摘要
本文提出了用于解决车间作业调度问题的混合自适应变异粒子群算法,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,利用遗传算法思想对粒子进行选择、交叉操作,并将模拟退火算法的优点融入到AMPSO算法中。仿真结果表明,混合AMPSO算法能够有效地、高质量地解决作业车间调度问题。
A Hybrid Adaptive Mutation Particle Swarm Optimization algorithm is proposed for the Job Shop scheduling problem. In the process of running, the mutation probability for the current best particle is determined by two factors： the variance of the population＇s fitness and the current optimal solution. Through combining genetic algorithms and simulated annealing algorithms with the Adaptive Mutation PSO algorithm, numerical simulation demonstrates that within the frame- work of the newly designed hybrid algorithm, the NP-hard classic job shop scheduling problem can be solved efficiently.
出处
《计算机工程与科学》
CSCD
北大核心
2010年第1期47-49,54,共4页
Computer Engineering & Science
关键词
粒子群优化
遗传算法
车间作业调度
particle swarm optimization
genetic algorithm
job-shop scheduling
