Hybrid Particle Swarm Optimization for Flexible Job-Shop Scheduling

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 (HPSO) 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.