DocumentCode
507996
Title
Hybrid Ensemble Particle Swarm Optimization
Author
Yan, Shi
Author_Institution
Sch. of Comput. & Inf. Eng., Beijing Technol. & Bus. Univ., Beijing, China
Volume
3
fYear
2009
fDate
14-16 Aug. 2009
Firstpage
255
Lastpage
259
Abstract
In this paper a hybrid ensemble particle swarm optimization (HEPSO) algorithm is presented. It combines ensemble learning, subpopulation, part dimensions and random order strategies together. Ensemble learning can help providing a more accurate global guider through combining some previous best positions (pbest) of the particles. The other three strategies increase the diversity. And this algorithm is compared with standard PSO and some other improved PSO to illustrate how HPSO can benefit from these strategies.
Keywords
learning (artificial intelligence); particle swarm optimisation; diversity; ensemble learning; hybrid ensemble particle swarm optimization algorithm; part dimensions strategy; particle best position; random order strategy; subpopulation; Clamps; Diversity reception; Equations; Genetic mutations; Greedy algorithms; History; Particle swarm optimization; ensemble learning; hybrid strategies; particle swarm optimization;
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.2009.565
Filename
5364556
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