An improved Artificial Fish Swarm Algorithm to solve flexible job shop

Abstract

The Artificial Fish Swarm Algorithm (AFSA) is a swarm-based metaheuristic algorithm which mimics the conduct of a group of fish in nature. In this paper, we proposed an improved AFSA algorithm for solving the Flexible Job Shop Scheduling Problem (FJSSP). The improvement is based on Variable Neighborhood Descent (VND) strategy which is performed on AFSA by different neighborhood structures to improve the performance of the original AFSA. The improved algorithm called (AFSA-VND) has been tested on some FJSSP benchmark instances for performance examination. The experimental results have shown that AFSA-VND has the superiority over the original AFSA, in terms of the solution quality and convergence rate.