A Chaotic Differential Evolution Algorithm for Flexible Job Shop Scheduling

To solve the flexible job shop scheduling problem (FJSP), a chaotic differential evolution algorithm (CDEA) is proposed with makespan minimization criterion. In the CDEA, logistic mapping is used to generate chaotic numbers for the initialization, because it is helpful to diversify the CDEA population and improve its performance in preventing premature convergence to local minima. A compositive operation-machine-based (COMB) encoding method is employed to reduce computational burden. Meanwhile, a self-adaptive double mutation scheme and an elitist strategy in the selection operator are introduced to balance the population diversity and the convergence rate. The performance of schedules is evaluated in terms of makespan and relative error. The results are compared with different well-known algorithm from open literatures. The results indicate that the proposed CDEA is effective in reducing makespan because the small value of relative error and the faster convergence rate are observed.