摘要
提出一种求解双目标job shop排序问题的混合进化算法.该算法采用改进的精英复制策略,降低了计算复杂性;通过引入递进进化模式,避免了算法的早熟;通过递进过程中的非劣解邻域搜索,增强了算法局部搜索性能.采用该算法和代表性算法NSGA-Ⅱ,MOGLS对82个标准双目标job shop算例进行优化对比,所得结果验证了该算法求解双目标job shop排序问题的有效性.
Aiming at solving bi-objective job shop scheduling problems, a hybrid evolutionary algorithm is proposed. An improved elite duplication strategy is applied, which reduces computational cost of the algorithm. An escalating evolutionary strategy is introduced into the algorithm, which is designed to overcome premature convergence. Besides, by applying a variable neighborhood search strategy to achieve Pareto solutions during the population escalation, the algorithm＇s local search ability is enhanced. Numerical experiments, which employ the proposed algorithm, together with other two typical algorithms NSGA-Ⅱ and MOGLS, is made to solve 82 bi-objective job shop scheduling problems. The optimization results show the effectiveness of the algorithm proposed here on solving bi-objective job shop scheduling problems.
出处
《控制与决策》
EI
CSCD
北大核心
2007年第11期1228-1234,共7页
Control and Decision
基金
国家自然科学基金项目(70771003
70521001)
新世纪优秀人才支持计划项目(NCET)
关键词
多目标优化
递进进化
JOB
SHOP
进化算法
Multi-objective optimization
Escalating evolution
Job shop
Evolutionary algorithm
