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Cooperative quantum-behaved particle swarm optimization with dynamic varying search areas and Lévy flight disturbance.

This paper proposes a novel variant of cooperative quantum-behaved particle swarm optimization (CQPSO) algorithm with two mechanisms to reduce the search space and avoid the stagnation, called CQPSO-DVSA-LFD. One mechanism is called Dynamic Varying Search Area (DVSA), which takes charge of limiting...

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Published in:Scientific World Journal pp. 370691 - 370692
Main Author: Li, Desheng
Format: research Journal Article
Published: Wiley-Blackwell 2014
Online Access:View this record in EBSCOhost
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      jtl: Scientific World Journal
      issn: 1537744X
      maglogo: N
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      dt: 2014
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        103949886
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        NLM24851085
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        10.1155/2014/370691
        NLM24851085
        PMC3960554
        103949886
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      formats:
      tig:
        atl: Cooperative quantum-behaved particle swarm optimization with dynamic varying search areas and Lévy flight disturbance.
      aug:
        au: Li, Desheng
        affil: Anhui Science and Technology University, Fengyang, Anhui 233100, China.
      sug:
        subj:
          Models, Theoretical
          Particle Swarm Optimization
          Algorithms
      ab: This paper proposes a novel variant of cooperative quantum-behaved particle swarm optimization (CQPSO) algorithm with two mechanisms to reduce the search space and avoid the stagnation, called CQPSO-DVSA-LFD. One mechanism is called Dynamic Varying Search Area (DVSA), which takes charge of limiting the ranges of particles' activity into a reduced area. On the other hand, in order to escape the local optima, Lévy flights are used to generate the stochastic disturbance in the movement of particles. To test the performance of CQPSO-DVSA-LFD, numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on both benchmark test functions and the combinatorial optimization issue, that is, the job-shop scheduling problem.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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