Parameter Selection of Quantum-Behaved Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a population-based evolutionary search technique, which has comparable performance with Genetic algorithm. The existing PSOs, however, are not global-convergence-guaranteed algorithms, because the evolution equation of PSO, make the particle only search in a fini...

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Bibliographic Details
Published inAdvances in Natural Computation pp. 543 - 552
Main Authors Sun, Jun, Xu, Wenbo, Liu, Jing
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Quantum computation
Genetic algorithm
Evolutionary algorithm
Stochastic simulation
Swarm intelligence
Neural network
Sampling
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Summary:Particle Swarm Optimization (PSO) is a population-based evolutionary search technique, which has comparable performance with Genetic algorithm. The existing PSOs, however, are not global-convergence-guaranteed algorithms, because the evolution equation of PSO, make the particle only search in a finite sampling space. In [10,11], a Quantum-behaved Particle Swarm Optimization algorithm is proposed that outperforms traditional PSOs in search ability as well as having less parameter. This paper focuses on discussing how to select parameter when QPSO is practically applied. After the QPSO algorithm is described, the experiment results of stochastic simulation are given to show how the selection of the parameter value influences the convergence of the particle in QPSO. Finally, two parameter control methods are presented and experiment results on the benchmark functions testify their efficiency.
ISBN:9783540283201
354028320X
3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539902_66