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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Veröffentlicht in:CIS 2006 : 2006 International Conference on Computational Intelligence and Security : Guangzhou, China, November 3-6, 2006 : proceedings Jg. 1; S. 316 - 319
Hauptverfasser: Qingjian Ni, Hancheng Xing
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2006
Schlagworte:
Benchmark testing
Birds
Computer science
Educational institutions
Equations
Evolutionary computation
Genetic algorithms
Marine animals
Particle swarm optimization
Space exploration
ISBN:1424406048, 9781424406043
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Zusammenfassung: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