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

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