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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/19014
Title: Information sharing strategy among particles in particle swarm optimization using laplacian operator
Authors: Bansal J.C.
Deep, K.
Veeramachaneni K.
Osadciw L.
Published in: Proceedings of 2009 IEEE Swarm Intelligence Symposium, SIS 2009
Abstract: Particle Swarm Optimization (PSO) has been extensively used in recent years for the optimization of nonlinear optimization problems. Two of the most popular variants of PSO are PSO-W (PSO with inertia weight) and PSO-C (PSO with constriction factor). Typically particles in swarm use information from global best performing particle, gbest and their own personal best, pbest. Recently, studies have focused on incorporating influences of other particles other than gbest. In this paper, we develop a methodology to share information between two particles using a Laplacian operatordesigned from Laplace probability density function. The properties of this operator are analyzed. Two particles share their positional information in the search space and a new particle is formed. The particle, called as Laplacian particle,replaces the worst performing particle in the swarm. Using this new operator, this paper introduces two algorithms namely Laplace Crossover PSO with inertia weight (LXPSO-W) and Laplace Crossover PSO with constriction factor (LXPSO-C).The performance of the newly designed algorithms is evaluated with respect to PSO-W and PSO-C using 15 benchmark test problems. The empirical results show that the new approach improves performance measured in terms of efficiency, reliability and robustness. © 2009 IEEE.
Citation: Proceedings of 2009 IEEE Swarm Intelligence Symposium, SIS 2009, (2009), 30- 36. Nashville, TN
URI: https://doi.org/10.1109/SIS.2009.4937841
http://repository.iitr.ac.in/handle/123456789/19014
Issue Date: 2009
Keywords: A-Laplacian
Benchmark test problem
Constriction factor
Empirical results
Inertia weight
Information sharing strategies
Laplacian
Laplacian operator
New approaches
Non-linear optimization problems
Positional information
Reliability and robustness
Search spaces
Two particles
Cellular automata
Information use
Laplace equation
Laplace transforms
Probability density function
Surveying instruments
Switching circuits
Particle swarm optimization (PSO)
ISBN: 9.78E+12
Author Scopus IDs: 57189656835
8561208900
7801508939
6603145104
Author Affiliations: Bansal, J.C., Indian Institute of Technology, Roorkee, 247667, India
Deep, K., Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse NY, 13244, United States
Veeramachaneni, K., Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse NY, 13244, United States
Osadciw, L., Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse NY, 13244, United States
Corresponding Author: Bansal, J. C.; Indian Institute of Technology, Roorkee, 247667, India; email: jcbansal@gmail.com
Appears in Collections:Conference Publications [MA]

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