关键词: 微粒群算法, 遗传算法, 作业车间调度, 模拟退火

Abstract: The Standard Particle Optimization algorithm（PSO） generally is used to solve continuous optimization problems，and is used rarely to solve discrete problems such as Job-shop Scheduling Problem（JSP）.So，based on the problem of premature convergence and slow search speed of PSO，this paper proposes a hybrid particle swarm optimization algorithm to solve JSP.By combining PSO，Genetic Algorithm（GA） and Simulated Annealing（SA） algorithm，this algorithm not only enhances the global search ability，but also reduces the algorithm’s dependence on the parameters，and at the same time，overtakes the premature convergence of GA and PSO algorithm.The experimental results indicate that this algorithm not only has great advantage of convergence property over PSO，but also can avoid the premature convergence problem effectively.

Key words: particle swarm optimization algorithm, genetic algorithm, job-shop scheduling, simulated annealing