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

摘要： 提出一种混合粒子群优化算法用于求解约束优化问题。新算法的主要特点是：在搜索机制方面，利用混沌初始化种群以提高初始群体的质量。为了扩大粒子的搜索范围，引入柯西变异算子。利用单形交叉算子对种群进行局部搜索。在约束处理技术方面，根据当前种群中可行解比例自适应地选择不同的个体比较准则。数值实验结果表明了该算法的有效性。

关键词: 约束优化问题, 粒子群优化算法, 自适应, 混沌