Hybrid algorithm of particle swarm optimization and stimulated annealing for job-shop scheduling
解决作业车间调度的微粒群退火算法*

This paper proposed a hybrid algorithm of particle swarm optimization (PSO) and simulated annealing (SA) algorithm, which was used to overcome the deficiency of solving job-shop scheduling problem (JSP), such as premature convergence and poor search accuracy. By combing PSO with SA algorithm, increased the ability of global search and jumping out of local optimum. And built the optimization of poor solutions to increase the search efficiency. And, by adding the self-adaptive temperature decay coefficient, made the SA algorithm could auto-tune the search criteria according the environment, avoid the deficiency of premature convergence. Comparsion with other results in some of the literatures indicates that this algorithm is a viable and effective approach for the job-shop scheduling problem.