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

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.