Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13272
Title: Fully learned multi-swarm particle swarm optimization
Authors: Niu, B
Huang, H
Ye, B
Tan, L
Liang, JJ
Keywords: Fully learned
Multi-swarm particle swarm optimization
Particle swarm optimizer (PSO)
Issue Date: 2014
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2014, v. 8794, p. 150-157 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: This paper presents a new variant of PSO, called fully learned multiswarm particle swarm optimization (FLMPSO) for global optimization. In FLMPSO, the whole population is divided into a number of sub-swarms, in which the learning probability is employed to influence the exemplar of each individual and the center position of the best experience found so far by all the sub-swarms is also used to balance exploration and exploitation. Each particle updates its velocity based on its own historical experience or others relying on the learning probability, and the center position is also applied to adjust its flying. The experimental study on a set of six test functions demonstrates that FLMPSO outperform the others in terms of the convergence efficiency and the accuracy.
URI: http://hdl.handle.net/10397/13272
ISSN: 0302-9743 (print)
1611-3349 (online)
Appears in Collections:Conference Paper

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