subject:(Swarm Intelligence)

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 (6^th 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 (16^th Edition):

Proffitt, Matthew R. “Optimization of swarm robotic constellation communication for object detection and event recognition.” 2011. Masters Thesis, Western Carolina University. Accessed December 11, 2019. http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874.

MLA Handbook (7^th Edition):

Proffitt, Matthew R. “Optimization of swarm robotic constellation communication for object detection and event recognition.” 2011. Web. 11 Dec 2019.

Vancouver:

Proffitt MR. Optimization of swarm robotic constellation communication for object detection and event recognition. [Internet] [Masters thesis]. Western Carolina University; 2011. [cited 2019 Dec 11]. Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874.

Council of Science Editors:

Proffitt MR. Optimization of swarm robotic constellation communication for object detection and event recognition. [Masters Thesis]. Western Carolina University; 2011. Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=7874

Baylor University

2. Yu, Albert R. Optimizing multi-agent dynamics for underwater tactical applications.

Degree: Engineering., 2011, Baylor University

URL: http://hdl.handle.net/2104/8181

► Large groups of autonomous agents, or swarms, can exhibit complex emergent behaviors that are difficult to predict and characterize from their low-level interactions. These emergent… (more)

Subjects/Keywords: Swarm intelligence.; Multi-agent systems.; Swarm inversion.

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APA (6^th Edition):

Yu, A. R. (2011). Optimizing multi-agent dynamics for underwater tactical applications. (Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/8181

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Yu, Albert R. “Optimizing multi-agent dynamics for underwater tactical applications. ” 2011. Thesis, Baylor University. Accessed December 11, 2019. http://hdl.handle.net/2104/8181.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Yu, Albert R. “Optimizing multi-agent dynamics for underwater tactical applications. ” 2011. Web. 11 Dec 2019.

Vancouver:

Yu AR. Optimizing multi-agent dynamics for underwater tactical applications. [Internet] [Thesis]. Baylor University; 2011. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/2104/8181.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yu AR. Optimizing multi-agent dynamics for underwater tactical applications. [Thesis]. Baylor University; 2011. Available from: http://hdl.handle.net/2104/8181

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Windsor

3. Zhang, Miao. Digital Filter Design Using Improved Teaching-Learning-Based Optimization.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Windsor

URL: https://scholar.uwindsor.ca/etd/7856

► Digital filters are an important part of digital signal processing systems. Digital filters are divided into finite impulse response (FIR) digital filters and infinite impulse… (more)

▼ Digital filters are an important part of digital signal processing systems. Digital filters are divided into finite impulse response (FIR) digital filters and infinite impulse response (IIR) digital filters according to the length of their impulse responses. An FIR digital filter is easier to implement than an IIR digital filter because of its linear phase and stability properties. In terms of the stability of an IIR digital filter, the poles generated in the denominator are subject to stability constraints. In addition, a digital filter can be categorized as one-dimensional or multi-dimensional digital filters according to the dimensions of the signal to be processed. However, for the design of IIR digital filters, traditional design methods have the disadvantages of easy to fall into a local optimum and slow convergence. The Teaching-Learning-Based optimization (TLBO) algorithm has been proven beneficial in a wide range of engineering applications. To this end, this dissertation focusses on using TLBO and its improved algorithms to design five types of digital filters, which include linear phase FIR digital filters, multiobjective general FIR digital filters, multiobjective IIR digital filters, two-dimensional (2-D) linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters. Among them, linear phase FIR digital filters, 2-D linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters use single-objective type of TLBO algorithms to optimize; multiobjective general FIR digital filters use multiobjective non-dominated TLBO (MOTLBO) algorithm to optimize; and multiobjective IIR digital filters use MOTLBO with Euclidean distance to optimize. The design results of the five types of filter designs are compared to those obtained by other state-of-the-art design methods. In this dissertation, two major improvements are proposed to enhance the performance of the standard TLBO algorithm. The first improvement is to apply a gradient-based learning to replace the TLBO learner phase to reduce approximation error(s) and CPU time without sacrificing design accuracy for linear phase FIR digital filter design. The second improvement is to incorporate Manhattan distance to simplify the procedure of the multiobjective non-dominated TLBO (MOTLBO) algorithm for general FIR digital filter design. The design results obtained by the two improvements have demonstrated their efficiency and effectiveness. Advisors/Committee Members: Kwan, H.K..

Subjects/Keywords: digital filters; swarm intelligence algorithms

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APA (6^th 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 (16^th Edition):

Zhang, Miao. “Digital Filter Design Using Improved Teaching-Learning-Based Optimization.” 2019. Doctoral Dissertation, University of Windsor. Accessed December 11, 2019. https://scholar.uwindsor.ca/etd/7856.

MLA Handbook (7^th Edition):

Zhang, Miao. “Digital Filter Design Using Improved Teaching-Learning-Based Optimization.” 2019. Web. 11 Dec 2019.

Vancouver:

Zhang M. Digital Filter Design Using Improved Teaching-Learning-Based Optimization. [Internet] [Doctoral dissertation]. University of Windsor; 2019. [cited 2019 Dec 11]. Available from: https://scholar.uwindsor.ca/etd/7856.

Council of Science Editors:

Zhang M. Digital Filter Design Using Improved Teaching-Learning-Based Optimization. [Doctoral Dissertation]. University of Windsor; 2019. Available from: https://scholar.uwindsor.ca/etd/7856

University of Saskatchewan

4. Moallem, Azin. Using swarm intelligence for distributed job scheduling on the grid.

Degree: 2009, University of Saskatchewan

URL: http://hdl.handle.net/10388/etd-04132009-123250

► With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. Grids are playing an important… (more)

Subjects/Keywords: Ant colony; Swarm intelligence; Grid; particle Swarm; Load balancing

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APA (6^th Edition):

Moallem, A. (2009). Using swarm intelligence for distributed job scheduling on the grid. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/etd-04132009-123250

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Moallem, Azin. “Using swarm intelligence for distributed job scheduling on the grid.” 2009. Thesis, University of Saskatchewan. Accessed December 11, 2019. http://hdl.handle.net/10388/etd-04132009-123250.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Moallem, Azin. “Using swarm intelligence for distributed job scheduling on the grid.” 2009. Web. 11 Dec 2019.

Vancouver:

Moallem A. Using swarm intelligence for distributed job scheduling on the grid. [Internet] [Thesis]. University of Saskatchewan; 2009. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/10388/etd-04132009-123250.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Moallem A. Using swarm intelligence for distributed job scheduling on the grid. [Thesis]. University of Saskatchewan; 2009. Available from: http://hdl.handle.net/10388/etd-04132009-123250

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

5. Talukder, Satyobroto. Mathematicle Modelling and Applications of Particle Swarm Optimization.

Degree: 2011, , School of Engineering

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671

► Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or… (more)

Subjects/Keywords: Optimization; swarm intelligence; particle swarm; social network; convergence; stagnation; multi-objective.

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APA (6^th 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 (16^th Edition):

Talukder, Satyobroto. “Mathematicle Modelling and Applications of Particle Swarm Optimization.” 2011. Thesis, , School of Engineering. Accessed December 11, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Talukder, Satyobroto. “Mathematicle Modelling and Applications of Particle Swarm Optimization.” 2011. Web. 11 Dec 2019.

Vancouver:

Talukder S. Mathematicle Modelling and Applications of Particle Swarm Optimization. [Internet] [Thesis]. , School of Engineering; 2011. [cited 2019 Dec 11]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Talukder S. Mathematicle Modelling and Applications of Particle Swarm Optimization. [Thesis]. , School of Engineering; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

6. PRATHYUSHA, YERRA. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization.

Degree: Master, Computer Science and Engineering, 2018, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344

► The air pollution has become a major ecological issue. The surpassed pollution levels can be controlled by searching the pollution source. An environmental monitoring UAVs… (more)

Subjects/Keywords: Swarm intelligence; UAV; Particle Swarm Optimization (PSO); Navigation algorithm; Path planning

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APA (6^th 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 (16^th Edition):

PRATHYUSHA, YERRA. “UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization.” 2018. Thesis, NSYSU. Accessed December 11, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

PRATHYUSHA, YERRA. “UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization.” 2018. Web. 11 Dec 2019.

Vancouver:

PRATHYUSHA Y. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 Dec 11]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

PRATHYUSHA Y. UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804118-233344

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Vanderbilt University

7. Cody, Jason Robert. Discrete Consensus Decisions in Human-Collective Teams.

Degree: PhD, Computer Science, 2018, Vanderbilt University

URL: http://etd.library.vanderbilt.edu/available/etd-04032018-164817/ ;

► Robotic collectives are large decentralized robot groups of more than fifty individuals that coordinate using interactions inspired by social insect behaviors. Collectives make group decisions… (more)

▼ Robotic collectives are large decentralized robot groups of more than fifty individuals that coordinate using interactions inspired by social insect behaviors. Collectives make group decisions that are facilitated by information pooling within a shared decision space, similar to an insect colony. Biological collectives must frequently choose the best option from a finite set of options and execute an action based on that choice. Discrete collective consensus achievement algorithms have enabled robotic collectives to make decisions, but most research does not consider scenarios in which the collective must ignore biasing features within the environment. Whether the collective decides between occupation sites, routes, or future actions, the environmental features (e.g., distance between a resource and the collective) alter robot interactions and bias collective decisions towards options that are the easiest to find, evaluate, and reach, but may not be the optimal choice. Robotic collectives that must ignore biasing environmental features during decision making are likely to be inaccurate and inefficient. Robotic collectives do not have centralized control; thus, they are challenged to synchronize the initiation and execution of the chosen actions, which is critical to future collectives that must respond to the environment and complete complex tasks. Discrete collective consensus achievement strategies have only recently been considered in the field of Human-Swarm Interaction, which has largely focused on enabling humans to control artificial swarms. Typically, swarms are comprised of agents that interact according to a protocol that causes a desired emergent behavior, such as flocking. Most Human-Swarm Interaction research assumes the human has near-perfect knowledge of the swarm and global communication with the swarm's agents. Robotic collectives have the potential to share decision making functions with humans; however, methods of human interaction with collective discrete consensus strategies have not been designed or evaluated. This dissertation develops a new algorithm influenced by biologically inspired discrete consensus achievement strategies in order to enable robotic collectives to choose and implement the best actions, despite the presence of environmental bias. The new model enables future human-collective teams to make decisions when the human does not have perfect global knowledge. Further, human-collective interaction mechanisms are developed in order to facilitate collaborative decisions between a human and a simulated robotic collective. The robotic collective model is implemented and evaluated for its ability to act independently and as a part of a human-collective team in trials featuring the human supervision of multiple targeting collectives. Advisors/Committee Members: Julie A. Adams (chair), Yevgeniy Vorobeychik (committee member), Maithilee Kunda (committee member), Jennifer S. Trueblood (committee member), Alexander S. Mentis (committee member).

Subjects/Keywords: multi-agent systems; swarm intelligence; collective decision making; human-swarm interaction

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APA (6^th Edition):

Cody, J. R. (2018). Discrete Consensus Decisions in Human-Collective Teams. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-04032018-164817/ ;

Chicago Manual of Style (16^th Edition):

Cody, Jason Robert. “Discrete Consensus Decisions in Human-Collective Teams.” 2018. Doctoral Dissertation, Vanderbilt University. Accessed December 11, 2019. http://etd.library.vanderbilt.edu/available/etd-04032018-164817/ ;.

MLA Handbook (7^th Edition):

Cody, Jason Robert. “Discrete Consensus Decisions in Human-Collective Teams.” 2018. Web. 11 Dec 2019.

Vancouver:

Cody JR. Discrete Consensus Decisions in Human-Collective Teams. [Internet] [Doctoral dissertation]. Vanderbilt University; 2018. [cited 2019 Dec 11]. Available from: http://etd.library.vanderbilt.edu/available/etd-04032018-164817/ ;.

Council of Science Editors:

Cody JR. Discrete Consensus Decisions in Human-Collective Teams. [Doctoral Dissertation]. Vanderbilt University; 2018. Available from: http://etd.library.vanderbilt.edu/available/etd-04032018-164817/ ;

The Ohio State University

8. Gazi, Veysel. Stability analysis of swarms.

Degree: PhD, Graduate School, 2002, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482

Subjects/Keywords: Engineering; Swarm intelligence

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APA (6^th 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 (16^th Edition):

Gazi, Veysel. “Stability analysis of swarms.” 2002. Doctoral Dissertation, The Ohio State University. Accessed December 11, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482.

MLA Handbook (7^th Edition):

Gazi, Veysel. “Stability analysis of swarms.” 2002. Web. 11 Dec 2019.

Vancouver:

Gazi V. Stability analysis of swarms. [Internet] [Doctoral dissertation]. The Ohio State University; 2002. [cited 2019 Dec 11]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482.

Council of Science Editors:

Gazi V. Stability analysis of swarms. [Doctoral Dissertation]. The Ohio State University; 2002. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1486463321623482

Univerzitet u Beogradu

9. Bačanin-Džakula, Nebojša V. , 1983-. Unapređenje hibridizacijom metaheuristika inteligencije rojeva za rešavanje problema globalne optimizacije.

Degree: Matematički fakultet, 2016, Univerzitet u Beogradu

URL: https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get

►

Računarstvo - Veštačka inteligencija / Computer Science - Artificial Intelligence

Te²ki optimizacioni problemi, nere²ivi u prihvatljivom vremenu izvr²avanja deterministi £kim matemati£kim metodama, uspe²no se poslednjih… (more)

▼

Računarstvo - Veštačka inteligencija / Computer Science - Artificial Intelligence

Te²ki optimizacioni problemi, nere²ivi u prihvatljivom vremenu izvr²avanja deterministi £kim matemati£kim metodama, uspe²no se poslednjih godina re²avaju populacionim stohasti£kim metaheuristikama, meu kojima istaknutu klasu predstavljaju algoritmi inteligencije rojeva. U ovom radu razmatra se unapreenje metaheuristika inteligencije rojeva pomo¢u hibridizacije. Analizom postoje¢ih metaheuristika u odreenim slu£ajevima uo£eni su nedostaci i slabosti u mehanizmima pretrage prostora re²enja koji pre svega proisti£u iz samog matemati£kog modela kojim se simulira proces iz prirode kao i iz nedovoljno usklaenog balansa izmeu intenzikacije i diversikacije. U radu je ispitivano da li se postoje¢i algoritmi inteligencije rojeva za globalnu optimizaciju mogu unaprediti (u smislu dobijanja boljih rezultata, brºe konvergencije, ve¢e robustnosti) hibridizacijom sa drugim algoritmima. Razvijeno je i implementirano vi²e hibridizovanih metaheuristika inteligencije rojeva. S obzirom da dobri hibridi ne nastaju slu£ajnom kombinacijom pojedinih funkcionalnih elemenata i procedura razli£itih algoritama, ve¢ su oni utemeljeni na sveobuhvatnom izu£avanju na£ina na koji algoritmi koji se hibridizuju funkcioni²u, kreiranju hibridnih pristupa prethodila je detaljna analiza prednosti i nedostataka posmatranih algoritma kako bi se napravila najbolja kombinacija koja nedostatke jednih neutrali²e prednostima drugih pristupa. Razvijeni hibridni algoritmi verikovani su testiranjima na standardnim skupovima test funkcija za globalnu optimizaciju sa ograni£enjima i bez ograni£enja, kao i na poznatim prakti£nim problemima. Uporeivanjem sa najboljim poznatim algoritmima iz literature pokazan je kvalitet razvijenih hibrida, £ime je potvrena i osnovna hipoteza ovog rada da se algoritmi inteligencije rojeva mogu uspe²no unaprediti hibridizacijom.

Advisors/Committee Members: Tuba, Milan, 1952-.

Subjects/Keywords: swarm intelligence metaheuristics; hybrid algorithms; global optimization

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APA (6^th 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 (16^th Edition):

Bačanin-Džakula, Nebojša V. , 1983-. “Unapređenje hibridizacijom metaheuristika inteligencije rojeva za rešavanje problema globalne optimizacije.” 2016. Thesis, Univerzitet u Beogradu. Accessed December 11, 2019. https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Bačanin-Džakula, Nebojša V. , 1983-. “Unapređenje hibridizacijom metaheuristika inteligencije rojeva za rešavanje problema globalne optimizacije.” 2016. Web. 11 Dec 2019.

Vancouver:

Bačanin-Džakula, Nebojša V. 1. Unapređenje hibridizacijom metaheuristika inteligencije rojeva za rešavanje problema globalne optimizacije. [Internet] [Thesis]. Univerzitet u Beogradu; 2016. [cited 2019 Dec 11]. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bačanin-Džakula, Nebojša V. 1. Unapređenje hibridizacijom metaheuristika inteligencije rojeva za rešavanje problema globalne optimizacije. [Thesis]. Univerzitet u Beogradu; 2016. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:13147/bdef:Content/get

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University

10. Potanapalli, Kranti Kumar. Coactive learning for multi-robot search and coverage.

Degree: MS, Computer Science, 2013, Oregon State University

URL: http://hdl.handle.net/1957/45107

► We investigate a search and coverage planning problem, where an area of interest has to be explored by a number of vehicles, given a fixed… (more)

Subjects/Keywords: Coactive Learning; Swarm intelligence – Mathematical models

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Potanapalli, Kranti Kumar. “Coactive learning for multi-robot search and coverage.” 2013. Masters Thesis, Oregon State University. Accessed December 11, 2019. http://hdl.handle.net/1957/45107.

MLA Handbook (7^th Edition):

Potanapalli, Kranti Kumar. “Coactive learning for multi-robot search and coverage.” 2013. Web. 11 Dec 2019.

Vancouver:

Potanapalli KK. Coactive learning for multi-robot search and coverage. [Internet] [Masters thesis]. Oregon State University; 2013. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/1957/45107.

Council of Science Editors:

Potanapalli KK. Coactive learning for multi-robot search and coverage. [Masters Thesis]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/45107

11. Sarker, Md Omar Faruque. Self-regulated multi-robot task allocation.

Degree: PhD, 2010, University of South Wales

URL: https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749689

► To deploy a large group of autonomous robots in dynamic multi-tasking environments, a suitable multi-robot task-allocation (MRTA) solution is required. This must be scalable to… (more)

▼ To deploy a large group of autonomous robots in dynamic multi-tasking environments, a suitable multi-robot task-allocation (MRTA) solution is required. This must be scalable to variable number of robots and tasks. Recent studies show that biology-inspired self-organized approaches can effectively handle task-allocation in large multi-robot systems. However most existing MRTA approaches have overlooked the role of different communication and sensing strategies found in selfregulated biological societies. This dissertation proposes to solve the MRTA problem using a set of previously published generic rules for division of labour derived from the observation of ant,human and robotic social systems. The concrete form of these rules, the attractive field model (AFM), provides sufficient abstraction to local communication and sensing which is uncommon in existing MRTA solutions. This dissertation validates the effectiveness of AFM to address MRTA using two bio-inspired communication and sensing strategies: "global sensing - no communication" and "local sensing - local communication". The former is realized using a centralized communication system and the latter is emulated under a peer-topeer local communication scheme. They are applied in a manufacturing shop-floor scenario using 16 e-puck robots. A robotic interpretation of AFM is presented that maps the generic parameters of AFM to the properties of a manufacturing shopfloor. A flexible multi-robot control architecture, hybrid event-driven architecture on D-Bus, has been outlined which uses the state-of-the-art D-Bus interprocess communication to integrate heterogeneous software components. Based-on the organization of task-allocation, communication and interaction among robots, a novel taxonomy of MRTA solutions has been proposed to remove the ambiguities found in existing MRTA solutions. Besides, a set of domainindependent metrics, e.g., plasticity, task-specialization and energy usage, has been formalized to compare the performances of the above two strategies. The presented comparisons extend our general understanding of the role of information exchange strategies to achieve the distributed task-allocations among various social groups.

Subjects/Keywords: Swarm intelligence; Robotics; Attractive field model (AFM)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sarker, M. O. F. (2010). Self-regulated multi-robot task allocation. (Doctoral Dissertation). University of South Wales. Retrieved from https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749689

Chicago Manual of Style (16^th Edition):

Sarker, Md Omar Faruque. “Self-regulated multi-robot task allocation.” 2010. Doctoral Dissertation, University of South Wales. Accessed December 11, 2019. https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749689.

MLA Handbook (7^th Edition):

Sarker, Md Omar Faruque. “Self-regulated multi-robot task allocation.” 2010. Web. 11 Dec 2019.

Vancouver:

Sarker MOF. Self-regulated multi-robot task allocation. [Internet] [Doctoral dissertation]. University of South Wales; 2010. [cited 2019 Dec 11]. Available from: https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749689.

Council of Science Editors:

Sarker MOF. Self-regulated multi-robot task allocation. [Doctoral Dissertation]. University of South Wales; 2010. Available from: https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749689

Brno University of Technology

12. Winklerová, Zdenka. Inteligence skupiny .

Degree: 2015, Brno University of Technology

URL: http://hdl.handle.net/11012/63276

► Záměrem disertační práce je aplikovaný výzkum skupinové ( kolektivní ) inteligence . K prokázání použitelnosti inteligence skupiny je zkoumán algoritmus na bázi roje částic (… (more)

▼ Záměrem disertační práce je aplikovaný výzkum skupinové ( kolektivní ) inteligence . K prokázání použitelnosti inteligence skupiny je zkoumán algoritmus na bázi roje částic ( Particle Swarm Optimization PSO ), v němž je problém inteligence skupiny převeden na matematickou optimalizaci, kdy roj částic ( particle swarm ) hledá globální optimum ve vymezeném prostoru problému a prohledávání je řízeno podle předem nadefinované účelové funkce ( objective function ), která zastupuje řešený problém. Byla navržena a experimentálně ověřena strategie prohledávání, v níž částice průběžně přizpůsobují své chování charakteristikám prostoru řešeného problému, a bylo experimentálně zjištěno, jak se vliv řídící účelové funkce zastupující řešený problém projevuje v chování částic. Výsledky experimentování s navrženou strategií prohledávání byly porovnány s výsledky experimentů s referenční verzí algoritmu PSO . Experimenty ukázaly, že klasické prohledávání, kde jedinou podmínkou je stabilní trajektorie, po níž se částice pohybuje v prostoru řešeného problému, a kde je ve výsledku eliminován vliv řídící účelové funkce, může selhat a že dynamická stabilita trajektorií částic sama o sobě není ukazatelem prohledávacích schopností algoritmu ani konvergence algoritmu ke správnému, globálnímu řešení. Byl navržen způsob prohledávání prostoru řešeného problému, v němž algoritmus PSO reguluje stabilitu algoritmu průběžným přizpůsobováním chování částic charakteristikám prostoru problému. Navržený algoritmus usměrňoval vývoj prohledávání prostoru problému tak, že vzrostla pravděpodobnost úspěšnosti řešení.; The intention of the dissertation is the applied research of the collective ( group ) ( swarm ) intelligence . To demonstrate the applicability of the collective intelligence, the Particle Swarm Optimization ( PSO ) algorithm has been studied in which the problem of the collective intelligence is transferred to mathematical optimization in which the particle swarm searches for a global optimum within the defined problem space, and the searching is controlled according to the pre-defined objective function which represents the solved problem. A new search strategy has been designed and experimentally tested in which the particles continuously adjust their behaviour according to the characteristics of the problem space, and it has been experimentally discovered how the impact of the objective function representing a solved problem manifests itself in the behaviour of the particles. The results of the experiments with the proposed search strategy have been compared to the results of the experiments with the reference version of the PSO algorithm. Experiments have shown that the classical reference solution, where the only condition is a stable trajectory along which the particle moves in the problem space, and where the influence of a control objective function is ultimately eliminated, may fail, and that the dynamic stability of the trajectory of the particle itself is not an indicator of the searching ability nor the convergence of the… Advisors/Committee Members: Zbořil, František (advisor).

Subjects/Keywords: Kolektivní inteligence; inteligence skupiny; rojová inteligence; optimalizace rojem částic; Collective intelligence; group intelligence; swarm intelligence; particle swarm optimization.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Winklerová, Z. (2015). Inteligence skupiny . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/63276

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Winklerová, Zdenka. “Inteligence skupiny .” 2015. Thesis, Brno University of Technology. Accessed December 11, 2019. http://hdl.handle.net/11012/63276.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Winklerová, Zdenka. “Inteligence skupiny .” 2015. Web. 11 Dec 2019.

Vancouver:

Winklerová Z. Inteligence skupiny . [Internet] [Thesis]. Brno University of Technology; 2015. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/11012/63276.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Winklerová Z. Inteligence skupiny . [Thesis]. Brno University of Technology; 2015. Available from: http://hdl.handle.net/11012/63276

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

13. Kelman, Alexander. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games.

Degree: Informatics, 2017, University of Skövde

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636

► The Ant Colony Optimization and Particle Swarm Optimization are two Swarm Intelligence algorithms often utilized for optimization. Swarm Intelligence relies on agents that possess… (more)

Subjects/Keywords: Swarm Intelligence; Pathfinding; Ant Colony Optimization; Particle Swarm Optimization; A*; Computer Sciences; Datavetenskap (datalogi)

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APA (6^th 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 (16^th Edition):

Kelman, Alexander. “Utilizing Swarm Intelligence Algorithms for Pathfinding in Games.” 2017. Thesis, University of Skövde. Accessed December 11, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Kelman, Alexander. “Utilizing Swarm Intelligence Algorithms for Pathfinding in Games.” 2017. Web. 11 Dec 2019.

Vancouver:

Kelman A. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. [Internet] [Thesis]. University of Skövde; 2017. [cited 2019 Dec 11]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kelman A. Utilizing Swarm Intelligence Algorithms for Pathfinding in Games. [Thesis]. University of Skövde; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

14. Magg, Sven. Self-Organised Task Differentiation in Homogeneous and Heterogeneous Groups of Autonomous Agents .

Degree: 2012, University of Hertfordshire

URL: http://hdl.handle.net/2299/9038

► The field of swarm robotics has been growing fast over the last few years. Using a swarm of simple and cheap robots has advantages in… (more)

▼ The field of swarm robotics has been growing fast over the last few years. Using a swarm of simple and cheap robots has advantages in various tasks. Apart from performance gains on tasks that allow for parallel execution, simple robots can also be smaller, enabling them to reach areas that can not be accessed by a larger, more complex robot. Their ability to cooperate means they can execute complex tasks while offering self-organised adaptation to changing environments and robustness due to redundancy. In order to keep individual robots simple, a control algorithm has to keep expensive communication to a minimum and has to be able to act on little information to keep the amount of sensors down. The number of sensors and actuators can be reduced even more when necessary capabilities are spread out over different agents that then combine them by cooperating. Self-organised differentiation within these heterogeneous groups has to take the individual abilities of agents into account to improve group performance. In this thesis it is shown that a homogeneous group of versatile agents can not be easily replaced by a heterogeneous group, by separating the abilities of the versatile agents into several specialists. It is shown that no composition of those specialists produces the same outcome as a homogeneous group on a clustering task. In the second part of this work, an adaptation mechanism for a group of foragers introduced by Labella et al. (2004) is analysed in more detail. It does not require communication and needs only the information on individual success or failure. The algorithm leads to self-organised regulation of group activity depending on object availability in the environment by adjusting resting times in a base. A possible variation of this algorithm is introduced which replaces the probabilistic mechanism with which agents determine to leave the base. It is demonstrated that a direct calculation of the resting times does not lead to differences in terms of differentiation and speed of adaptation. After investigating effects of different parameters on the system, it is shown that there is no efficiency increase in static environments with constant object density when using a homogeneous group of agents. Efficiency gains can nevertheless be achieved in dynamic environments. The algorithm was also reported to lead to higher activity of agents which have higher performance. It is shown that this leads to efficiency gains in heterogeneous groups in static and dynamic environments.

Subjects/Keywords: self-organisation; differentiation; talk allocation; adaptive behaviour; swarm intelligence; multi-agent-systems; swarm robotics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Magg, S. (2012). Self-Organised Task Differentiation in Homogeneous and Heterogeneous Groups of Autonomous Agents . (Thesis). University of Hertfordshire. Retrieved from http://hdl.handle.net/2299/9038

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Magg, Sven. “Self-Organised Task Differentiation in Homogeneous and Heterogeneous Groups of Autonomous Agents .” 2012. Thesis, University of Hertfordshire. Accessed December 11, 2019. http://hdl.handle.net/2299/9038.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Magg, Sven. “Self-Organised Task Differentiation in Homogeneous and Heterogeneous Groups of Autonomous Agents .” 2012. Web. 11 Dec 2019.

Vancouver:

Magg S. Self-Organised Task Differentiation in Homogeneous and Heterogeneous Groups of Autonomous Agents . [Internet] [Thesis]. University of Hertfordshire; 2012. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/2299/9038.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Magg S. Self-Organised Task Differentiation in Homogeneous and Heterogeneous Groups of Autonomous Agents . [Thesis]. University of Hertfordshire; 2012. Available from: http://hdl.handle.net/2299/9038

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Georgia

15. Oruganti Venkata, Sanjay Sarma. A swarm engineering framework for microtubule self-organization.

Degree: MS, Engineering, 2017, University of Georgia

URL: http://hdl.handle.net/10724/37465

► Microtubules are highly dynamic polymers distributed in the cytoplasm of a biological cell. Alpha and beta tubulins combine to form these tubules through polymerization, controlled… (more)

Subjects/Keywords: Microtubules; Microtubule Associated Proteins; Self-Organization; Swarm Engineering; Swarm Intelligence; Game Engine; Protofilaments

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APA (6^th Edition):

Oruganti Venkata, S. S. (2017). A swarm engineering framework for microtubule self-organization. (Masters Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37465

Chicago Manual of Style (16^th Edition):

Oruganti Venkata, Sanjay Sarma. “A swarm engineering framework for microtubule self-organization.” 2017. Masters Thesis, University of Georgia. Accessed December 11, 2019. http://hdl.handle.net/10724/37465.

MLA Handbook (7^th Edition):

Oruganti Venkata, Sanjay Sarma. “A swarm engineering framework for microtubule self-organization.” 2017. Web. 11 Dec 2019.

Vancouver:

Oruganti Venkata SS. A swarm engineering framework for microtubule self-organization. [Internet] [Masters thesis]. University of Georgia; 2017. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/10724/37465.

Council of Science Editors:

Oruganti Venkata SS. A swarm engineering framework for microtubule self-organization. [Masters Thesis]. University of Georgia; 2017. Available from: http://hdl.handle.net/10724/37465

De Montfort University

16. Al-Obaidi, Mohanad. ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence.

Degree: PhD, 2010, De Montfort University

URL: http://hdl.handle.net/2086/5187

► Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm… (more)

▼ Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military. These application domains have a number of characteristics that challenge the algorithmic design of WSNs. Firstly, mobility has a negative effect on the quality of the wireless communication and the performance of networking protocols. Nevertheless, it has been shown that mobility can enhance the functionality of the network by exploiting the movement patterns of mobile objects. Secondly, the heterogeneity of devices in a WSN has to be taken into account for increasing the network performance and lifetime. Thirdly, the WSN services should ideally assist the user in an unobtrusive and transparent way. Fourthly, energy-efficiency and scalability are of primary importance to prevent the network performance degradation. This thesis contributes toward the design of a new hybrid optimization algorithm; ENAMS (Energy optimizatioN Algorithm for Mobile Sensor networks) which is based on the Evolutionary Computation and Swarm Intelligence to increase the life time of mobile wireless sensor networks. The presented algorithm is suitable for large scale mobile sensor networks and provides a robust and energy- efficient communication mechanism by dividing the sensor-nodes into clusters, where the number of clusters is not predefined and the sensors within each cluster are not necessary to be distributed in the same density. The presented algorithm enables the sensor nodes to move as swarms within the search space while keeping optimum distances between the sensors. To verify the objectives of the proposed algorithm, the LEGO-NXT MIND-STORMS robots are used to act as particles in a moving swarm keeping the optimum distances while tracking each other within the permitted distance range in the search space.

Subjects/Keywords: 600; genetic algorithms; clustering; evolutionary computation; sensor networks; swarm intelligence; energy optimization; particle swarm optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Al-Obaidi, M. (2010). ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence. (Doctoral Dissertation). De Montfort University. Retrieved from http://hdl.handle.net/2086/5187

Chicago Manual of Style (16^th Edition):

Al-Obaidi, Mohanad. “ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence.” 2010. Doctoral Dissertation, De Montfort University. Accessed December 11, 2019. http://hdl.handle.net/2086/5187.

MLA Handbook (7^th Edition):

Al-Obaidi, Mohanad. “ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence.” 2010. Web. 11 Dec 2019.

Vancouver:

Al-Obaidi M. ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence. [Internet] [Doctoral dissertation]. De Montfort University; 2010. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/2086/5187.

Council of Science Editors:

Al-Obaidi M. ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence. [Doctoral Dissertation]. De Montfort University; 2010. Available from: http://hdl.handle.net/2086/5187

University of New Mexico

17. Lu, Qi. An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms.

Degree: Department of Computer Science, 2019, University of New Mexico

URL: https://digitalrepository.unm.edu/cs_etds/100

► Searching and collecting multiple resources from large unmapped environments is an important challenge. It is particularly difficult given limited time, a large search area… (more)

▼ Searching and collecting multiple resources from large unmapped environments is an important challenge. It is particularly difficult given limited time, a large search area and incomplete data about the environment. This search task is an abstraction of many real-world applications such as search and rescue, hazardous material clean-up, and space exploration. The collective foraging behavior of robot swarms is an effective approach for this task. In our work, individual robots have limited sensing and communication range (like ants), but they are organized and work together to complete foraging tasks collectively. An efficient foraging algorithm coordinates robots to search and collect as many resources as possible in the least amount of time. In the foraging algorithms we study, robots act independently with little or no central control. As the swarm size and arena size increase (e.g., thousands of robots searching over the surface of Mars or ocean), the foraging performance per robot decreases. Generally, larger robot swarms produce more inter-robot collisions, and in swarm robot foraging, larger search arenas result in larger travel distances causing the phenomenon of diminishing returns. The foraging performance per robot (measured as a number of collected resources per unit time) is sublinear with the arena size and the swarm size. Our goal is to design a scale-invariant foraging robot swarm. In other words, the foraging performance per robot should be nearly constant as the arena size and the swarm size increase. We address these problems with the Multiple-Place Foraging Algorithm (MPFA), which uses multiple collection zones distributed throughout the search area. Robots start from randomly assigned home collection zones but always return to the closest collection zones with found resources. We simulate the foraging behavior of robot swarms in the robot simulator ARGoS and employ a Genetic Algorithm (GA) to discover different optimized foraging strategies as swarm sizes and the number of resources is scaled up. In our experiments, the MPFA always produces higher foraging rates, fewer collisions, and lower travel and search time than the Central-Place Foraging Algorithm (CPFA). To make the MPFA more adaptable, we introduce dynamic depots that move to the centroid of recently collected resources, minimizing transport times when resources are clustered in heterogeneous distributions. Finally, we extend the MPFA with a bio-inspired hierarchical branching transportation network. We demonstrate a scale-invariant swarm foraging algorithm that ensures that each robot finds and delivers resources to a central collection zone at the same rate, regardless of the size of the swarm or the search area. Dispersed mobile depots aggregate locally foraged resources and transport them to a central place via a hierarchical branching transportation network. This approach is inspired by ubiquitous fractal branching networks such as animal cardiovascular networks that deliver resources to cells and determine… Advisors/Committee Members: Melanie E. Moses, Carlo Pinciroli, Stephanie Forrest, Joshua P. Hecker.

Subjects/Keywords: Swarm Robotics; Swarm Intelligence; Bio-Inspired Robot Swarm; Autonomous Robot; Foraging Robots; Multi-agent Systems; Robotics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Lu, Qi. “An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms.” 2019. Doctoral Dissertation, University of New Mexico. Accessed December 11, 2019. https://digitalrepository.unm.edu/cs_etds/100.

MLA Handbook (7^th Edition):

Lu, Qi. “An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms.” 2019. Web. 11 Dec 2019.

Vancouver:

Lu Q. An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms. [Internet] [Doctoral dissertation]. University of New Mexico; 2019. [cited 2019 Dec 11]. Available from: https://digitalrepository.unm.edu/cs_etds/100.

Council of Science Editors:

Lu Q. An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms. [Doctoral Dissertation]. University of New Mexico; 2019. Available from: https://digitalrepository.unm.edu/cs_etds/100

University of Pretoria

18. [No author]. Particle swarm optimisation in dynamically changing environments - an empirical study .

Degree: 2012, University of Pretoria

URL: http://upetd.up.ac.za/thesis/available/etd-06262012-124432/

► Real-world optimisation problems often are of a dynamic nature. Recently, much research has been done to apply particle swarm optimisation (PSO) to dynamic environments (DE).… (more)

Subjects/Keywords: Atomic pso; Charged pso; Self-adapting multi-swarm; Re-evaluating pso; Particle swarm optimisation (pso); Dynamically changing environment; Quantum swarm optimisation; Reinitialising pso; Computational intelligence; Multi-swarm; UCTD

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

author], [No. “Particle swarm optimisation in dynamically changing environments - an empirical study .” 2012. Masters Thesis, University of Pretoria. Accessed December 11, 2019. http://upetd.up.ac.za/thesis/available/etd-06262012-124432/.

MLA Handbook (7^th Edition):

author], [No. “Particle swarm optimisation in dynamically changing environments - an empirical study .” 2012. Web. 11 Dec 2019.

Vancouver:

author] [. Particle swarm optimisation in dynamically changing environments - an empirical study . [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2019 Dec 11]. Available from: http://upetd.up.ac.za/thesis/available/etd-06262012-124432/.

Council of Science Editors:

author] [. Particle swarm optimisation in dynamically changing environments - an empirical study . [Masters Thesis]. University of Pretoria; 2012. Available from: http://upetd.up.ac.za/thesis/available/etd-06262012-124432/

University of Pretoria

19. Duhain, Julien Georges Omer Louis. Particle swarm optimisation in dynamically changing environments - an empirical study.

Degree: Computer Science, 2012, University of Pretoria

URL: http://hdl.handle.net/2263/25875

► Real-world optimisation problems often are of a dynamic nature. Recently, much research has been done to apply particle swarm optimisation (PSO) to dynamic environments (DE).… (more)

Subjects/Keywords: Atomic PSO; Charged PSO; Self-adapting multi-swarm; Re-evaluating PSO; Particle swarm optimisation (PSO); Dynamically changing environment; Quantum swarm optimisation; Reinitialising PSO; Computational intelligence; Multi-swarm; UCTD

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Duhain, Julien Georges Omer. “Particle swarm optimisation in dynamically changing environments - an empirical study.” 2012. Masters Thesis, University of Pretoria. Accessed December 11, 2019. http://hdl.handle.net/2263/25875.

MLA Handbook (7^th Edition):

Duhain, Julien Georges Omer. “Particle swarm optimisation in dynamically changing environments - an empirical study.” 2012. Web. 11 Dec 2019.

Vancouver:

Duhain JGO. Particle swarm optimisation in dynamically changing environments - an empirical study. [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/2263/25875.

Council of Science Editors:

Duhain JGO. Particle swarm optimisation in dynamically changing environments - an empirical study. [Masters Thesis]. University of Pretoria; 2012. Available from: http://hdl.handle.net/2263/25875

University of Saskatchewan

20. Guha, Tapashree. Investigation of service selection algorithms for grid services.

Degree: 2009, University of Saskatchewan

URL: http://hdl.handle.net/10388/etd-09112009-201250

► Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also… (more)

▼ Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters. Advisors/Committee Members: Ludwig, Simone, Wu, Fang Xiang, McQuillan, Ian, Keil, Mark.

Subjects/Keywords: Evolutionary Algorithms; Grid Computing; Service Selection; Swarm Intelligence; Artificial Intelligence; Assignment Problem

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APA (6^th Edition):

Guha, T. (2009). Investigation of service selection algorithms for grid services. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/etd-09112009-201250

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Guha, Tapashree. “Investigation of service selection algorithms for grid services.” 2009. Thesis, University of Saskatchewan. Accessed December 11, 2019. http://hdl.handle.net/10388/etd-09112009-201250.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Guha, Tapashree. “Investigation of service selection algorithms for grid services.” 2009. Web. 11 Dec 2019.

Vancouver:

Guha T. Investigation of service selection algorithms for grid services. [Internet] [Thesis]. University of Saskatchewan; 2009. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/10388/etd-09112009-201250.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Guha T. Investigation of service selection algorithms for grid services. [Thesis]. University of Saskatchewan; 2009. Available from: http://hdl.handle.net/10388/etd-09112009-201250

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

21. Sun, Yanxia. Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation.

Degree: Docteur es, Information Scientifique et Technique, 2011, Université Paris-Est

URL: http://www.theses.fr/2011PEST1049

►

Optimisation Swarm Particle (PSO) est basé sur une métaphore de l'interaction sociale […] en ajustant les trajectoires des vecteurs individuels, appelés «particules» conceptualisées comme des… (more)

▼

Optimisation Swarm Particle (PSO) est basé sur une métaphore de l'interaction sociale […] en ajustant les trajectoires des vecteurs individuels, appelés «particules» conceptualisées comme des points se déplaçant dans un espace multidimensionnel. Le poids aléatoire des paramètres de contrôle est utilisé pour provoquer les particules à aller stochastiquement vers une région ayant plus de succès dans un espace tridimensionnel. Les particules itératives ajustent leur vitesse et leur direction en fonction de leurs personnels et des meilleures positions dans l'essaim. PSO a été appliquée avec succès pour optimiser une large gamme de problèmes. Cependant, les algorithmes standard PSO sont facilement piégés dans les points locaux suboptimaux lorsqu'il est appliqué à des problèmes avec de nombreux extrema locaux ou avec des contraintes. Cette thèse présente plusieurs algorithmes / techniques pour améliorer la capacité de l'OPS recherche mondiale: 1) Deux nouveaux algorithmes chaotiques de particules essaim d'optimisation, d'avoir une chaotiques Hopfield Neural Network (HNN) la structure, sont proposées. L'utilisation d'un système chaotique pour déterminer les poids des particules aide des algorithmes OSP pour échapper à des extrema locaux et de trouver l'optimum global. 2) Pour les algorithmes existants OSP, la relation et l'influence compter que sur les composants correspondants dimensions de l'essaim de particules. Pour montrer la relation intérieure entre les différentes composantes d'une particule, les réseaux de neurones peuvent être utilisés pour modéliser les projections d'ordre du problème d'optimisation, et une optimisation des intérieurs entièrement connecté essaim de particules est proposé à cet effet. 3) En raison de la complexité des contraintes, une solution déterministe générale est souvent difficile à trouver. Par conséquent, une particule détendue contrainte optimisation par essaim algorithme est proposé. Cette méthode améliore la capacité de recherche de l'OSP. 4) Pour améliorer les performances de l'optimisation par essaim de particules, une méthode adaptative de particules essaim d'optimisation basée sur les tests d'hypothèses sont proposées. Cette méthode applique un test d'hypothèse pour déterminer si le piège des particules dans un minimum local ou non. 5) Afin de renforcer la capacité du MPSO de recherche globale, une approche adaptative multi-objectif l'optimisation par essaim de particules (MOPSO) est proposé. Les résultats de simulation et d'analyse confirment l'efficacité des algorithmes proposés / techniques par rapport à l'autre état d'algorithmes

Particle Swarm Optimisation (PSO) is based on a metaphor of social interaction such as birds flocking or fish schooling to search a space by adjusting the trajectories of individual vectors, called “particles” conceptualized as moving points in a multidimensional space. The random weights of the control parameters are used to cause the particles to stochastically move towards a successful region in a higher dimensional space. Particles iteratively…

Advisors/Committee Members: Siarry, Patrick (thesis director).

Subjects/Keywords: Optimisation par essaim de particules; Le chaos; Intelligence; Particle swarm optimization; Chaos; Intelligence

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APA (6^th 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 (16^th Edition):

Sun, Yanxia. “Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation.” 2011. Doctoral Dissertation, Université Paris-Est. Accessed December 11, 2019. http://www.theses.fr/2011PEST1049.

MLA Handbook (7^th Edition):

Sun, Yanxia. “Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation.” 2011. Web. 11 Dec 2019.

Vancouver:

Sun Y. Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation. [Internet] [Doctoral dissertation]. Université Paris-Est; 2011. [cited 2019 Dec 11]. Available from: http://www.theses.fr/2011PEST1049.

Council of Science Editors:

Sun Y. Improved particle Swarm Optimisation algorithms : Des algorithmes améliorés de particules Swarm Optimisation. [Doctoral Dissertation]. Université Paris-Est; 2011. Available from: http://www.theses.fr/2011PEST1049

22. Steyven, Andreas Siegfried Wilhelm. A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics.

Degree: PhD, 2017, Edinburgh Napier University

URL: http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125

► This thesis investigates several aspects of environment-driven adaptation in simulated evolutionary swarm robotics. It is centred around a specific algorithm for distributed embodied evolution called… (more)

Subjects/Keywords: 006.3; Computer science; swarm robotics; artificial intelligence; 006.3 Artificial intelligence; QA75 Electronic computers. Computer science

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APA (6^th 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 (16^th Edition):

Steyven, Andreas Siegfried Wilhelm. “A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics.” 2017. Doctoral Dissertation, Edinburgh Napier University. Accessed December 11, 2019. http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125.

MLA Handbook (7^th Edition):

Steyven, Andreas Siegfried Wilhelm. “A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics.” 2017. Web. 11 Dec 2019.

Vancouver:

Steyven ASW. A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics. [Internet] [Doctoral dissertation]. Edinburgh Napier University; 2017. [cited 2019 Dec 11]. Available from: http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125.

Council of Science Editors:

Steyven ASW. A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics. [Doctoral Dissertation]. Edinburgh Napier University; 2017. Available from: http://researchrepository.napier.ac.uk/Output/1253630 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.754125

Anna University

23. Nagarani, S. Swarm intelligence based dynamic subcarrier bit and power allocation for ofdma relay networks; -.

Degree: mathematics, 2013, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/27015

►

Orthogonal Frequency Division Multiplexing OFDM is a newlinepromising and a reliable solution for future wireless communication networks newlineOFDM provides a high performance physical layer and… (more)

▼

Orthogonal Frequency Division Multiplexing OFDM is a newlinepromising and a reliable solution for future wireless communication networks newlineOFDM provides a high performance physical layer and medium access newlinecontrol due to its capability to combat Inter Symbol Interference ISI and newlinemultipath fading Moreover multiuser diversity can be exploited by OFDM newlinewhich is inherent in the multiuser wireless network via dynamic subcarrier newlineallocation newlineThe toughest concern in OFDM system is the problem of subcarrier newlineand power allocation in a multiuser network to reduce the transmitted power newlineand maximize the total data rate or a utility function of data rate of users newlineResource allocation in OFDM has become one of the active areas newlineof research which refers to assigning subcarrier to consumer and choosing the newlinepower level and the modulation approach on the assigned subcarrier with the newlinegoal of satisfying individual consumer Quality of Service QS necessities newlineIn an Orthogonal Frequency Division Multiple Access OFDMA newlinerelay network with a single source a single relay and a single destination the newlineinstantaneous rate of the source is increased by power allocation approach newlineIn an OFDMA relay network with a single source multiple relays newlineand multiple destinations the sum rate of the relay network is increased by newlineheuristic subchannel allocation approach newlineIn an OFDMA relay network with multiple sources multiple newlinerelays and a single destination the sum rate per subcarrier is increased by newlinesubcarrier allocation approach newlineThe opening in relaying and OFDMA approach also provide certain newlineexciting challenges due to the increased dynamics degree of freedom and newlineresource reuse and difficulties present in resource allocation and interference newlinemanagement This reality emphasizes the significance and necessity of newlinedynamic and intelligent resource allocation approach with efficient spectrum newlineutilization The fundamental resources in OFDM are Subchannel and Power newlineSubchannels experience frequency selective fading which takes different newlinevalues for different users newline newline

-

Advisors/Committee Members: Seshaiah, C.

Subjects/Keywords: Bit; Dynamic Subcarrier; Ofdma; Power Allocation; Relay Networks; Swarm Intelligence

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APA (6^th Edition):

Nagarani, S. (2013). Swarm intelligence based dynamic subcarrier bit and power allocation for ofdma relay networks; -. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/27015

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Nagarani, S. “Swarm intelligence based dynamic subcarrier bit and power allocation for ofdma relay networks; -.” 2013. Thesis, Anna University. Accessed December 11, 2019. http://shodhganga.inflibnet.ac.in/handle/10603/27015.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Nagarani, S. “Swarm intelligence based dynamic subcarrier bit and power allocation for ofdma relay networks; -.” 2013. Web. 11 Dec 2019.

Vancouver:

Nagarani S. Swarm intelligence based dynamic subcarrier bit and power allocation for ofdma relay networks; -. [Internet] [Thesis]. Anna University; 2013. [cited 2019 Dec 11]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/27015.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nagarani S. Swarm intelligence based dynamic subcarrier bit and power allocation for ofdma relay networks; -. [Thesis]. Anna University; 2013. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/27015

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

24. Sudholt, Dirk. Computational complexity of evolutionary algorithms, hybridizations, and swarm intelligence.

Degree: 2008, Technische Universität Dortmund

URL: http://hdl.handle.net/2003/25954

► Bio-inspired randomized search heuristics such as evolutionary algorithms, hybridizations with local search, and swarm intelligence are very popular among practitioners as they can be applied… (more)

Subjects/Keywords: Computational complexity; evolutionary algorithms; hybridization; probabilistic analysis; swarm intelligence; theory; 004

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sudholt, D. (2008). Computational complexity of evolutionary algorithms, hybridizations, and swarm intelligence. (Thesis). Technische Universität Dortmund. Retrieved from http://hdl.handle.net/2003/25954

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sudholt, Dirk. “Computational complexity of evolutionary algorithms, hybridizations, and swarm intelligence.” 2008. Thesis, Technische Universität Dortmund. Accessed December 11, 2019. http://hdl.handle.net/2003/25954.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sudholt, Dirk. “Computational complexity of evolutionary algorithms, hybridizations, and swarm intelligence.” 2008. Web. 11 Dec 2019.

Vancouver:

Sudholt D. Computational complexity of evolutionary algorithms, hybridizations, and swarm intelligence. [Internet] [Thesis]. Technische Universität Dortmund; 2008. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/2003/25954.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sudholt D. Computational complexity of evolutionary algorithms, hybridizations, and swarm intelligence. [Thesis]. Technische Universität Dortmund; 2008. Available from: http://hdl.handle.net/2003/25954

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Pretoria

25. Rakitianskaia, A.S. (Anastassia Sergeevna). Using particle swarm optimisation to train feedforward neural networks in dynamic environments.

Degree: Computer Science, 2012, University of Pretoria

URL: http://hdl.handle.net/2263/28618

► The feedforward neural network (NN) is a mathematical model capable of representing any non-linear relationship between input and output data. It has been succesfully applied… (more)

Subjects/Keywords: Computational intelligence; Particle swarm optimization (PSO); Concept drift; Neural networks; UCTD

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APA (6^th Edition):

Rakitianskaia, A. S. (. (2012). Using particle swarm optimisation to train feedforward neural networks in dynamic environments. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/28618

Chicago Manual of Style (16^th Edition):

Rakitianskaia, A S (Anastassia. “Using particle swarm optimisation to train feedforward neural networks in dynamic environments.” 2012. Masters Thesis, University of Pretoria. Accessed December 11, 2019. http://hdl.handle.net/2263/28618.

MLA Handbook (7^th Edition):

Rakitianskaia, A S (Anastassia. “Using particle swarm optimisation to train feedforward neural networks in dynamic environments.” 2012. Web. 11 Dec 2019.

Vancouver:

Rakitianskaia AS(. Using particle swarm optimisation to train feedforward neural networks in dynamic environments. [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/2263/28618.

Council of Science Editors:

Rakitianskaia AS(. Using particle swarm optimisation to train feedforward neural networks in dynamic environments. [Masters Thesis]. University of Pretoria; 2012. Available from: http://hdl.handle.net/2263/28618

University of Pretoria

26. Neethling, Charles Marais. Using SetPSO to determine RNA secondary structure.

Degree: Computer Science, 2009, University of Pretoria

URL: http://hdl.handle.net/2263/29202

► RNA secondary structure prediction is an important field in Bioinformatics. A number of different approaches have been developed to simplify the determination of RNA molecule… (more)

Subjects/Keywords: Rna; Secondary structure; Setpso; Combinatorial; Computational intelligence; Particle swarm optimiser; UCTD

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APA (6^th Edition):

Neethling, C. (2009). Using SetPSO to determine RNA secondary structure. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/29202

Chicago Manual of Style (16^th Edition):

Neethling, Charles. “Using SetPSO to determine RNA secondary structure.” 2009. Masters Thesis, University of Pretoria. Accessed December 11, 2019. http://hdl.handle.net/2263/29202.

MLA Handbook (7^th Edition):

Neethling, Charles. “Using SetPSO to determine RNA secondary structure.” 2009. Web. 11 Dec 2019.

Vancouver:

Neethling C. Using SetPSO to determine RNA secondary structure. [Internet] [Masters thesis]. University of Pretoria; 2009. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/2263/29202.

Council of Science Editors:

Neethling C. Using SetPSO to determine RNA secondary structure. [Masters Thesis]. University of Pretoria; 2009. Available from: http://hdl.handle.net/2263/29202

University of Pretoria

27. Messerschmidt, Leon. Using particle swarm optimization to evolve two-player game agents.

Degree: Computer Science, 2007, University of Pretoria

URL: http://hdl.handle.net/2263/23980

► Computer game-playing agents are almost as old as computers themselves, and people have been developing agents since the 1950's. Unfortunately the techniques for game-playing agents… (more)

Subjects/Keywords: Swarm intelligence; Computer games; UCTD

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Messerschmidt, L. (2007). Using particle swarm optimization to evolve two-player game agents. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/23980

Chicago Manual of Style (16^th Edition):

Messerschmidt, Leon. “Using particle swarm optimization to evolve two-player game agents.” 2007. Masters Thesis, University of Pretoria. Accessed December 11, 2019. http://hdl.handle.net/2263/23980.

MLA Handbook (7^th Edition):

Messerschmidt, Leon. “Using particle swarm optimization to evolve two-player game agents.” 2007. Web. 11 Dec 2019.

Vancouver:

Messerschmidt L. Using particle swarm optimization to evolve two-player game agents. [Internet] [Masters thesis]. University of Pretoria; 2007. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/2263/23980.

Council of Science Editors:

Messerschmidt L. Using particle swarm optimization to evolve two-player game agents. [Masters Thesis]. University of Pretoria; 2007. Available from: http://hdl.handle.net/2263/23980

University of Toledo

28. Vaddhireddy, Jyothirmye. A Novel Swarm Intelligence based IWD Algorithm for Routing in MANETs.

Degree: MSin Electrical Engineering, Electrical Engineering, 2011, University of Toledo

URL: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321589580

► In this thesis a new routing protocol for Mobile Ad-Hoc Networks (MANETs) has been developed and simulated. The protocol is named IWDHocNet. With the… (more)

Subjects/Keywords: Electrical Engineering; MANETs; Intelligent Water Drops; Swarm Intelligence; Routing

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APA (6^th Edition):

Vaddhireddy, J. (2011). A Novel Swarm Intelligence based IWD Algorithm for Routing in MANETs. (Masters Thesis). University of Toledo. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321589580

Chicago Manual of Style (16^th Edition):

Vaddhireddy, Jyothirmye. “A Novel Swarm Intelligence based IWD Algorithm for Routing in MANETs.” 2011. Masters Thesis, University of Toledo. Accessed December 11, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321589580.

MLA Handbook (7^th Edition):

Vaddhireddy, Jyothirmye. “A Novel Swarm Intelligence based IWD Algorithm for Routing in MANETs.” 2011. Web. 11 Dec 2019.

Vancouver:

Vaddhireddy J. A Novel Swarm Intelligence based IWD Algorithm for Routing in MANETs. [Internet] [Masters thesis]. University of Toledo; 2011. [cited 2019 Dec 11]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321589580.

Council of Science Editors:

Vaddhireddy J. A Novel Swarm Intelligence based IWD Algorithm for Routing in MANETs. [Masters Thesis]. University of Toledo; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321589580

University of Pretoria

29. [No author]. Using particle swarm optimization to evolve two-player game agents .

Degree: 2007, University of Pretoria

URL: http://upetd.up.ac.za/thesis/available/etd-04172007-083117/

► Computer game-playing agents are almost as old as computers themselves, and people have been developing agents since the 1950's. Unfortunately the techniques for game-playing agents… (more)

Subjects/Keywords: Swarm intelligence; Computer games; UCTD

Record Details Similar Records Cite « Share

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APA (6^th 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 (16^th Edition):

author], [No. “Using particle swarm optimization to evolve two-player game agents .” 2007. Masters Thesis, University of Pretoria. Accessed December 11, 2019. http://upetd.up.ac.za/thesis/available/etd-04172007-083117/.

MLA Handbook (7^th Edition):

author], [No. “Using particle swarm optimization to evolve two-player game agents .” 2007. Web. 11 Dec 2019.

Vancouver:

author] [. Using particle swarm optimization to evolve two-player game agents . [Internet] [Masters thesis]. University of Pretoria; 2007. [cited 2019 Dec 11]. Available from: http://upetd.up.ac.za/thesis/available/etd-04172007-083117/.

Council of Science Editors:

author] [. Using particle swarm optimization to evolve two-player game agents . [Masters Thesis]. University of Pretoria; 2007. Available from: http://upetd.up.ac.za/thesis/available/etd-04172007-083117/

University of Pretoria

30. Rakitianskaia, A.S. (Anastassia Sergeevna). Using particle swarm optimisation to train feedforward neural networks in dynamic environments .

Degree: 2012, University of Pretoria

URL: http://upetd.up.ac.za/thesis/available/etd-02132012-233212/

► The feedforward neural network (NN) is a mathematical model capable of representing any non-linear relationship between input and output data. It has been succesfully applied… (more)

Subjects/Keywords: Computational intelligence; Particle swarm optimization (PSO); Concept drift; Neural networks; UCTD

Record Details Similar Records Cite « Share

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Rakitianskaia, A S (Anastassia. “Using particle swarm optimisation to train feedforward neural networks in dynamic environments .” 2012. Masters Thesis, University of Pretoria. Accessed December 11, 2019. http://upetd.up.ac.za/thesis/available/etd-02132012-233212/.

MLA Handbook (7^th Edition):

Rakitianskaia, A S (Anastassia. “Using particle swarm optimisation to train feedforward neural networks in dynamic environments .” 2012. Web. 11 Dec 2019.

Vancouver:

Rakitianskaia AS(. Using particle swarm optimisation to train feedforward neural networks in dynamic environments . [Internet] [Masters thesis]. University of Pretoria; 2012. [cited 2019 Dec 11]. Available from: http://upetd.up.ac.za/thesis/available/etd-02132012-233212/.

Council of Science Editors:

Rakitianskaia AS(. Using particle swarm optimisation to train feedforward neural networks in dynamic environments . [Masters Thesis]. University of Pretoria; 2012. Available from: http://upetd.up.ac.za/thesis/available/etd-02132012-233212/

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Open Access Theses and Dissertations