Improved Adaptive Non-Dominated Sorting Genetic Algorithm With Elite Strategy for Solving Multi-Objective Flexible Job-Shop Scheduling Problem

Regarding the complicated flexible job-shop scheduling problem, it is not only required to get optimal solution of problem but also ensure low-carbon and environmental protection. Based on NSGA-II algorithm, this article proposes an improved adaptive non-dominated sorting genetic algorithm with elite strategy (IA-NSGA-ES). Firstly, constructive heuristic introduced in initial population phase, weight aggregation method used restrain multi-objective mathematical model which takes total completion time, carbon emission maximum machine tools load as objectives; secondly, improved, simulated annealing replace parent generation by child enhance replaced quality. The obtains Pareto set faster. Using standard computation example practical workshop for simulation, results simulation prove that effective feasible.