A golden-sine strategy based honey badger algorithm for flexible job shop scheduling problem

Abstract

Honey badger algorithm (HBA) is a new intelligent optimization algorithm proposed in recent years. However, HBA is easy to fall into local optimum and has low convergence speed when solving flexible job shop scheduling problem (FJSP). In this paper, we present a golden-sine strategy based honey badger algorithm (GSHBA). Golden-sine method has the advantages of fast computing speed, easy implementation, and good robustness. The performance of HBA can be improved by introducing this method. To verify the effectiveness of GSHBA, two benchmark test sets Brandimarte and Kacem are used. Our proposed GSHBA is compared with four other relatively new algorithms which are RFO, EI-TLBO, HBA and WOA. The comparison results show that the performance of GSHBA is better than these algorithms. Besides, the convergence and stability are analyzed as well by experiment, respectively. The experimental results show that GSHBA can effectively accelerate the convergence speed and enhance the stability.