【摘要】 针对Job Shop调度问题,提出了一种改进的合作型协同进化算法。根据机器数量"自然"分割种群,每个种群对应一台机器,个体以机器前工件的优先列表为编码;将静态繁殖理论引入遗传算子,并通过三种共生伙伴选择方式,利用改进的基于优先列表的G&T算法解码来评价个体;最后采用一种更新技术和动态群体更新策略来加快算法收敛。通过对Job Shop基准问题的优化,该算法获得了比传统的遗传算法更好的结果。更多还原

【Abstract】 An improved cooperative coevolutionary algorithm was proposed to solve job shop scheduling problem.According to the number of machines,the whole population was naturally divided into some subpopulation whose individuals encoded the preference list of jobs on the corresponding machine.The steady-state reproduction was introduced to genetic operators.The proposed algorithm combined three types of cooperative partners from every other subpopulation with the evaluated individuals to form the whole solutions and adopted the improved preference-list-based G&T algorithm to decode them to evaluate.Finally an updating technology and dynamic substitution with some new individuals at some other generations was adopted to speed up the convergence.Numerical experiments have been made to solve some job shop benchmark problems.The optimization results show the proposed algorithm have outperformed traditional genetic algorithms.更多还原