Simulation analysis of the fish swarm algorithm optimized by PSO
Duan Qi-chan · Kongzhi yu juece · 2013
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.