Abstract: In order to improve the traditional artificial fish swarm algorithm (AFSA), which is easy to fall into local optimal, poor robustness and low search accuracy, an adaptive dual-strategy artificial fish swarm algorithm based on particle swarm algorithm is proposed. Firstly, the algorithm simulates the moving operator of particle swarm optimization algorithm to adjust the moving direction and position of artificial fish, so that artificial fish has inertia mechanism and better expand new areas, so as to provide more opportunities for exploring potential better solutions and enhance its ability to jump out of local optimization. Then a strategy of adaptive field of view and inertia weight is used to balance the relationship between global search and local search. Finally, the opposition-based learning mechanism is introduced to design the random behavior of the two strategies to avoid the blindness of the original random behavior and increase the diversity of the fish. The simulation results show that the improved algorithm is better than other artificial fish swarm algorithms in terms of optimization accuracy, convergence speed and robustness, and has a good optimization effect in solving high-dimensional problems.

Key words: artificial fish swarm algorithm, particle swarm optimization algorithm, adaptive, inertia weight, opposition-based learning