计算机技术与发展2016，Vol.26Issue(11)：37-40,44,5.DOI:10.3969/j.issn.1673-629X.2016.11.008
人工鱼群算法的改进
Improvement of Artificial Fish Swarm Algorithm
摘要
Abstract
Artificial Fish Swarm Algorithm ( AFSA) is a new random search optimization algorithm. The preliminary study shows that it has many promising features,but also some disadvantages. Aiming at the problem of AFSA,such as long running time or being in local optimal,caused by uniformly random behavior and constant of congestion factor. Based on symmetric normality random behavior,self-a-daption adjusts the parameter of this behavior,and a large number of unused circuitous searches are reduced,and a more complete search within solution space is obtained for artificial fishes so that a high search efficiency is arrived at. The self-adaption congestion factor is a-dopted and a new fitness function is porposed,increasing the convergence rate of satisfactory solution domain,making the result more sta-ble. Results of experiments show that there is an obvious advantage for this improved method compared with the basic artificial fish-swarm algorithm.关键词
随机行为/拥挤度因子/适应度函数/人工鱼群算法/优化Key words
random behavior/congestion factor/fitness function/artificial fish swarm algorithm/optimization分类
信息技术与安全科学引用本文复制引用
唐莉,张正军,王俐莉..人工鱼群算法的改进[J].计算机技术与发展,2016,26(11):37-40,44,5.基金项目
全国统计科学研究计划重点项目(2013LZ45) （2013LZ45）
