求解Job-Shop问题的改进搜索范围自适应遗传算法  

Modified Genetic Algorithm with Search Area Adaptation for Solving Job-shop Scheduling Problems

作　　者：王书锋[1] 梁燕[1] 王策[1] 

机构地区：[1]郑州大学电气工程学院,河南郑州450001

出　　处：《机床与液压》2010年第1期101-103,80,共4页Machine Tool & Hydraulics

摘　　要：为改善遗传算法求解Job-Shop问题时较差的局部搜索能力,并提高搜索最优解的速度,提出了一种改进的搜索范围自适应遗传算法。该算法采用一种新型的交叉操作,通过交叉和变异搜索过程提高遗传算法的局部搜索能力,同时采取MWKR优先规则优化初始种群。对作业车间调度问题进行仿真研究,结果表明该算法能找到问题的最优解,是可行和有效的。To improve the poor local search ability in using the genetic algorithm to solve Job-Shop problem, and to raise the searching speed for optimal solutions, a new modified genetic algorithm with search area adaptation was proposed. The algorithm proposes a new type of crossover operation, it uses crossover and mutation search phase to raise local search ability of genetic algorithm, and takes MWKR priority rules to optimize the initial populations. Combined with an example of job-shop scheduling problem, the simulation results illustrate that this algorithm can find the optimal solution, it is feasible and effective.

关 键 词：改进的搜索范围自适应遗传算法 作业车间调度问题 全局和局部搜索 优化规则 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]