Solving high dimensional bilevel multiobjective programming problem using a hybrid particle swarm optimization algorithm with crossover operator

  • Authors:

  • Tao Zhang;Tiesong Hu;Xuning Guo;Zhong Chen;Yue Zheng

  • Affiliations:

  • State Key Laboratory of Water Resource and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China and School of Information and Mathematics, Yangtze University, Jingzhou 434023, Chi ...;State Key Laboratory of Water Resource and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China;State Key Laboratory of Water Resource and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China;School of Information and Mathematics, Yangtze University, Jingzhou 434023, China;School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2013

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Abstract

In this paper, a hybrid particle swarm optimization with crossover operator (denoted as C-PSO) is proposed, in which a crossover operator is adopted for enhancing the information exchange between particles to prevent premature convergence of the swarm. The C-PSO algorithm is employed for solving high dimensional bilevel multiobjective programming problem (HDBLMPP) in this study, which performs better than the existing method with respect to the generational distance and has almost the same performance with respect to the spacing. Finally, we use four test problems and a practical application to measure and evaluate the proposed algorithm. Our results indicate that the proposed algorithm is highly competitive with respect to the algorithm representative of the state-of-the-art in high dimensional bilevel multiobjective optimization.