English, Article edition: ANALYSIS OF MUTATION OPERATORS ON QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION ALGORITHM WEI FANG; JUN SUN; WENBO XU

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Bookmark: http://trove.nla.gov.au/version/18286
Physical Description
  • article
Language
  • English

Edition details

Title
  • ANALYSIS OF MUTATION OPERATORS ON QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION ALGORITHM
Author
  • WEI FANG
  • JUN SUN
  • WENBO XU
Physical Description
  • article
Notes
  • Mutation operator is one of the mechanisms of evolutionary algorithms (EAs) and it can provide diversity in the search and help to explore the undiscovered search place. Quantum-behaved particle swarm optimization (QPSO), which is inspired by fundamental theory of PSO algorithm and quantum mechanics, is a novel stochastic searching technique and it may encounter local minima problem when solving multi-modal problems just as that in PSO. A novel mutation mechanism is proposed in this paper to enhance the global search ability of QPSO and a set of different mutation operators is introduced and implemented on the QPSO. Experiments are conducted on several well-known benchmark functions. Experimental results show that QPSO with some of the mutation operators is proven to be statistically significant better than the original QPSO.
  • Particle swarm optimization, mutation operator, global convergence
  • RePEc:wsi:nmncxx:v:05:y:2009:i:02:p:487-496
Language
  • English
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