关键词: 约束优化问题, 粒子群优化算法, 自适应, 混沌

Abstract: A hybrid Particle Swarm Optimization algorithm is proposed for solving constrained optimization problems. The primary features of the algorithm proposed are as follows. As for search mechanism, chaotic initialization is introduced to improve the quality of initial population. The Cauchy mutation operator is introduced which can expand the search range. The simplex crossover operator is used to enrich the exploratory and exploitative abilities of the algorithm proposed. As for constraint-handling technique, a new individual comparison criterion is proposed, which can adaptively select different individual comparison criteria according to the proportion of feasible solution in current population. The proposed algorithm is tested on several well-known benchmark problems, and the results show that it is effective.

Key words: constrained optimization problems, Particle Swarm Optimization（PSO） algorithm, adaptive, chaos