带学习策略的遗传算法求解柔性作业调度问题  

Improved evolutionary algorithm with learning strategy for the flexible job-shop scheduling problem

作　　者：王公堂[1] 许化强[1] 

机构地区：[1]山东师范大学物理与电子科学学院,山东济南250014

出　　处：《电子设计工程》2011年第16期57-60,共4页Electronic Design Engineering

摘　　要：多个不同最优染色体之间存在许多共有特征,如果进化过程中能识别这些优秀特征并尽可能遗传到后代个体中,则可以改善后代种群质量,加快遗传算法的收敛速度。在分析柔性作业调度问题的基础上,采用共生遗传算法并加入学习策略进行改进,在进化过程中学习父代优秀特征并指导后代的进化。通过实验测试,并与其他文献中的结果进行比较,表明改进算法在解的质量上有较好的效果。There are some common traits between different best chromosomes.If these traits can be identified during evolution and be copied to the offspring,the later population will have a high quality and the efficiency of algorithm will be enhanced.Based on the analysis of the characteristics of the flexible job-shop problem,the symbiotic evolutionary algorithm was improved and was added with learning strategy.So offspring can inherit best traits of parent generation with high efficiency.The improved algorithm is tested on instances taken from the literature and compared with their results.The results show that the proposed algorithm outperformed in solution quality.

关 键 词：作业调度问题 共生遗传算法 学习策略 柔性 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]