Orthogonal Learning Particle Swarm Optimization

Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood's best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when...

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Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 15; no. 6; pp. 832 - 847
Main Authors:	Zhan, Zhi-Hui, Zhang, Jun, Li, Yun, Shi, Yu-Hui
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.12.2011 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithm design and analysis Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Arrays Artificial intelligence Computer science; control theory; systems Convergence Exact sciences and technology Global optimization Optimization orthogonal experimental design (OED) orthogonal learning particle swarm optimization (OLPSO) Oscillators Particle swarm optimization particle swarm optimization (PSO) Search problems Studies swarm intelligence Theoretical computing orthogonal learning particle swarm optimization (OLPSO) Useful information Evolutionary algorithm Global optimum Information retrieval orthogonal experimental design (OED) Global solution Particle swarm optimization Topological structure Global optimization Efficiency Experimental design Swarm intelligence Robustness particle swarm optimization (PSO) Learning algorithm Artificial intelligence Summation
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
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