Constrained optimization by the ε constrained hybrid algorithm of particle swarm optimization and genetic algorithm

Authors:
Tetsuyuki Takahama;Setsuko Sakai;Noriyuki Iwane
Affiliations:
Hiroshima City University, Hiroshima, Japan;Hiroshima Shudo University, Hiroshima, Japan;Hiroshima City University, Hiroshima, Japan
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

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.