An improved sand cat swarm optimization algorithm for flexible job shop scheduling problem

Meta-heuristic algorithms have become a popular approach for flexible job shop scheduling optimization problem. In this paper, an improved sand cat swarm optimization algorithm (ISCSO) is proposed for the flexible job shop scheduling problem. Firstly, the encoding and decoding scheme of the problem is defined, and the sand cat population is initialized by chaotic mapping, which increases the diversity of the initial distribution and accelerates the convergence speed. Secondly, a mechanism with a nonlinear convergence decreasing factor is used to balance exploration and exploitation and improve the global optimization performance. Finally, the fusion of the genetic algorithm to update agent positions achieves the discretization of the algorithm and helps escape from the local optima. In addition, we tested ISCSO, and the experimental results demonstrate its good performance in flexible job shop scheduling optimization problem.