Study on improving multi-objective flexible job shop scheduling based on Memetic algorithm in the NSGA-II framework

This paper was the research about multi-objective flexible job shop scheduling based on Memetic algorithm under the improved Non-dominated sorting genetic algorithm II (NSGA - II). On the basis of NSGA-II, a strategy of improving elite which was based on circular crowding distance was designed to increase the diversity of population distribution, prevent algorithm trapping in locally optimal solution, and avoid the disadvantage of premature genetic algorithm. And the research of multi-objective flexible job shop scheduling was established to optimize indexes, which was based on maximum completion time and different equipment load index.