Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point

Jun Sun, Wei Fang, Vasile Palade, Xiaojun Wu, Wenbo Xu

Research output: Contribution to journalArticlepeer-review

188 Citations (Scopus)


This paper proposes a novel variant of quantum-behaved particle swarm optimization (QPSO) algorithm with the local attractor point subject to a Gaussian probability distribution (GAQPSO). The local attractor point in QPSO plays an important in that determining the convergence behavior of an individual particle. As such, the mean value and standard deviation of the proposed Gaussian probability distribution in GAQPSO are carefully selected. The distributions and diversities of the local attractor points in GAQPSO and QPSO are evaluated and compared. For the purpose of comparison, two variants of the GAQPSO algorithm are proposed by using a mutation probability and other types of probability distribution. The GAQPSO has been comprehensively evaluated on the suite of CEC2005 benchmark functions, and the experimental results are compared with those of the PSO and QPSO algorithms based on different probability distributions. It is shown by the results that the GAQPSO algorithm is an effective approach that can improve the QPSO performance considerably, that is, the GAQPSO algorithm is less likely to be stuck in local optima and hence it can achieve better solutions in most cases.

Original languageEnglish
Pages (from-to)3763-3775
Number of pages13
JournalApplied Mathematics and Computation
Issue number7
Publication statusPublished - 1 Dec 2011
Externally publishedYes


  • Gaussian probability distribution
  • Particle swarm optimization
  • Quantum behavior
  • Swarm intelligence

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics


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