计算机应用研究2017，Vol.34Issue(12)：3589-3593,5.DOI:10.3969/j.issn.1001-3695.2017.12.016
基于共轭梯度法改进的人工鱼群算法
Hybrid AFSA with conjugate gradient methods
摘要
Abstract
The basic artificial fish swarm algorithm has the shortcomings of low precision and low efficiency.Aiming at this problem,this paper introduced the conjugate gradient method in the artificial fish swarm algorithm,and obtained the improved artificial fish swarm algorithm.The proposed algorithm performed clustering and trailing operators on each artificial fish.If the update result was not improved,the algorithm would be updated using the conjugate gradient method.This paper introduced the conjugate gradient method to updating the artificial fish swarm,which could reduce the randomness and enhance the local searching ability of the artificial fish.This ensured that the artificial fish would be improved at the same time,thus speeding up the convergence rate of the artificial fish swarm algorithm.The results of numerical experiments show that the improved artificial fish swarm algorithm has faster convergence speed,and the convergence accuracy is also improved.关键词
人工鱼群算法/共轭梯度法/数值实验/适应度函数Key words
artificial fish swarm algorithm(AFSA)/conjugate gradient method/numerical experiment/fitness function分类
信息技术与安全科学引用本文复制引用
李君,梁昔明..基于共轭梯度法改进的人工鱼群算法[J].计算机应用研究,2017,34(12):3589-3593,5.基金项目
国家自然科学基金资助项目(61463009) （61463009）
北京市自然科学基金资助项目(4122022) （4122022）
中央支持地方科研创新团队项目(PXM2013-014210-000173) （PXM2013-014210-000173）
