Improved NSGA-II Algorithm for multi-objective flexible job shop scheduling problem

Abstract

In order to solve the problems such as poor diversity and poor convergence ability of the offspring population of the NSGA-II Algorithm in the vehicle production scheduling problem, an improved shop scheduling algorithm based on NSGA-II is proposed. The improved NSGA-ii Algorithm mainly focuses on the crossover and mutation of the traditional NSGA-II Algorithm, and proposes a new improved self-adaptive Crossover and mutation operator. By comparing the individual crowding degree with the average crowding degree of the population, and combining the iterative evolution process of the population, in order to avoid blind orientation and to improve the convergence speed of the population, the genetic probability is correlated with the individuals and the evolution iteration times of the population, and a new uniform evolution strategy is proposed to select the individuals of the population through adaptive hierarchical selection, in order to improve the quality of the solution, the problem of the poor diversity of the offspring population was solved. The improved NSGA-II Algorithm is used to carry out the experimental simulation analysis. The effectiveness of the proposed algorithm is verified by comparing the optimization results before and after the improvement.