An improved particle swarm optimization for multi-objective flexible job-shop scheduling problem

This paper presents an improved particle swarm optimization(PSO) algorithm to solve the multi-objective flexible job-shop scheduling problem, which integrates the global search ability of PSO and the superiority of escaping from a local optimum with chaos. Firstly, the parameters of PSO are self-adaptively adjusted to balance the exploration and the exploitation abilities efficiently. Secondly, during the search of PSO, a chaotic local optimizer is adopted to improve its resulting precision and convergence rate. Experiments with typical problem instances are conducted to compare the performance of the proposed method with some other methods. The experimental analysis indicates that the proposed method performs better than the others in terms of the quality of solutions and computational time.