An improved shuffled complex evolution algorithm with sequence mapping mechanism for job shop scheduling problems

The job shop problem is an important part of scheduling in the manufacturing industry. A new intelligent algorithm named Shuffled Complex Evolution (SCE) algorithm is proposed in this paper with the aim of getting the minimized makespan. The sequence mapping mechanism is used to change the variables in the continuous domain to discrete variables in the combinational optimization problem; the sequence, which is based on job permutation, is adopted for encoding mechanism and sequence insertion mechanism for decoding. While considering that the basic SCE algorithm has the drawbacks of poor solution and lower rate of convergence, a new strategy is used to change the individual's evolution in the basic SCE algorithm. The strategy makes the new individual closer to best individual in the current population. The improved SCE algorithm (ISCE) was used to solve the typical job shop problems and the results show that the improved algorithm is effective to the job shop scheduling.