DocumentCode :
508044
Title :
Compound Particle Optimization Using Speciation for Multimodal Function Optimization
Author :
Hu, KunYuan ; Zhu, Yunlong
Author_Institution :
Lab. of Ind. Inf., Shenyang Inst. of Autom. Chinese Acad. of Sci., Shenyang, China
Volume :
4
fYear :
2009
fDate :
14-16 Aug. 2009
Firstpage :
182
Lastpage :
186
Abstract :
Multimodal optimization problems pose a new challenge to evolutionary computation, since they usually not only require a search for one global optimum, but also simultaneously locating multiple optima. This paper presents a new variant of particle swarm optimization, which incorporates the notion of speciation into the compound particle optimization for solving multimodal functions. In the proposed species-based compound particle swarm optimization (SCPSO), several species containing compound particles are adaptively formed according to their similarity at each iteration step. The corresponding techniques of the compound particle, which are inspired by physics mechanisms, provides successive local improvements for each species to precisely and quickly identifying multiple global optima. Experiments on multimodal test functions suggest that SCPSO is more computationally efficient than the conventional species-based PSO.
Keywords :
particle swarm optimisation; evolutionary computation; multimodal function optimization; species-based compound particle swarm optimization; Automation; Computer industry; Design engineering; Design optimization; Evolutionary computation; Informatics; Particle swarm optimization; Physics; Space exploration; Testing; Particle swarm optimization; compound particles; multimodal optimization; species-based method;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
Type :
conf
DOI :
10.1109/ICNC.2009.446
Filename :
5364996
Link To Document :
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