A hybrid particle swarm optimization for binary CSPs

  • Authors:

  • Qingyun Yang;Jigui Sun;Juyang Zhang;Chunjie Wang

  • Affiliations:

  • ,College of Computer Science and Technology, Jilin University, Changchun, China;,College of Computer Science and Technology, Jilin University, Changchun, China;,College of Computer Science and Technology, Jilin University, Changchun, China;Basic Sciences of ChangChun University of Technology, ChangChun University of Technology, Changchun, China

  • Venue:
  • ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
  • Year:
  • 2006

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Abstract

The target of solving constraint satisfaction problems(CSP) is to satisfy all constraints simultaneously. The CSP model is transformed into a discrete optimization problem with boundary constraints and is solved by particle swarm optimization(PSO) in this paper. To improve the performance of the proposed PSO algorithm, ERA(Environment, Reactive rules, Agent) model is used to proceed with local search after the process of boundary constraints. Further improvement including nohope and tabu list are also combined with PSO. When particles can not explore more search space, nohope is introduced to improve the activities of particles. Tabu list is used to avoid cycling in the global best particle. We experiment with random constraint satisfaction problem instances based on phase transition theory. Experimental results indicate that the hybrid algorithm has advantages on the search capability and the iterative number.