基于记忆库拉马克进化算法的作业车间调度

Memory Based Lamarckian Evolutionary Algorithm for Job Shop Scheduling Problem

多种群遗传算法相比遗传算法在性能上能够有所提高,但对具有较多局部最优解的作业车间调度问题,多种群遗传算法仍然难以改善易陷入局部最优解和局部搜索能力差的缺点.因此,提出了一种求解作业车间调度问题的新算法MGA-MBL(multi-population genetic algorithm based on memory-base and Lamarckian evolution for jobshop scheduling problem).MGA-MBL在多种群遗传算法的基础上通过引入记忆库策略,不但使子种群间的个体可以进行信息交换,而且有利于保持整个种群的多样性;通过构造基于拉马克进化机制的局部搜索算子来提高多种群遗传算法中子种群进化的局部搜索能力.由于MGA-MBL采用了全局寻优能力较强的模拟退火算法对记忆库中的个体进行优化,从而缓解了多种群遗传算法易陷入局部最优解的问题,并提高了算法求解作业车间调度问题的性能.对著名的benchmark数据进行测试,实验结果证实了MGA-MBL在求解作业车间调度问题上的有效性.

Compared with the Genetic Algorithm, a multi-population genetic algorithm has an enhancement in performance, but for a job shop scheduling problem, which has a lot of local optima, it also has the shortcomings of an easy-to-fall into local optima and a poor ability of local search. Therefore, an effective algorithm is proposed to solve job shop scheduling problem. The proposed algorithm, based on multi-population genetic algorithm, involves the strategy of memory-base and a mechanism of the Lamarckian evolution. Not only does the memory-base make individuals between sub-populations exchange information, but it can maintain the diversity of the population. The local search operator, based on Lamarckian evolution, is adupted to enhance the individual＇s ability of local search. The simulated annealing algorithm that has a stronger ability to jump out local optima than the genetic algorithm is used, thus, alleviated the problem and enhances the performance of the algorithm for job shop scheduling. The experimental results on the well-known benchmark instances show the proposed algorithm is very effective in solving job shop scheduling problems.