Improved Artificial Fish Swarm Algorithm

Abstract

Artificial fish swarm algorithm (AFSA) is a novel intelligent optimization algorithm. It has many advantages, such as good robustness, global search ability, tolerance of parameter setting, and it is also proved to be insensitive to initial values. However, it has some weaknesses as low optimizing precision and low convergence speed in the later period of the optimization. In this paper, an improved AFSA (IAFSA) is proposed with global information added to the artificial fish position in updating process. The experimental results indicate that the optimization precision and the convergence speed of the proposed method are significantly improved when compared with those of original AFSA.