摘要
针对车间调度问题,提出了一种2阶段混合粒子群算法(TS-HPSO).该算法在第1阶段为每个粒子设置较大的惯性系数w,同时去掉了粒子的社会学习能力,从而保证每个微粒在局部范围内充分搜索.第2阶段的混合粒子群算法以第1阶段每个粒子找到的最好解作为初始解,同时以遗传算法中的变异操作保证粒子多样性;为保证算法的寻优能力,对全局gbest进行贪婪邻域搜索.计算结果证明了本算法的有效性.
A two-stage hybrid particle swarm optimization（TS-HPSO） algorithm is proposed to solve the job-shop scheduling problem.In the first phase,the inertia coeffcient w is set bigger and the ability of social learning of particles is removed so that each particle can search the local area fully.In the second phase,the initial particles are initialized according to the best position of each particle searched in the first phase,and at the same time,the mutation operation of genetic algorithm is used to ensure the diversity of particles.A neighborhood based random greedy search is performed on the best particle gbest to ensure the optimization of the algorithm.The computational results show the effectiveness of the algorithm.
出处
《信息与控制》
CSCD
北大核心
2012年第2期193-196,209,共5页
Information and Control
基金
辽宁省教育厅计划资助项目(L2010086)
关键词
粒子群算法
车间作业调度问题
最小化完工时间
变异
particle swarm optimization
job-shop scheduling problem
minimized makespan
mutation
