Adaptive Genetic Algorithm Based on Individual Similarity to Solve Multi-Objective Flexible Job-Shop Scheduling Problem

Aiming at the coupling of energy consumption and completion time in flexible job-shop scheduling, this paper took makespan as optimization objectives, established a scheduling model, proposed strategy based on improved genetic algorithm. Firstly, hybrid initialization method global minimum selection workload is introduced to generate initial population, scale population expanded increase diversity population; Secondly, generation offspring individuals improved, grouped according non-dominated ranking level crowding degree self-contained are generated by performing crossover mutation, neighborhood search simulated annealing reverse learning mutation operations respectively. Finally, an adaptive operation individual similarity proposed, which applied algorithm improve ability Relevant experimental results show that feasible effective scheduling.