解决作业车间调度的微粒群退火算法*

针对微粒群优化算法在求解作业车间调度问题时存在的易早熟、搜索准确度差等缺点，在微粒群优化算法的基础上引入了模拟退火算法，从而使得算法同时具有全局搜索和跳出局部最优的能力，并且增加了对不可行解的优化，从而提高了算法的搜索效率；同时，在模拟退火算法中引入自适应温度衰变系数，使得SA算法能根据当前环境自动调整搜索条件，从而避免了微粒群优化算法易早熟的缺点。对经典JSP问题的仿真实验表明，与其他算法相比，该算法是一种切实可行、有效的方法。

Hybrid algorithm of particle swarm optimization and stimulated annealing for job-shop scheduling

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.