Multi-objective flexible job shop scheduling based on feature information optimization algorithm

Multi-objective optimization methods are increasingly used in job shop scheduling optimization strategies. However, in the design process of multi-objective optimization strategies, a neighborhood search is performed on all solutions in the optimization algorithm, resulting in a time-consuming search. In the algorithm selection process, feature information carried by individuals is often ignored, leading to a lack of targeted guidance ability in the algorithm. To address the limitations of the existing methods, a multi-objective flexible job shop scheduling method based on a feature information optimization algorithm (FIOA) was proposed. First, a framework of multiple group optimization algorithms was applied to construct diverse groups. Subsequently, a representative individual selection strategy was applied to mine individual offspring information and accelerate population convergence. To balance the exploration ability and computational resources of the FIOA, multiple neighborhood search rules were used to improve the utilization rate of individual offspring. In this study, the parameter configuration of the proposed algorithm was calibrated using the Taguchi method. To evaluate the effectiveness and superiority of the FIOA, each improvement of the FIOA algorithm was evaluated. In addition, it was compared with state-of-the-art algorithms in benchmark tests, and the results showed that the FIOA outperformed the other algorithms in solving flexible job shop scheduling.