Double Adaptive Artificial Fish Swarm Optimization Algorithm

The Artificial Fish Swarm Algorithm has some disadvantages such as falling into local optimum easily, slow convergence rate and low search accuracy. To solve these problems, this paper proposed a double adaptive artificial fish swarm optimization algorithm. Combining gauss mutation adaptive artificial fish swarm algorithm based on Von-neumann neighborhood and an adaptive artificial fish swarm algorithm with inertial weight, the algorithm uses same way that adapt visual and step adaptively to find the optimal solution. At the same time, the artificial fish swarm communication mechanism is introduced. This approach can keep balance between global exploration and local exploration. The simulation results show that the algorithm can effectively improve the quality of the search and avoid the phenomenon of precocious fish.