A New Artificial Fish Swarm Algorithm

Zhou Shu-bo; Liu Wei; Luo Ping

doi:10.3969/j.issn.1007-7162.2015.04.018

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A New Artificial Fish Swarm Algorithm

更新时间：2026-05-27

- A New Artificial Fish Swarm Algorithm
- Vol. 32, Issue 4, Pages: 99-104(2015)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1007-7162.2015.04.018
  CLC：
- Online First：04 December 2015，
  
  Published：2015
- 稿件说明：
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周述波, 刘伟, 罗萍. 一种新颖的人工鱼群算法[J]. 广东工业大学学报, 2015, 32(4): 99-104.

Zhou Shu-bo, Liu Wei, Luo Ping. A New Artificial Fish Swarm Algorithm[J]. 2015, 32(4): 99-104.
周述波, 刘伟, 罗萍. 一种新颖的人工鱼群算法[J]. 广东工业大学学报, 2015, 32(4): 99-104. DOI： 10.3969/j.issn.1007-7162.2015.04.018.

Zhou Shu-bo, Liu Wei, Luo Ping. A New Artificial Fish Swarm Algorithm[J]. 2015, 32(4): 99-104. DOI： 10.3969/j.issn.1007-7162.2015.04.018.

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.

Abstract

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.

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