基于改进粒子群算法的车间作业调度问题研究  

Research on Job-shop Scheduling Problem Based on Improved Particle Swarm Optimization

作　　者：乔佩利[1] 马丽丽[1] 郑林[1] 

机构地区：[1]哈尔滨理工大学计算机科学与技术学院,黑龙江哈尔滨150080

出　　处：《哈尔滨理工大学学报》2011年第2期35-39,共5页Journal of Harbin University of Science and Technology

基　　金：黑龙江省发展信息产业专项基金

摘　　要：针对现行的遗传算法存在过早收敛和进化速度过慢的局限,以及标准粒子群算法收敛精确度不高、易陷入局部极值点的缺点,通过分析原有算法的优化机理,提出一种惯性权重随粒子的进化代数增加而非线性减小的改进型粒子群算法,并将此算法应用于车间作业调度问题中.大量仿真实验结果表明,该算法在求解车间作业调度问题上具有可行性和有效性.Because the current genetic algorithms has the limitation of a premature convergence and the slow-evolutionary,and the standard particle swarm optimization has the shortcomings of low convergence precision,through analyzing the mechanism of the original optimization algorithm,this article proposes an improved particle swarm optimization in which inertia weight non-linear decreases with the increase of iterative generation,and applies this algorithm in job-shop scheduling problem.A large number of simulation results show that this algorithm has good feasibility and effectiveness in job-shop scheduling problem.

关 键 词：粒子群算法 车间作业调度问题 惯性权重 

分 类 号：TH166[机械工程—机械制造及自动化]