Constrained Optimization by the ε Constrained Hybrid Algorithm of Particle Swarm Optimization and Genetic Algorithm

  • Tetsuyuki Takahama
  • Setsuko Sakai
  • Noriyuki Iwane
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)


The ε constrained method is an algorithm transformation method, which can convert algorithms for unconstrained problems to algorithms for constrained problems using the ε level comparison that compares search points based on the constraint violation of them. We proposed the ε constrained particle swarm optimizer εPSO, which is the combination of the ε constrained method and particle swarm optimization. The εPSO can run very fast and find very high quality solutions, but the εPSO is not very stable and sometimes can only find lower quality solutions. On the contrary, the εGA, which is the combination of the ε constrained method and GA, is very stable and can find high quality solutions, but it is difficult for the εGA to find higher quality solutions than the εPSO. In this study, we propose the hybrid algorithm of the εPSO and the εGA to find very high quality solutions stably. The effectiveness of the hybrid algorithm is shown by comparing it with various methods on well known nonlinear constrained problems.


Hybrid Algorithm Constrain Optimization Problem Constraint Violation Nonlinear Optimization Problem High Quality Solution 
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 2005

Authors and Affiliations

  • Tetsuyuki Takahama
    • 1
  • Setsuko Sakai
    • 2
  • Noriyuki Iwane
    • 1
  1. 1.Hiroshima City UniversityHiroshimaJapan
  2. 2.Hiroshima Shudo UniversityHiroshimaJapan

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