To solve the problem that the standard particle swarm optimization(PSO) algorithm has a low success rate when
applied to the optimization of multi-dimensional and multi-extreme value functions, and the convergence rate and precision
of basic artificial fish-swarm algorithm(AFSA) also need to be improved, an algorithm called PSO-FSA is proposed. This
algorithm introduces the velocity inertia, remembering capacity and communicating capacity of PSO algorithm into the
AFSA. As a result, the PSO-FSA has totally five kinds of behavior pattern as follows: swarming, following, remembering,
communicating and searching. In addition, a parameter called max?? is defined to limit the visual and step of the fish swarm
dynamically. The simulation analysis shows that the PSO-FSA has a better performance in convergence speed, searching
precision compared to the standard PSO algorithm and the basic AFSA.