中文摘要：

将粒子群优化（Particle Swarm Optimization，PSO）算法和混沌搜索方法结合在一起，提出一种求解多目标柔性作业车间调度问题（Flexible job shop scheduling problem，FJSP）的新算法，利用混沌对PSO的参数进行自适应优化来有效平衡算法的全局搜索和局部开挖能力，并采用混沌局部优化策略来改善算法的搜索性能．此外，为了搜索到问题的所有非劣解，采用基于模糊逻辑的适应度函数来评价粒子．对于四个典型FJSP实例的实验验证了算法的可行性和有效性．

英文摘要：

In order to solve the multi-objective flexible job-shop scheduling problem（FJSP）,a novel algorithm combining Particle Swarm Optimization （PSO） and chaos is proposed. The parameters of PSO are self-adaptively adjusted by means of chaos to balance the global search and the local exploitation abilities efficiently. During the search of PSO, a chaotic local optimizer is adopted to improve its resulting precision. Moreover, for the purpose of finding all the non-dominated solutions ,a fitness function based on fuzzy logic is used to evaluate the particles. Experiments on four typical FJSP instances are presented to show the effectiveness and efficiency of the proposed approach.