Multilevel learning aided coevolutionary particle swarm optimization algorithm for multiobjective fuzzy flexible job shop scheduling problem

Multi-objective fuzzy Flexible Job shop Scheduling Problems (MofFJSPs) aim to optimize multiple fuzzy and conflicting objectives by finding the sequences of jobs and machines under realistic production constraints. However, as the objective functions of MofFJSPs exhibit fuzzy and conflicting properties, algorithms often face the challenge of being trapped in local optima and slow convergence. Therefore, to solve MofFJSPs effectively and efficiently, a Multi-Level Learning-aided Co-evolutionary Particle Swarm Optimization (MLL-CPSO) algorithm is proposed. The MLL-CPSO uses multiple populations to cooperatively solve multiple fuzzy objectives and hierarchically learns the evolutionary information, together with three novel designs. First, a Multi-Level Learning (MLL) strategy is proposed to learn the short-term personal evolutionary information, the long-term social information, and the co-evolutionary information to avoid local optima and approach the Pareto optima fast. Second, a Simulated Annealing-based Strengthening Diversity (SASD) strategy is designed to improve the diversity of populations and enhance the global search ability. Thirdly, a Co-evolutionary Information Update (CeIU) mechanism is developed to improve the quality of co-evolutionary information from different populations and drive populations to explore more Pareto optimal solutions. In experiments, MLL-CPSO is compared with 7 state-of-the-art algorithms on 3 typical benchmarks with 23 instances. The experimental results show that MLL-CPSO outperforms the compared algorithms in most test environments.