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Chaotic Simulated Annealing Quantum-Behaved Particle Swarm Optimization Research

In: Proceedings of 20th International Conference on Industrial Engineering and Engineering Management

Author

Listed:
  • Ai-jun Liu

    (Xidian University)

  • Hua Li

    (Xidian University)

  • Ming Dong

    (Xidian University)

Abstract

In order to solve the premature convergence problem of Quantum-behaved Particle Swarm Optimization (QPSO), a Chaotic Simulated Annealing Quantum-behaved Particle Swarm Optimization (SAQPSO) is presented. Particles in population are first initialized using Logistics chaotic mapping, which in return, improve the global convergence performance of algorithm. Simulated annealing algorithm is introduced, with a certain probability of accepting bad solutions, enriches the population diversity, and improves the ability of global optimization. Adaptive temperature decay coefficient is introduced, so the simulated annealing algorithm can automatically adjust the search based on the current environment conditions, so as to improve the search efficiency of the algorithm. Results on Benchmark functions show that the proposed algorithm shows better search and convergence performance than standard QPSO and other algorithms.

Suggested Citation

  • Ai-jun Liu & Hua Li & Ming Dong, 2013. "Chaotic Simulated Annealing Quantum-Behaved Particle Swarm Optimization Research," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), Proceedings of 20th International Conference on Industrial Engineering and Engineering Management, edition 127, pages 1179-1186, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-40072-8_117
    DOI: 10.1007/978-3-642-40072-8_117
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