An Improved Gaussian Dynamic Particle Swarm Optimization Algorithm

An improved Gaussian dynamic particle swarm optimization (PSO) algorithm is proposed in this paper. In the proposed version of PSO, the original swarm of particles is initialized by canonical PSO. The time varying linear inertial weight is reintroduced to add to the position update formula. And the...

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Bibliographic Details
Published in:CIS 2006 : 2006 International Conference on Computational Intelligence and Security : Guangzhou, China, November 3-6, 2006 : proceedings Vol. 1; pp. 316 - 319
Main Authors: Qingjian Ni, Hancheng Xing
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2006
Subjects:
Benchmark testing
Birds
Computer science
Educational institutions
Equations
Evolutionary computation
Genetic algorithms
Marine animals
Particle swarm optimization
Space exploration
ISBN:1424406048, 9781424406043
Online Access:Get full text
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Summary:An improved Gaussian dynamic particle swarm optimization (PSO) algorithm is proposed in this paper. In the proposed version of PSO, the original swarm of particles is initialized by canonical PSO. The time varying linear inertial weight is reintroduced to add to the position update formula. And the crazinness variable is also used in order to maintain the diversity of particle swarms. The performance of improved Gaussian dynamic PSO is demonstrated by applying it to several benchmark functions and comparing to other variants of PSO
ISBN:1424406048
9781424406043
DOI:10.1109/ICCIAS.2006.294146