关键词: 人工鱼群算法, 自适应, 高斯变异, 反向解

Abstract: The Artificial Fish Swarm Algorithm（CAFSA） has some disadvantages such as falling into local optimum, poor robustness and low search accuracy. To solve these problems, this paper proposes an opposite adaptive and Gauss mutation artificial fish swarm algorithm. To provide more opportunities to explore potential better area, the algorithm applies opposite point to adjust direction and location of artificial fish. Thereby, the algorithm can jump out of local optimum fast and improve better global searching ability. In addition, this algorithm balances the global and local searching ability by using a non-linear function to adjust artificial fish’s visual and step. Otherwise, in order to solve early-maturing of artificial fish, using Gauss mutation mechanism based on optimal solution increases the diversity of every artificial fish. The simulation results show that improved artificial fish swarm algorithm has good searching quality, better accuracy and robustness. Meanwhile, the algorithm avoids early-maturing compared with other AFSAs.

Key words: Artificial Fish Swarm Algorithm（AFSA）, adaptive, Gauss Mutation（GM）, opposite point