东南大学学报（自然科学版）2012，Vol.42Issue(z1)：157-162,6.DOI:10.3969/j.issn.1001-0505.2012.S1.032
用改进的协同免疫算法求解Flow Shop调度问题
An improved co-evolutionary immune algorithm for Flow Shop scheduling problem
摘要
Abstract
An improved co-evolutionary immune algorithm (ICIA) is developed for the Flow Shop scheduling problem. The vaccine of the algorithm is obtained from the local optimal solution iterated TV times, and refreshed with the changing of the optimal value of the different generations. Local search algorithm was applied to avoid the slow convergence in the start of the convergence phase. A new selection mechanism "80/20 Principle" was proposed to improve the objective function value difference of the later convergence phase. Computational results show the effectiveness of the ICIA in solving flow shop scheduling problem compared with GA( genetic algorithm) and CIA (co-evolutionary immune algorithm).关键词
协同进化算法/免疫算法/局部搜索算法/Flow Shop调度问题/80/20法则Key words
co-evolutionary algorithm/ immune algorithm/ local search algorithm/ Flow Shop scheduling problem/ eighty/twenty principle分类
信息技术与安全科学引用本文复制引用
张顺,徐震浩,顾幸生..用改进的协同免疫算法求解Flow Shop调度问题[J].东南大学学报（自然科学版）,2012,42(z1):157-162,6.基金项目
上海市自然科学基金资助项目(10ZR1408300)、国家自然科学基金资助项目(61104178,61174040)、上海市教委重点学科资助项目(J51901). （10ZR1408300）
