摘要
针对传统遗传算法在车间作业调度问题难以解决求解约束优化问题时存在难以同时兼顾求解质量和收敛效率这一问题,通过采用了基于工序编码的方式生成可行调度及借鉴遗传算法单点交叉方法,生成基于工件的交叉算子作为粒子的更新方式,将改进后的粒子群优化算法用于求解精冲零件车间调度问题,并在算法中通过利用局部搜索的方式提升粒子群中粒子收敛效率。通过对典型的调度测试问题进行模拟实验,证明了改进后的混合粒子群算法对于求解车间调度问题的适用性及具有不错的求解性能。
Genetic algorithm is applied for solving Job Shop Scheduling Problem, so it is difficult to take into account the quality of the solution and the efficiency of convergence for solving the problem of solving the constraint optimization problem. In this paper, we adopt the method of generating the feasible scheduling based on the process coding and the single point crossover method of the genetic algorithm to generate the workpiece-based crossover operator as the updating method of the particle, so that we can apply the particle swarm optimization algorithm to the shop job scheduling. It uses the local search method to further enhance the particle convergence rate. The simulation experiment is carried out by using the scheduling standard test problem, and the result proves the applicability of the algorithm to the job scheduling problem and the better performance.
出处
《机电工程技术》
2017年第9期6-10,共5页
Mechanical & Electrical Engineering Technology
基金
广东省科技计划项目(编号:2015A030401088)
省级科技型中小企业技术创新专项资金项目(编号:2016A010119143)
