基本蚁群优化算法及其改进毕业设计

摘要

自意大利学者M. Dorigo于1991年提出蚁群算法后，该算法引起了学者们的极大关注，在短短十多年的时间里，已在组合优化、网络路由、函数优化、数据挖掘、机器人路径规划等领域获得了广泛应用，并取得了较好的效果。本文首先讨论了该算法的基本原理，接着介绍了旅行商问题，然后对蚁群算法及其二种改进算法进行了分析，并通过计算机仿真来说明蚁群算法基本原理，然后分析了聚类算法原理和蚁群聚类算法的数学模型，通过调整传统的蚁群算法构建了求解聚类问题的蚁群聚类算法。最后，本文还研究了一种依赖信息素解决聚类问题的蚁群聚类算法，并把此蚁群聚类算法应用到对人工数据进行分类，还利用该算法对2005年中国24所高校综合实力进行分类，得到的分类结果与实际情况相符，说明了蚁群算法在聚类分析中能够收到较为理想的结果。

【关键词】蚁群算法；计算机仿真；聚类；蚁群聚类

Study on Ant Colony Algorithm and its Application in

Clustering

Abstract：

As the ant colony algorithm was proposed by M. Dorigo in 1991,it bringed a extremely large attention of scholars, in past short more than ten years, optimized, the network route, the function in the combination optimizes, domains and so on data mining, robot way plan has obtained the widespread application, and has obtained the good effect.This acticle discussed the basic principle of it at first, then introduced the TSP,this acticle also analysed the ant colony algorithm and its improved algorithm, and explanated it by the computer simulates, then it analysed the clustering algorithm and the ant clustering algorithm, builded the ant clustering algorith to solution the clustering by the traditioned ant algorithm. At last, this article also proposed the ant clustering algorith to soluted the clustering dependent on pheromon. Carry on the classification to the artificial data using this ant clustering algorithm; Use this algorithm to carry on the classification of the synthesize strength of the 2005 Chinese 24 universities; we can obtain the classified result which matches to the actual situation case. In the next work, we also should do the different cluster algorithm respective good and bad points as well as the classified performance aspect the comparison research; distinguish the different performance of different algorithm in the analysis when the dates are different.

Key words：

Ant colony algorithm; Computer simulation; clustering; Ant clustering

目录

1 引言 (3)

1.1群智能 (2)

1.2蚁群算法 (3)

1.3聚类问题 (4)

1.4本文研究工作 (5)

2 蚁群算法原理及算法描述 (5)

2.1蚁群算法原理 (5)

2.2蚁群优化的原理分析 (8)

2.3算法基本流程 (10)

2.4蚁群觅食过程计算机动态模拟 (11)

2.5人工蚂蚁与真实蚂蚁的对比 (13)

2.6本章小结 (14)

3 基本蚁群优化算法及其改进 (15)

3.1旅行商问题 (15)

3.2基本蚁群算法及其典型改进 (15)

3.2.1 蚂蚁系统 (15)

3.2.2 蚁群系统 (16)

3.2.3 最大-最小蚂蚁系统 (16)

3.3基本蚁群算法仿真实验 (16)

3.3.1 软硬件环境 (16)

3.3.2 重要参数设置 (16)

3.3.3仿真试验 (17)

3.4本章小结 (19)

4 蚁群聚类算法及其应用 (20)

4.1聚类问题 (20)

4.2蚁群聚类算法的数学模型 (21)

4.3蚁群聚类算法 (21)

4.3.1 蚁群聚类算法分析 (22)

4.3.2 蚁群聚类算法流程 (25)

4.4蚁群聚类算法在高校分类中的应用 (25)

4.5本章小结 (27)

5 结论与展望 (28)

参考文献 (29)

致谢 (31)

附录 (32)

1 引言

下面将介绍群智能以及蚁群算法和聚类问题。