粒子群优化鱼群算法仿真分析

针对标准粒子群算法(PSO)寻优多维多极值函数成功率低，基本人工鱼群算法(AFSA)收敛速度和精度有待提高等问题，提出粒子群优化鱼群算法(PSO-FSA)。该算法将速度惯性、个体记忆和个体间交流等特征引入鱼群算法，使鱼群行为模式扩充至追尾、聚群、记忆、交流以及觅食。此外，定义参数max D动态限定鱼群搜索的视野和步长。仿真分析表明，粒子群优化鱼群算法较两种基本算法而言具有更快的收敛速度和寻优精度。

Simulation analysis of the fish swarm algorithm optimized by PSO

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.