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Solving Bilevel Multiobjective Programming Problem by Elite Quantum Behaved Particle Swarm Optimization

Author

Listed:
  • Tao Zhang
  • Tiesong Hu
  • Jia-wei Chen
  • Zhongping Wan
  • Xuning Guo

Abstract

An elite quantum behaved particle swarm optimization (EQPSO) algorithm is proposed, in which an elite strategy is exerted for the global best particle to prevent premature convergence of the swarm. The EQPSO algorithm is employed for solving bilevel multiobjective programming problem (BLMPP) in this study, which has never been reported in other literatures. Finally, we use eight different test problems to measure and evaluate the proposed algorithm, including low dimension and high dimension BLMPPs, as well as attempt to solve the BLMPPs whose theoretical Pareto optimal front is not known. The experimental results show that the proposed algorithm is a feasible and efficient method for solving BLMPPs.

Suggested Citation

  • Tao Zhang & Tiesong Hu & Jia-wei Chen & Zhongping Wan & Xuning Guo, 2012. "Solving Bilevel Multiobjective Programming Problem by Elite Quantum Behaved Particle Swarm Optimization," Abstract and Applied Analysis, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnlaaa:v:2012:y:2012:i:1:n:102482
    DOI: 10.1155/2012/102482
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    References listed on IDEAS

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    1. I. Nishizaki & M. Sakawa, 1999. "Stackelberg Solutions to Multiobjective Two-Level Linear Programming Problems," Journal of Optimization Theory and Applications, Springer, vol. 103(1), pages 161-182, October.
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    Cited by:

    1. Kuo-Yang Wu & Sendren Sheng-Dong Xu & Tzong-Chen Wu, 2013. "Optimal Scheduling for Retrieval Jobs in Double‐Deep AS/RS by Evolutionary Algorithms," Abstract and Applied Analysis, John Wiley & Sons, vol. 2013(1).

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