论文部分内容阅读
为求解生产调度中的多目标混流装配线排序问题,提出一种将遗传算法与粒子群算法相结合的混合算法——GA-PSO算法.为更好地评价个体,提出一种引入个体的Pareto分级和拥挤距离的适应度函数.针对标准PSO算法求解排序问题的不足,提出了一种将实数映射成离散值的方法.在算法的历次迭代中,早期通过遗传算法全局搜索优势扩大搜索范围,抑制早期收敛;后期通过粒子群算法局部搜索加速收敛.通过基准算例分析,证明了所提算法优于SPEA-Ⅱ,NSGA-Ⅱ和PS-NC GA算法.本算法已实际应用于某企业混流装配线排序.
In order to solve the scheduling problem of multi-objective mixed-flow assembly line in production scheduling, a hybrid algorithm based on genetic algorithm and particle swarm optimization (GA-PSO) is proposed.For better evaluation of individuals, a Pareto classification And crowding distance fitness function.Aiming at the shortage of the standard PSO algorithm to solve the sorting problem, a method of mapping the real numbers into discrete values is proposed.In the previous iterations of the algorithm, the advantages of the global search of the genetic algorithm are extended early to expand the search range, Which converges in the early stage and accelerates convergence in the late stage through the local search of Particle Swarm Optimization.The algorithm is proved to be superior to the SPEA-Ⅱ, NSGA-Ⅱ and PS-NC GA algorithms through the benchmark example analysis.The algorithm has been applied to the mixed-flow assembly line Sort.