求解Job Shop调度问题的自适应遗传算法设计

The Design of Adaptive Genetic Algorithms for the Solution of Job Shop Scheduling Problem

针对标准遗传算法中交叉概率Pc和变异概率Pm固定不变带来的局限性,以及M.Sr-invivas自适应遗传算法的缺点,提出了根据适应值集中程度,自适应地变化整个种群的Pc和Pm的一种改进的自适应遗传算法,文中系统地介绍了算法的改进及算法的流程,并将算法应用于求解JSP问题,最后用一个典型的测试例子,对本文设计的算法的求解效果进行了测试,并对测试结果进行了分析.

To overcome the limitation resulting from the fixed cross probability Pc and mutation probability Pm in standard genetic algorithm and the limitation in adaptive genetic algorithms, this paper proposes a revised adaptive genetic algorithm, which adaptively changes the whole population based on the fitness. First, an introduction of the revised algorithm and its process is given and the algorithm is applied to solve the job shop scheduling problem. And then, the result of the revised algorithm on a typical example is tested. Finally, the outputs of the test are analyzed.