一种新颖的人工鱼群算法

摘要

为了克服基本人工鱼群算法( AFSA)收敛速度慢、求解精度不高和易陷入局部最优的不足,提出了一种新颖的人工鱼群算法( AO-AFSA).该算法结合人工鱼与粒子群( PSO)中的粒子都具有个体学习能力和社会学习能力,模拟粒子群中粒子的速度位置更新公式去分别修改人工鱼群算法中人工鱼的觅食行为、聚群行为、追尾行为的更新公式.并采用5个典型的测试函数进行仿真实验,分析算法的寻优精度、收敛速度以及稳定性.测试结果表明改进后的算法能够较快地收敛至全局较优解,有更强的稳定性,并具有较好的寻优性能.%In order to overcome the slow convergence,low precision and local optimum of basic artificial fish swarm algorithm ( AFSA),this paper proposes a novel artificial fish swarm algorithm.This algorithm takes advantage of individual learning ability and social learning ability of particles in artificial fish swarm and particle swarm,simulates the speed and position of particles in particle swarm to update and modify the formulas of foraging,grouping and tailing.It then experiments five typical testing functions,analyzes the optimization precision,convergence speed and stability of the algorithm.The results turn out that the improved algorithm has stronger convergence,more stability and better performance.