人工鱼群算法的优化及其软件实现
毕业论文关键词 人工鱼群算法 拥挤度因子 自适应参数 衰减函数 优化问题
Title Optimization of artificial fish swarm algorithm and software implementation
Abstract Artificial fish swarm algorithm (AFSA) is a new type of swarm intelligence algorithm. The study found that it is of a sort of good properties but some drawbacks meanwhile. In this paper, contrapose the existing problems in the basic AFSA such as the search accuracy is not high enough, the search is slow and so on, the traditional AFSA makes improvements in some parameters. We fine tune the behavior of random function, introduce the attenuation function of Visual and Step and use the adaptive congestion factor. Improved Artificial fish swarm algorithm (IAFSA) can reduce some unnecessary search, increase the number of searches near the extreme points, make the search faster and the search accuracy higher, get solutions with better stability. Simulation results show that the improved AFSA performances even more outstanding.
Keywords AFSA Congestion factor Adaptive parameter Attenuation function Optimization problem
目次
1绪论1
1.1课题的研究背景及意义1
1.2群智能算法研究与应用概述.2
1.2.1群智能算法概述..2
1.2.2群智能算法的主要分支..2
1.2.3群智能算法的优势与缺陷.3
1.3群智能优化算法的发展展望.3
1.4本文的主要工作及内容安排.4
2基本人工鱼群算法4
2.1人工鱼群算法概论..4
2.2人工鱼群算法的发展.5
2.3人工鱼群算法的结构模型..6
2.4人工鱼的基本行为..6
2.4.1觅食行为..6
2.4.2聚群行为..6
2.4.3追尾行为..7
2.4.4随机行为..7
2.5人工鱼群算法的寻优过程..7
2.5.1寻优原理..7
2.5.2基本人工鱼群算法的步骤.7
2.6本章小结8
3人工鱼群算法的参数分析9
3.1视野和步长..9
3.2拥挤度因子..9
3.2.1拥挤度因子定义..9
3.2.2拥挤度因子
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