with simulation. We also compare our model with more popular linear leadership models. The leaders in our model are embedded in the swarms instead of accumulating into the front in contrast to the linear model. We apply this model to wireless robotic applications, in which densities are calculated utilizing positions of neighboring robots. The result on the QualNet platform is consistent with our ideal simulation results. In Chapter 3, we explore problems where two classes of covert leaders with different information try to influence the same
swarm. The swarms will choose the average direction if the information differential is small. The swarms will randomly choose a direction of the leaders' if the information differential is large. We validate our modeling and analysis using realistic wireless protocols and channel models on the QualNet network simulator. We also perform two case studies which are simplified forms of our model to find the bifurcation point analytically. In Chapter 4, we try to solve the problem: whether or not it is possible to distinguish between followers and leaders when we observe a group of individuals in motion. We explore the interplay between
swarm dynamics, covert leadership and theoretical information transfer. Depending upon the leadership model, leaders can use their external information either all the time or in response to local conditions. We use theoretical information transfer as a means of analyzing
swarm interactions. We find that covert leaders can be distinguished from followers in a
swarm because they receive less transfer…
Advisors/Committee Members: Rossi, Louis F..
Subjects/Keywords: Swarm intelligence – Mathematical models.
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APA (6th Edition):
Sun, Y. (2015). Modeling and analyzing large swarms with covert leaders. (Doctoral Dissertation). University of Delaware. Retrieved from http://udspace.udel.edu/handle/19716/17195
Chicago Manual of Style (16th Edition):
Sun, Yu. “Modeling and analyzing large swarms with covert leaders.” 2015. Doctoral Dissertation, University of Delaware. Accessed January 26, 2021.
http://udspace.udel.edu/handle/19716/17195.
MLA Handbook (7th Edition):
Sun, Yu. “Modeling and analyzing large swarms with covert leaders.” 2015. Web. 26 Jan 2021.
Vancouver:
Sun Y. Modeling and analyzing large swarms with covert leaders. [Internet] [Doctoral dissertation]. University of Delaware; 2015. [cited 2021 Jan 26].
Available from: http://udspace.udel.edu/handle/19716/17195.
Council of Science Editors:
Sun Y. Modeling and analyzing large swarms with covert leaders. [Doctoral Dissertation]. University of Delaware; 2015. Available from: http://udspace.udel.edu/handle/19716/17195
University of Windsor
5.
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 ·
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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 January 26, 2021.
https://scholar.uwindsor.ca/etd/7856.
MLA Handbook (7th Edition):
Zhang, Miao. “Digital Filter Design Using Improved Teaching-Learning-Based Optimization.” 2019. Web. 26 Jan 2021.
Vancouver:
Zhang M. Digital Filter Design Using Improved Teaching-Learning-Based Optimization. [Internet] [Doctoral dissertation]. University of Windsor; 2019. [cited 2021 Jan 26].
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
Vanderbilt University
6.
Kirkpatrick, Douglas Andrew.
Development And Analysis of Biologically Inspired Communication Algorithms for Artificial Agents.
Degree: MS, Computer Science, 2015, Vanderbilt University
URL: http://hdl.handle.net/1803/11788
► The capabilities of robots have increased to the point where it is more effective and economical to field a large group of less expensive robots…
(more)
▼ The capabilities of robots have increased to the point where it is more effective and economical to field a large group of less expensive robots than to field a single excessively costly robot. A consistent and effective system for managing large amounts of robots in a
swarm must be developed to expand this capability for regular application. Inspiration for these expansive communications systems can be found in the biological world. Many species, whether birds, fish, or insects, have demonstrated powerful swarming capabilities across a wide variety of tasks. Models from the biological literature have demonstrated properties and capabilities that make them ideal for
swarm robotics.
This thesis identifies three relevant
swarm communications models and develops algorithms to allow these models to interact in a robotic
swarm environment. Using a simulation, the three models are evaluated over a series of tasks chosen to approximate
swarm robotics tasks. The eight tasks included searching for goals, avoiding adversaries, and controlling the position and density of the
swarm. The capabilities of each model - closer or farther sensing capabilities or unique target selection - provided varying performances on each of the tasks. The results indicated that specific
swarm communications models need to be selected for each task to achieve optimal results.
Advisors/Committee Members: Douglas H. Fisher, Ph.D. (committee member), Julie A. Adams, Ph.D. (Committee Chair).
Subjects/Keywords: biological swarms; swarm; robotics; swarm intelligence; swarm robotics
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APA ·
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Manager
APA (6th Edition):
Kirkpatrick, D. A. (2015). Development And Analysis of Biologically Inspired Communication Algorithms for Artificial Agents. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11788
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):
Kirkpatrick, Douglas Andrew. “Development And Analysis of Biologically Inspired Communication Algorithms for Artificial Agents.” 2015. Thesis, Vanderbilt University. Accessed January 26, 2021.
http://hdl.handle.net/1803/11788.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Kirkpatrick, Douglas Andrew. “Development And Analysis of Biologically Inspired Communication Algorithms for Artificial Agents.” 2015. Web. 26 Jan 2021.
Vancouver:
Kirkpatrick DA. Development And Analysis of Biologically Inspired Communication Algorithms for Artificial Agents. [Internet] [Thesis]. Vanderbilt University; 2015. [cited 2021 Jan 26].
Available from: http://hdl.handle.net/1803/11788.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Kirkpatrick DA. Development And Analysis of Biologically Inspired Communication Algorithms for Artificial Agents. [Thesis]. Vanderbilt University; 2015. Available from: http://hdl.handle.net/1803/11788
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://hdl.handle.net/1803/11985
► 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: Yevgeniy Vorobeychik (committee member), Maithilee Kunda (committee member), Jennifer S. Trueblood (committee member), Alexander S. Mentis (committee member), Julie A. Adams (Committee Chair).
Subjects/Keywords: multi-agent systems; swarm intelligence; collective decision making; human-swarm interaction
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APA ·
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MLA ·
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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://hdl.handle.net/1803/11985
Chicago Manual of Style (16th Edition):
Cody, Jason Robert. “Discrete Consensus Decisions in Human-Collective Teams.” 2018. Doctoral Dissertation, Vanderbilt University. Accessed January 26, 2021.
http://hdl.handle.net/1803/11985.
MLA Handbook (7th Edition):
Cody, Jason Robert. “Discrete Consensus Decisions in Human-Collective Teams.” 2018. Web. 26 Jan 2021.
Vancouver:
Cody JR. Discrete Consensus Decisions in Human-Collective Teams. [Internet] [Doctoral dissertation]. Vanderbilt University; 2018. [cited 2021 Jan 26].
Available from: http://hdl.handle.net/1803/11985.
Council of Science Editors:
Cody JR. Discrete Consensus Decisions in Human-Collective Teams. [Doctoral Dissertation]. Vanderbilt University; 2018. Available from: http://hdl.handle.net/1803/11985
NSYSU
8.
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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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 January 26, 2021.
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. 26 Jan 2021.
Vancouver:
PRATHYUSHA Y. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Jan 26].
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
9.
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 (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 January 26, 2021.
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. 26 Jan 2021.
Vancouver:
Talukder S. Mathematicle Modelling and Applications of Particle Swarm Optimization. [Internet] [Thesis]. , School of Engineering; 2011. [cited 2021 Jan 26].
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
10.
Sudheer Menon, Vishnu.
Decentralized Approach to SLAM using Computationally Limited Robots.
Degree: MS, 2017, Worcester Polytechnic Institute
URL: etd-052517-130653
;
https://digitalcommons.wpi.edu/etd-theses/1315
► Simultaneous localization and mapping (SLAM) is a challenging and vital problem in robotics. It is important in tasks such as disaster response, deep-sea and cave…
(more)
▼ Simultaneous localization and mapping (SLAM) is a challenging and vital problem in robotics. It is important in tasks such as disaster response, deep-sea and cave exploration, in which robots must construct a map of an unknown terrain, and at the same time localize themselves within the map. The issue with single- robot SLAM is the relatively high rate of failure in a realistic application, as well as the time and energy cost. In this work, we propose a new approach to decentralized multi-robot SLAM which uses a robot
swarm to map the environment. This system is capable of mapping an environment without human assistance and without the need for any additional infrastructure. We assume that 1) no robot possesses sufficient memory to store the entire map of the environment, 2) the communication range of the robots is limited, and 3)there is no infrastructure present in the environment to assist the robot in communicating with others. To cope with these limitations, the
swarm system is designed to work as an independent entity. The
swarm can deploy new robots towards the region that is yet to be explored, coordinate the communication between the robots by using itself as the communication network and replace any malfunctioning robots. The proposed method proves to be a reliable and robust exploration algorithm. It is shown to be a self-growing mapping network that is able to coordinate among numerous robots and replace any broken robots hence reducing the chance of system failure.
Advisors/Committee Members: Carlo Pinciroli, Advisor, Michael A. Gennert, Committee Member, Eugene Eberbach.
Subjects/Keywords: Multi-robot systems; SLAM; Swarm intelligence; Swarm robotics
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APA (6th Edition):
Sudheer Menon, V. (2017). Decentralized Approach to SLAM using Computationally Limited Robots. (Thesis). Worcester Polytechnic Institute. Retrieved from etd-052517-130653 ; https://digitalcommons.wpi.edu/etd-theses/1315
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):
Sudheer Menon, Vishnu. “Decentralized Approach to SLAM using Computationally Limited Robots.” 2017. Thesis, Worcester Polytechnic Institute. Accessed January 26, 2021.
etd-052517-130653 ; https://digitalcommons.wpi.edu/etd-theses/1315.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sudheer Menon, Vishnu. “Decentralized Approach to SLAM using Computationally Limited Robots.” 2017. Web. 26 Jan 2021.
Vancouver:
Sudheer Menon V. Decentralized Approach to SLAM using Computationally Limited Robots. [Internet] [Thesis]. Worcester Polytechnic Institute; 2017. [cited 2021 Jan 26].
Available from: etd-052517-130653 ; https://digitalcommons.wpi.edu/etd-theses/1315.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sudheer Menon V. Decentralized Approach to SLAM using Computationally Limited Robots. [Thesis]. Worcester Polytechnic Institute; 2017. Available from: etd-052517-130653 ; https://digitalcommons.wpi.edu/etd-theses/1315
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Oregon State University
11.
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 (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 January 26, 2021.
http://hdl.handle.net/1957/45107.
MLA Handbook (7th Edition):
Potanapalli, Kranti Kumar. “Coactive learning for multi-robot search and coverage.” 2013. Web. 26 Jan 2021.
Vancouver:
Potanapalli KK. Coactive learning for multi-robot search and coverage. [Internet] [Masters thesis]. Oregon State University; 2013. [cited 2021 Jan 26].
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
Univerzitet u Beogradu
12.
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 (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 January 26, 2021.
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. 26 Jan 2021.
Vancouver:
Bačanin-Džakula, Nebojša V. 1. Unapređenje hibridizacijom metaheuristika inteligencije
rojeva za rešavanje problema globalne optimizacije. [Internet] [Thesis]. Univerzitet u Beogradu; 2016. [cited 2021 Jan 26].
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
University of Georgia
13.
Geiger, Peter C.
A comparison of novel stochastic optimization methods.
Degree: 2015, University of Georgia
URL: http://hdl.handle.net/10724/32903
► Many real world problems are too complex to solve with traditional programming methods in a reasonable amount of time. Stochastic optimization techniques have been applied…
(more)
▼ Many real world problems are too complex to solve with traditional programming methods in a reasonable amount of time. Stochastic optimization techniques have been applied to this class of problems with success. Set up and tuning an
algorithm can be a daunting task, so this thesis first presents a method of simple optimization requiring no tuning parameters. Then, methods for dealing with search spaces with invalid solution space are introduced and compared.
Subjects/Keywords: Optimization; Genetic Algorithm; Swarm Intelligence; Repair Operators
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APA (6th Edition):
Geiger, P. C. (2015). A comparison of novel stochastic optimization methods. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/32903
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):
Geiger, Peter C. “A comparison of novel stochastic optimization methods.” 2015. Thesis, University of Georgia. Accessed January 26, 2021.
http://hdl.handle.net/10724/32903.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Geiger, Peter C. “A comparison of novel stochastic optimization methods.” 2015. Web. 26 Jan 2021.
Vancouver:
Geiger PC. A comparison of novel stochastic optimization methods. [Internet] [Thesis]. University of Georgia; 2015. [cited 2021 Jan 26].
Available from: http://hdl.handle.net/10724/32903.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Geiger PC. A comparison of novel stochastic optimization methods. [Thesis]. University of Georgia; 2015. Available from: http://hdl.handle.net/10724/32903
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
The Ohio State University
14.
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 ·
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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 January 26, 2021.
http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482.
MLA Handbook (7th Edition):
Gazi, Veysel. “Stability analysis of swarms.” 2002. Web. 26 Jan 2021.
Vancouver:
Gazi V. Stability analysis of swarms. [Internet] [Doctoral dissertation]. The Ohio State University; 2002. [cited 2021 Jan 26].
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
Vanderbilt University
15.
Sengupta, Saptarshi.
QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives.
Degree: PhD, Electrical Engineering, 2019, Vanderbilt University
URL: http://hdl.handle.net/1803/14438
► With sensor fusion and data-driven approaches taking center stage in ubiquitous computing, customized and application-specific optimization methods are increasingly important. The interest follows in part…
(more)
▼ With sensor fusion and data-driven approaches taking center stage in ubiquitous computing, customized and application-specific optimization methods are increasingly important. The interest follows in part from the limitations of specific optimization methods implied by the No Free Lunch Theorem. Applications of computational
intelligence are growing exponentially with the widespread availability of increasingly powerful computers. This has made feasible the mimicry of highly interactive multi-agent models of natural systems that solve complicated problems while remaining stable. The emergent behaviors arising in such systems hint at novel methods of optimization that can find solutions to machine learning problems of similar complexity.
This dissertation introduces a social, agent-based (
swarm)
intelligence algorithm viz. the Quantum Double Delta
Swarm (QDDS). It is modeled after the mechanism of convergence, under an attractive potential field, to the center of a single well in a double Dirac delta potential-well problem. The swarming model developed here extends the well-known Quantum-behaved Particle
Swarm Optimization (QPSO) algorithm to the more stable, double well configuration for optimal solutions of complex engineering design problems. Theoretical foundations and experimental illustrations lead to applications of the model to find solutions of problems in intrinsically high-dimensional feature spaces. In addition, the effects of chaos on the exploratory capacity of the algorithm are studied by including a Chebyshev map driven exploration (C-QDDS) step and benchmarking the results. Visualization of the process is enabled by tracking the trajectory of the best performing agent in each iteration over all episodes across benchmark contours. Under general assumptions common to random search convergence proofs the dynamical limitations of this model’s convergence are critically analyzed. Finally, results are demonstrated on: a) the multidimensional finite impulse response (FIR) filter design problem and b) Neuro-evolution, specifically using a two-layer neural architecture where the C-QDDS search mutates candidate architectures whose weights and biases are then trained using gradient-free swarming.
Advisors/Committee Members: Douglas Hardin (committee member), Nilanjan Sarkar (committee member), Don Mitchell Wilkes (committee member), Kazuhiko Kawamura (committee member), Alan Peters (Committee Chair).
Subjects/Keywords: quantum-inspired computational intelligence; stochastic optimization; particle swarm optimization; multiagent systems; swarm intelligence; distributed artificial intelligence
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APA (6th Edition):
Sengupta, S. (2019). QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/14438
Chicago Manual of Style (16th Edition):
Sengupta, Saptarshi. “QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives.” 2019. Doctoral Dissertation, Vanderbilt University. Accessed January 26, 2021.
http://hdl.handle.net/1803/14438.
MLA Handbook (7th Edition):
Sengupta, Saptarshi. “QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives.” 2019. Web. 26 Jan 2021.
Vancouver:
Sengupta S. QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives. [Internet] [Doctoral dissertation]. Vanderbilt University; 2019. [cited 2021 Jan 26].
Available from: http://hdl.handle.net/1803/14438.
Council of Science Editors:
Sengupta S. QDDS – A Novel Quantum-inspired Swarm Optimizer: Theoretical Foundations, Convergence Analyses and Application Perspectives. [Doctoral Dissertation]. Vanderbilt University; 2019. Available from: http://hdl.handle.net/1803/14438
Brno University of Technology
16.
Winklerová, Zdenka.
Inteligence skupiny: Swarm Intelligence.
Degree: 2019, Brno University of Technology
URL: http://hdl.handle.net/11012/63276
► The intention of the dissertation is the applied research of the collective ( group ) ( swarm ) intelligence . To demonstrate the applicability of the…
(more)
▼ 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 algorithm to the true global solution of the solved problem. A search strategy solution has been proposed in which the PSO algorithm regulates its stability by continuous adjustment of the particles behaviour to the characteristics of the problem space. The proposed algorithm influenced the evolution of the searching of the problem space, so that the probability of the successful problem solution increased.
Advisors/Committee Members: Zbořil, František (advisor), Šaloun, Petr (referee), Škrinárová,, Jarmila (referee).
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 (6th Edition):
Winklerová, Z. (2019). Inteligence skupiny: Swarm Intelligence. (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: Swarm Intelligence.” 2019. Thesis, Brno University of Technology. Accessed January 26, 2021.
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: Swarm Intelligence.” 2019. Web. 26 Jan 2021.
Vancouver:
Winklerová Z. Inteligence skupiny: Swarm Intelligence. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 26].
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: Swarm Intelligence. [Thesis]. Brno University of Technology; 2019. 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
17.
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 (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 January 26, 2021.
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. 26 Jan 2021.
Vancouver:
Kelman A. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. [Internet] [Thesis]. University of Skövde; 2017. [cited 2021 Jan 26].
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
18.
Magg, Sven.
Self-organised task differentiation in homogeneous and heterogeneous groups of autonomous agents.
Degree: PhD, 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: 629.892; self-organisation; differentiation; talk allocation; adaptive behaviour; swarm intelligence; multi-agent-systems; swarm robotics
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APA ·
Chicago ·
MLA ·
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CSE |
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APA (6th Edition):
Magg, S. (2012). Self-organised task differentiation in homogeneous and heterogeneous groups of autonomous agents. (Doctoral Dissertation). University of Hertfordshire. Retrieved from http://hdl.handle.net/2299/9038
Chicago Manual of Style (16th Edition):
Magg, Sven. “Self-organised task differentiation in homogeneous and heterogeneous groups of autonomous agents.” 2012. Doctoral Dissertation, University of Hertfordshire. Accessed January 26, 2021.
http://hdl.handle.net/2299/9038.
MLA Handbook (7th Edition):
Magg, Sven. “Self-organised task differentiation in homogeneous and heterogeneous groups of autonomous agents.” 2012. Web. 26 Jan 2021.
Vancouver:
Magg S. Self-organised task differentiation in homogeneous and heterogeneous groups of autonomous agents. [Internet] [Doctoral dissertation]. University of Hertfordshire; 2012. [cited 2021 Jan 26].
Available from: http://hdl.handle.net/2299/9038.
Council of Science Editors:
Magg S. Self-organised task differentiation in homogeneous and heterogeneous groups of autonomous agents. [Doctoral Dissertation]. University of Hertfordshire; 2012. Available from: http://hdl.handle.net/2299/9038
University of Georgia
19.
Oruganti Venkata, Sanjay Sarma.
A swarm engineering framework for microtubule self-organization.
Degree: 2018, 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.
Subjects/Keywords: Microtubules; Microtubule Associated Proteins; Self-Organization; Swarm Engineering; Swarm Intelligence; Game Engine; Protofilaments
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APA ·
Chicago ·
MLA ·
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Export
to Zotero / EndNote / Reference
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APA (6th Edition):
Oruganti Venkata, S. S. (2018). A swarm engineering framework for microtubule self-organization. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37465
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):
Oruganti Venkata, Sanjay Sarma. “A swarm engineering framework for microtubule self-organization.” 2018. Thesis, University of Georgia. Accessed January 26, 2021.
http://hdl.handle.net/10724/37465.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Oruganti Venkata, Sanjay Sarma. “A swarm engineering framework for microtubule self-organization.” 2018. Web. 26 Jan 2021.
Vancouver:
Oruganti Venkata SS. A swarm engineering framework for microtubule self-organization. [Internet] [Thesis]. University of Georgia; 2018. [cited 2021 Jan 26].
Available from: http://hdl.handle.net/10724/37465.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Oruganti Venkata SS. A swarm engineering framework for microtubule self-organization. [Thesis]. University of Georgia; 2018. Available from: http://hdl.handle.net/10724/37465
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of New Mexico
20.
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 January 26, 2021.
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. 26 Jan 2021.
Vancouver:
Lu Q. An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms. [Internet] [Doctoral dissertation]. University of New Mexico; 2019. [cited 2021 Jan 26].
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
Brno University of Technology
21.
Veselý, Filip.
Optimalizační úlohy na bázi částicových hejn (PSO): PSO-Particle Swarm Optimizations.
Degree: 2016, Brno University of Technology
URL: http://hdl.handle.net/11012/53237
► This work deals with swarm intelligence, strictly speaking particle swarm intelligence. It shortly describes questions of optimization and some optimization techniques. Part of this work…
(more)
▼ This work deals with
swarm intelligence, strictly speaking particle
swarm intelligence. It shortly describes questions of optimization and some optimization techniques. Part of this work is recherché of variants of particle
swarm optimization algorithm. These algorithms are mathematically described. Their advantages or disadvantages in comparison with the basic PSO algorithm are mentioned. The second part of this work describes mQPSO algorithm and created modification mQPSOPC. Described algorithms are compared with each other and with another evolution algorithm on several tests.
Advisors/Committee Members: Schwarz, Josef (advisor), Jaroš, Jiří (referee).
Subjects/Keywords: umělá inteligence; inteligence roje; částicové hejno; optimalizace; PSO; artificial intelligence; swarm intelligence; particle swarm; optimization; PSO
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Veselý, F. (2016). Optimalizační úlohy na bázi částicových hejn (PSO): PSO-Particle Swarm Optimizations. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/53237
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):
Veselý, Filip. “Optimalizační úlohy na bázi částicových hejn (PSO): PSO-Particle Swarm Optimizations.” 2016. Thesis, Brno University of Technology. Accessed January 26, 2021.
http://hdl.handle.net/11012/53237.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Veselý, Filip. “Optimalizační úlohy na bázi částicových hejn (PSO): PSO-Particle Swarm Optimizations.” 2016. Web. 26 Jan 2021.
Vancouver:
Veselý F. Optimalizační úlohy na bázi částicových hejn (PSO): PSO-Particle Swarm Optimizations. [Internet] [Thesis]. Brno University of Technology; 2016. [cited 2021 Jan 26].
Available from: http://hdl.handle.net/11012/53237.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Veselý F. Optimalizační úlohy na bázi částicových hejn (PSO): PSO-Particle Swarm Optimizations. [Thesis]. Brno University of Technology; 2016. Available from: http://hdl.handle.net/11012/53237
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Pretoria
22.
[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
Record Details
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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 January 26, 2021.
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. 26 Jan 2021.
Vancouver:
author] [. Particle swarm optimisation in dynamically changing
environments - an empirical study
. [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2021 Jan 26].
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
23.
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 January 26, 2021.
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. 26 Jan 2021.
Vancouver:
Duhain JGO. Particle swarm
optimisation in dynamically changing environments - an empirical
study. [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2021 Jan 26].
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
24.
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
Record Details
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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 January 26, 2021.
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. 26 Jan 2021.
Vancouver:
Sun Y. Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation. [Internet] [Doctoral dissertation]. Université Paris-Est; 2011. [cited 2021 Jan 26].
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
25.
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 (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 January 26, 2021.
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. 26 Jan 2021.
Vancouver:
Steyven ASW. A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics. [Internet] [Doctoral dissertation]. Edinburgh Napier University; 2017. [cited 2021 Jan 26].
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
University of Pretoria
26.
[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
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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 January 26, 2021.
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. 26 Jan 2021.
Vancouver:
author] [. Using particle swarm optimization to evolve two-player
game agents
. [Internet] [Masters thesis]. University of Pretoria; 2007. [cited 2021 Jan 26].
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
27.
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
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CSE |
Export
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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 January 26, 2021.
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. 26 Jan 2021.
Vancouver:
Rakitianskaia AS(. Using particle swarm optimisation to train feedforward
neural networks in dynamic environments
. [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2021 Jan 26].
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/
University of Pretoria
28.
[No author].
Using SetPSO to determine RNA secondary
structure
.
Degree: 2009, University of Pretoria
URL: http://upetd.up.ac.za/thesis/available/etd-02162009-112429/
► 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 ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
author], [. (2009). Using SetPSO to determine RNA secondary
structure
. (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-02162009-112429/
Chicago Manual of Style (16th Edition):
author], [No. “Using SetPSO to determine RNA secondary
structure
.” 2009. Masters Thesis, University of Pretoria. Accessed January 26, 2021.
http://upetd.up.ac.za/thesis/available/etd-02162009-112429/.
MLA Handbook (7th Edition):
author], [No. “Using SetPSO to determine RNA secondary
structure
.” 2009. Web. 26 Jan 2021.
Vancouver:
author] [. Using SetPSO to determine RNA secondary
structure
. [Internet] [Masters thesis]. University of Pretoria; 2009. [cited 2021 Jan 26].
Available from: http://upetd.up.ac.za/thesis/available/etd-02162009-112429/.
Council of Science Editors:
author] [. Using SetPSO to determine RNA secondary
structure
. [Masters Thesis]. University of Pretoria; 2009. Available from: http://upetd.up.ac.za/thesis/available/etd-02162009-112429/
University of Pretoria
29.
[No author].
A study of gradient based particle swarm
optimisers
.
Degree: 2011, University of Pretoria
URL: http://upetd.up.ac.za/thesis/available/etd-11292010-143123/
► Gradient-based optimisers are a natural way to solve optimisation problems, and have long been used for their efficacy in exploiting the search space. Particle swarm…
(more)
▼ Gradient-based optimisers are a natural way to solve
optimisation problems, and have long been used for their efficacy
in exploiting the search space. Particle
swarm optimisers (PSOs),
when using reasonable algorithm parameters, are considered to have
good exploration characteristics. This thesis proposes a specific
way of constructing hybrid gradient PSOs. Heterogeneous, hybrid
gradient PSOs are constructed by allowing the gradient algorithm to
optimise local best particles, while the PSO algorithm governs the
behaviour of the rest of the
swarm. This approach allows the
distinct algorithms to concentrate on performing the separate tasks
of exploration and exploitation. Two new PSOs, the Gradient Descent
PSO, which combines the Gradient Descent and PSO algorithms, and
the LeapFrog PSO, which combines the LeapFrog and PSO algorithms,
are introduced. The GDPSO represents arguably the simplest hybrid
gradient PSO possible, while the LeapFrog PSO incorporates the more
sophisticated LFOP1(b) algorithm, exhibiting a heuristic algorithm
design and dynamic time step adjustment mechanism. The strong
tendency of these hybrids to prematurely converge is examined, and
it is shown that by modifying algorithm parameters and delaying the
introduction of gradient information, it is possible to retain
strong exploration capabilities of the original PSO algorithm while
also benefiting from the exploitation of the gradient
algorithms.
Advisors/Committee Members: Engelbrecht, Andries P (advisor).
Subjects/Keywords: Artificial intelligence;
Gradient methods;
Hybrid;
Particle swarm optimisers;
UCTD
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
author], [. (2011). A study of gradient based particle swarm
optimisers
. (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-11292010-143123/
Chicago Manual of Style (16th Edition):
author], [No. “A study of gradient based particle swarm
optimisers
.” 2011. Masters Thesis, University of Pretoria. Accessed January 26, 2021.
http://upetd.up.ac.za/thesis/available/etd-11292010-143123/.
MLA Handbook (7th Edition):
author], [No. “A study of gradient based particle swarm
optimisers
.” 2011. Web. 26 Jan 2021.
Vancouver:
author] [. A study of gradient based particle swarm
optimisers
. [Internet] [Masters thesis]. University of Pretoria; 2011. [cited 2021 Jan 26].
Available from: http://upetd.up.ac.za/thesis/available/etd-11292010-143123/.
Council of Science Editors:
author] [. A study of gradient based particle swarm
optimisers
. [Masters Thesis]. University of Pretoria; 2011. Available from: http://upetd.up.ac.za/thesis/available/etd-11292010-143123/
Ryerson University
30.
Obaidullah, Muhammad.
Application mapping and NoC configuration using hybrid particle swarm optimization.
Degree: 2017, Ryerson University
URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A6881
► Network-on-Chip (NoC) has been proposed as an interconnection framework for connecting large number of cores for a System-on-Chip (SoC). Assuming a mesh-based NoC, we investigate…
(more)
▼ Network-on-Chip (NoC) has been proposed as an interconnection framework for connecting large number of cores for a System-on-Chip (SoC). Assuming a mesh-based NoC, we investigate application mapping and NoC configuration optimization using a hybrid optimization scheme. Our technique, Hybrid Discrete Particle
Swarm Optimization (HDPSO), combines Tabu-search, communication volume based core swapping, and
swarm intelligence. We employ a Tabu-list to discourage
swarm particles to re-visit the explored search space and propose an alternative route towards the intended movement direction. In each iteration of
swarm, a sub-
swarm containing configuration solutions (sub-particles) searches for optimal configuration for the parent particle (mapping solution). Optimization goals include minimum average communication latency, power, area, credit loop latency, and maximum average link duty factor. The proposed technique is tested for well-known multimedia application core graphs and several large synthetic cores-graphs. It was found that on average our hybrid scheme generates high quality NoC mapping and configuration solutions when compared to some existing stochastic optimization techniques.
Advisors/Committee Members: Khan, Gul N. (Degree supervisor).
Subjects/Keywords: Mathematical optimization.; Networks on a chip – Design and construction.; Swarm intelligence.
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APA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Obaidullah, M. (2017). Application mapping and NoC configuration using hybrid particle swarm optimization. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A6881
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):
Obaidullah, Muhammad. “Application mapping and NoC configuration using hybrid particle swarm optimization.” 2017. Thesis, Ryerson University. Accessed January 26, 2021.
https://digital.library.ryerson.ca/islandora/object/RULA%3A6881.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Obaidullah, Muhammad. “Application mapping and NoC configuration using hybrid particle swarm optimization.” 2017. Web. 26 Jan 2021.
Vancouver:
Obaidullah M. Application mapping and NoC configuration using hybrid particle swarm optimization. [Internet] [Thesis]. Ryerson University; 2017. [cited 2021 Jan 26].
Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A6881.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Obaidullah M. Application mapping and NoC configuration using hybrid particle swarm optimization. [Thesis]. Ryerson University; 2017. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A6881
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
◁ [1] [2] [3] [4] [5] [6] [7] [8] [9] ▶
. 
Open Access Theses and Dissertations
