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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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 15; H. 6; S. 832 - 847
Hauptverfasser: Zhan, Zhi-Hui, Zhang, Jun, Li, Yun, Shi, Yu-Hui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.12.2011
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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 searching in complex problem spaces. Hence, designing learning strategies that can utilize previous search information (experience) more efficiently has become one of the most salient and active PSO research topics. In this paper, we proposes an orthogonal learning (OL) strategy for PSO to discover more useful information that lies in the above two experiences via orthogonal experimental design. We name this PSO as orthogonal learning particle swarm optimization (OLPSO). The OL strategy can guide particles to fly in better directions by constructing a much promising and efficient exemplar. The OL strategy can be applied to PSO with any topological structure. In this paper, it is applied to both global and local versions of PSO, yielding the OLPSO-G and OLPSO-L algorithms, respectively. This new learning strategy and the new algorithms are tested on a set of 16 benchmark functions, and are compared with other PSO algorithms and some state of the art evolutionary algorithms. The experimental results illustrate the effectiveness and efficiency of the proposed learning strategy and algorithms. The comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2010.2052054