Improved slime mould algorithm based on Gompertz dynamic probability and Cauchy mutation with application in FJSP

Abstract

Slime mould algorithm (SMA) is a novel meta-heuristic algorithm with fast convergence speed and high convergence accuracy. However, it still has some drawbacks to be improved. The exploration and exploitation of SMA is difficult to balance, and it easy to fall into local optimum in the late iteration. Aiming at the problems existing in SMA, a multistrategy slime mould algorithm named GCSMA is proposed for global optimization in this paper. First, the Logistic-Tent double chaotic map approach is introduced to improve the quality of the initial population. Second, a dynamic probability threshold based on Gompertz curve is designed to balance exploration and exploitation. Finally, the Cauchy mutation operator based on elite individuals is employed to enhance the global search ability, and avoid it falling into the local optimum. 12 benchmark function experiments show that GCSMA has superior performance in continuous optimization. Compared with the original SMA and other novel algorithms, the proposed GCSMA has better convergence accuracy and faster convergence speed. Then, a special encoding and decoding method is used to apply GCSMA to discrete flexible job-shop scheduling problem (FJSP). The simulation experiment is verified that GCSMA can be effectively applied to FJSP, and the optimization results are satisfactory.