遗传算法与人工免疫算法对车间调度问题求解

Study on job-shop scheduling problem by genetic algorithm and artificial immune algorithm

针对求解job-shop调度问题中存在的易出现局部最优、效率低下的问题,提出了一种新算法。该算法采用了一种评价种群过早收敛标准的方法,引进了新的加快遗传算法进化速度的交叉算子,最后设计了人工免疫算法中疫苗的提取和接种方法,即基于加工机器的基因片断抽取疫苗方法和最后完工机器个体的接种方法。通过实验证明该算法能够有效地解决易出现局部最优、效率低下等问题。

This paper developed a new algorithm for solving local-optimal and inefficiency problems in the job-shop scheduling problem. The algorithm introduced a method of evaluating premature convergence criteria for population, adopted a new crossover operator speeding up the evolution speed of genetic algorithm, and designed a method of extracting and injecting vaccines during the artificial immune algorithm, which was based on the method of gene segments extracting vaccines of processing machine and the injecting method of finally completed machine. According to the experiment verification, the algorithm can solve the problems of local-optimal and inefficiency effectively.