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

Abstract

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

Keywords

• Hybrid initialization
• adaptive genetic algorithm
• opposition-based learning
• adaptive crossover-mutation