Multi-objective Flexible Job-Shop scheduling problem with DIPSO: More diversity, greater efficiency

The Flexible Job Shop Problem is one of the most important NP-hard combinatorial optimization problems. Evolutionary computation has been widely used in research concerning this problem due to its ability for dealing with large search spaces and the possibility to optimize multiple objectives. Particle Swarm Optimization has shown good results, but algorithms based on this technique have premature convergence, therefore some proposals have introduced genetic operators or other local search methods in order to avoid the local minima. Therefore, this paper presents a hybrid and multi-objective algorithm, Particle Swarm Optimization with Diversity (DIPSO), based on Particle Swarm Optimization along with genetic operators and Fast Non-dominated Sorting. Thus, to maintain a high degree of diversity in order to guide the search for a better solution while ensuring convergence, a new crossover operator has been introduced. The efficiency of this operator was tested in relation to the proposed objectives by using typical examples from literature. The results were compared to other studies that have shown good results by means Evolutionary Computation technique, for instance MOEA-GLS, MOGA, PSO + SA and PSO + TS.