协同进化遗传算法及在车间调度中的应用  

Co-Evolutionary Genetic Algorithm and Its Application in Shop Scheduling

作　　者：周艳平[1] 王功明 ZHOU Yan-Ping;WANG Gong-Ming(College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学信息科学技术学院,青岛266061

出　　处：《计算机系统应用》2021年第10期248-253,共6页Computer Systems & Applications

摘　　要：提出了一种新型协同进化遗传算法.该算法借鉴了协同进化的思想,对种群进行分组处理,每个组根据自己组内个体的优良情况以及个体差异情况采用不同的交叉策略和变异策略.为防止早熟,当未触发灾变条件时仅采用自适应策略动态调整变异因子;当触发灾变条件时,在采用自适应策略的基础上引入灾变机制产生部分新个体以跳出局部最优,函数优化结果表明了该算法的有效性.采用该算法求解以最小化最大完工时间为优化目标的流水车间调度问题,结果表明,该算法在收敛速度以及优化结果的准确性都优于传统的遗传算法,在求解车间调度问题方面具有良好的性能.A new co-evolutionary genetic algorithm is proposed. Based on the coevolution idea, the algorithm divides the population into groups. Each group adopts different crossover and mutation strategies according to the individual situation and difference in its own group. To prevent prematurity, this algorithm only employs the adaptive strategy to dynamically adjust the mutation factor when the catastrophic condition is not triggered. When the catastrophic condition is triggered,with the adaptive strategy applied, the catastrophe mechanism is introduced to generate some new individuals to jump out of the local optimum. The results of function optimization show the effectiveness of the algorithm. The algorithm is used to deal with flow shop scheduling with the optimization objective of minimizing the maximum completion time. The results show that the algorithm is superior to the traditional genetic algorithm in convergence speed and accuracy of optimization results and performs well in solving the shop scheduling problems.

关 键 词：自适应 协同进化 流水车间调度 灾变机制 

分 类 号：TH186[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]