中文摘要：

混沌是一种新颖的优化技术，具有随机性、遍历性的特点和易跳出局部极值的能力。为了提高粒子群优化算法（PSO）的性能，在PSO中引入混沌，优势互补，提出了一种混合PSO算法，并应用于柔性工作车间调度问题的求解。首先基于混沌时PSO的参数进行自适应优化。实现全局搜索与局部搜索同的有效平衡；然后。在PSO的搜索过程中引入混沌局部搜索策略，来提高解的精度和收敛速度．实验比较结果验证了该算法的全局搜索性能。

英文摘要：

As a new optimization technique, chaos bears randomicity, ergodicity and the superiority of escaping from a local optimum. By integrating the advantage of Chaos and PSO, a hybrid particle swarm optimization （FIPSO） algorithm was proposed and applied to solving the flexible job-shop scheduling problem （FJSP）. Parameters of PSO were adaptively chaotic optimized to efficiently balance the exploration and exploitation abilities. During the search process of PSO, the chaotic local optimizer was introduced to raise its resulting precision and convergence rate. The global search performance of HPSO was validated by the results of the comparative experiments.