A Hybrid Particle Swarm Optimization for Binary CSPs

  • Qingyun Yang
  • Jigui Sun
  • Juyang Zhang
  • Chunjie Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)


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.


Particle Swarm Optimization Local Search Particle Swarm Constraint Satisfaction Problem Tabu List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qingyun Yang
    • 1
    • 2
  • Jigui Sun
    • 1
    • 2
    • 3
  • Juyang Zhang
    • 1
    • 2
  • Chunjie Wang
    • 4
  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Key Laboratory for Symbolic Computation and KnowledgeEngineering of Ministry of Education, Jilin UniversityChangchunChina
  3. 3.Open Laboratory for Intelligence Information ProcessingFudan UniversityShanghaiChina
  4. 4.Basic Sciences of ChangChun University of TechnologyChangChun University of TechnologyChangchunChina

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