一种面向FJSP的混合优化遗传算法

A Modified Genetic Algorithm for Flexible Job-shop Scheduling Problem

针对柔性作业车间调度问题,传统的优化算法存在运行时间长、运行结果不稳定且不能有效求得近优解等问题。提出一种混合优化遗传算法(mGAs, Modified Genetic Algorithms),采用动态调整交叉概率和变异概率的自适应遗传算法,并使用量子粒子群算法优化染色体的选择算子。通过在经典数据集上与其它若干算法进行实验,结果表明了所提出算法相比同类优化算法,提高了算法的自适应能力,mGAs收敛速度更快、可以有效解决柔性作业车间调度问题,有效求取近优解。

For the problem of flexible job shop scheduling, the traditional optimization algorithm has many problems, such as long-running time, unstable running results, and inability to obtain the near-optimal solution. In this paper, a modified Genetic Algorithm(mGA) is proposed, which uses the adaptive Genetic algorithm to dynamically adjust the crossover probability and mutation probability, and uses the quantum particle swarm optimization algorithm to optimize the selection operator of chromosomes. Experiments on the classical data set and some other algorithms show that compared with the similar optimization algorithm, the proposed algorithm improves the adaptive ability of the algorithm, the mGA converges faster, can effectively solve the flexible job-shop scheduling problem, and can effectively obtain the near-optimal solution.