基于改进NSGA-II的车间排产优化算法研究

针对NSGA-II算法在处理车间排产优化问题中出现的子代种群多样性差、收敛能力差等问题,提出了一种改进NSGA-II的车间排产优化算法.改进NSGA-II算法主要对传统NSGA-II算法的交叉和变异环节,提出新的改进自适应交叉和变异算子,通过对个体拥挤度与种群平均拥挤度进行对比,并结合种群迭代进化过程,将遗传概率与种群...

Research on Optimization Algorithm of Workshop Scheduling Based on Improved NSGA-II

Aiming at the problems of poor population diversity and convergence ability of the progeny of NSGA-II algorithm in dealing with shop floor scheduling optimization, an improved NSGA-II algorithm is proposed. The new algorithm mainly proposes the new improved adaptive crossover and mutation operators for the crossover. It compares the individual crowding degree with the population average crowding degree, and combines with the population iterative evolution process. The genetic probability is associated with the population individual and the population evolution iteration times. So it avoids blind guidance and improves the convergence speed of the population. The new algorithm proposes the new uniform evolution elite retention strategy. Through choosing the population individuals via the adaptive hierarchy, it solves the problem of the poor diversity of the population of the offspring. Finally, it regards “maximize the minimum delivery lead time” and “minimize the maximum ideal processing time deviation” as the objective function. In the light of the problem of shop floor scheduling, it uses the improved NSGA-II algorithm to carry out the simulation analysis of the actual project. And by comparing the results of the algorithm optimization before and after the improvement, the effectiveness of the algorithm is verified. Its value parameter applied to the actual production scheduling problem examination is also proved.