文献类型：期刊文献

中文题名：用改进的协同免疫算法求解Flow Shop调度问题

英文题名：An improved co-evolutionary immune algorithm for Flow Shop scheduling problem

作者：张顺[1];徐震浩[1];顾幸生[1]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

年份：2012

卷号：42

期号：A01

起止页码：157

中文期刊名：东南大学学报（自然科学版）

外文期刊名：Journal of Southeast University：Natural Science Edition

收录：CSTPCD;;EI(收录号：20124515655552);Scopus;北大核心:【北大核心2011】；CSCD:【CSCD2011_2012】；

基金：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)

语种：中文

中文关键词：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

外文关键词：co-evolutionary algorithm; immune algorithm; local search algorithm; Flow Shop sched-uling problem; eighty/twenty principle

摘要：利用改进的协同免疫算法(improved co-evolutionary immune algorithm,ICIA)求解FlowShop调度问题.算法中的疫苗取自迭代N次的局部最优解,并随着每代最优值的变化不断更新.为了克服协同免疫算法初期收敛速度慢的问题,加入了局部搜索算法;针对算法后期求解目标函数值差的问题,提出了一种新的种群选择机制"80/20法则".通过与遗传算法(genetic algorithm,GA)和未改进的协同免疫算法(co-evolutionary immune algorithm,CIA)比较,仿真实验结果验证了ICIA解决Flow Shop问题的有效性.
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 N 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-evolu- tionary immune algorithm）.