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Title: Empirical study of an unconstrained modified particle swarm optimization
Author (s): Moore, P.W.
Venayagamoorthy, Ganesh K.
Department/Lab Affiliations: Electrical and Computer Engineering
Real-Time Power and Intelligent Systems Laboratory
Issue Date: 2006
Publisher: Institute of Electrical and Electronics Engineers
Citation: Moore, P.W.; Venayagamoorthy, G.K. "Empirical Study of an Unconstrained Modified Particle Swarm Optimization" CEC 2006. IEEE Congress on Evolutionary Computation, 2006. 16-21 July 2006 Pages: 1477- 1482
Abstract: In this paper, an unconstrained modified particle swarm optimization (UMPSO) algorithm is introduced and studied empirically. Four well known benchmark functions, with asymmetric initial position values, are used as testing functions for the UMPSO algorithm. The UMPSO is a variation of the canonical PSO in which the velocity and position is unconstrained, an additional strategic component is added, and the social component term has been modified. The strategy component is used instead of varying parameters or mutation to enhance diversity in the swarm during the search. The UMPSO algorithm is then compared to results obtained from the constrained canonical PSO (CPSO) and the unconstrained canonical PSO (UPSO). The results show that UMPSO algorithm with no maximum velocity and position, and no minimum velocity and position value that performs better than the CPSO and the UPSO for the Sphere, Rosenbrock, Rastrigrin, and Griewank benchmark functions.
Type: Article - Conference proceedings
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titleEmpirical study of an unconstrained modified particle swarm optimization
contributor.authorMoore, P.W.
contributor.authorVenayagamoorthy, Ganesh K.
contributor.deptlabElectrical and Computer Engineering
contributor.deptlabReal-Time Power and Intelligent Systems Laboratory
date.issued2006
date.submitted2007
publisherInstitute of Electrical and Electronics Engineers
identifier.citationMoore, P.W.; Venayagamoorthy, G.K. "Empirical Study of an Unconstrained Modified Particle Swarm Optimization" CEC 2006. IEEE Congress on Evolutionary Computation, 2006. 16-21 July 2006 Pages: 1477- 1482
identifier.pub.URI
http://ieeexplore.ieee.org/iel5/11108/35623/01688483.pdf?arnumber=168848
description.abstractIn this paper, an unconstrained modified particle swarm optimization (UMPSO) algorithm is introduced and studied empirically. Four well known benchmark functions, with asymmetric initial position values, are used as testing functions for the UMPSO algorithm. The UMPSO is a variation of the canonical PSO in which the velocity and position is unconstrained, an additional strategic component is added, and the social component term has been modified. The strategy component is used instead of varying parameters or mutation to enhance diversity in the swarm during the search. The UMPSO algorithm is then compared to results obtained from the constrained canonical PSO (CPSO) and the unconstrained canonical PSO (UPSO). The results show that UMPSO algorithm with no maximum velocity and position, and no minimum velocity and position value that performs better than the CPSO and the UPSO for the Sphere, Rosenbrock, Rastrigrin, and Griewank benchmark functions.
typeArticle - Conference proceedings
type.DCMITypetext
type.statusFinal version
rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
rights.URI
http://www.ieee.org/web/publications/rights/policies.html
date.accessioned2007-04-05T14:27:34Z
date.available2007-04-05T14:27:34Z
identifier.persist.URI
http://scholarsmine.mst.edu/post_prints/01688483_09007dcc8030dac9.html
Full Text
01688483_09007dcc8030dace.pdf