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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Constrained Optimization by the ε Constrained Hybrid Algorithm of Particle Swarm Optimization and Genetic Algorithm
verfasst von : Tetsuyuki Takahama, Setsuko Sakai, Noriyuki Iwane
Erschienen in: AI 2005: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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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
ε
PSO, which is the combination of the
ε
constrained method and particle swarm optimization. The
ε
PSO can run very fast and find very high quality solutions, but the
ε
PSO is not very stable and sometimes can only find lower quality solutions. On the contrary, the
ε
GA, 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
ε
GA to find higher quality solutions than the
ε
PSO. In this study, we propose the hybrid algorithm of the
ε
PSO and the
ε
GA 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.