基于自适应变异的粒子群优化算法的车间作业调度优化及其软件实现

Application of Particle Swarm Optimization with Adaptive Mutation to Job Shop Scheduling Problem and Its Software Implementation

由于现行的遗传算法在解决车间作业调度问题时有局限性,本文将一个自适应变异的粒子优化算法应用于车间作业调度.该算法在运行的过程中根据群体适应度方差以及当前最优解的大小来确定当前最佳粒子的变异概率,变异操作增强了粒子群优化算法跳出局部最优解的能力.仿真实例的结果表明:该算法在解决车间作业调度问题上是可行的.

The reason why genetic algorithm available exhibi ts limitations when it is applied to job-shop scheduling problem (JSSP) is analyz ed. In this paper, a new particle swarm optimization algorithm is applied to so lve the problems in the JSSP. During the running, the mutation probability for the current best particle is determined by two factors: the variance of the popu lation's fitness and the current optimal solution. The ability of particle swar m optimization algorithm(PSO) to break away from the local optimum is greatly im proved by the mutation. The results of the example verify its better performance compared with the conventional algorithms.