基于全局信息的人工蜂群聚类算法

邓玉婷; 宋威; 马伟

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基于全局信息的人工蜂群聚类算法

更新时间：2026-09-04

- 基于全局信息的人工蜂群聚类算法
- Artificial Colony Clustering Algorithm based on Global Information
- 微电子学与计算机 2017年34卷第2期页码：20-24
- 作者机构：
  
  江南大学物联网工程学院,江苏,无锡,214122
- 作者简介：
- 基金信息：
  
  教育部中央高校自主科研计划重点项目 JUSRP51635B
- DOI：
  中图分类号：
- 纸质出版：2017
- 稿件说明：
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邓玉婷, 宋威, 马伟. 基于全局信息的人工蜂群聚类算法[J]. 微电子学与计算机, 2017, 34(2): 20-24.

Deng Yu-ting, SONG Wei, Ma Wei. Artificial Colony Clustering Algorithm based on Global Information[J]. 2017, 34(2): 20-24.
邓玉婷, 宋威, 马伟. 基于全局信息的人工蜂群聚类算法[J]. 微电子学与计算机, 2017, 34(2): 20-24. DOI：

Deng Yu-ting, SONG Wei, Ma Wei. Artificial Colony Clustering Algorithm based on Global Information[J]. 2017, 34(2): 20-24. DOI：

摘要

针对人工蜂群算法易陷入局部最优和收敛速度慢的不足

提出了一种基于全局信息的人工蜂群聚类算法.基于全局信息的人工蜂群聚类算法通过加入食物源平均丰富度(richness)

利用中间聚类效果

更好地更新食物源; 并且通过引入全局最优信息

提高跟随蜂的搜索效率

以获取聚类问题的全局最优解.同时在UCI机器学习库的4个标准数据集上进行了大量的实验来评估算法的性能.并将该算法和基本人工蜂群算法、粒子群算法和K-means算法进行比较.实验结果证明提出的基于全局信息的人工蜂群聚类算法具有更好的性能.

Abstract

An improved artificial bee colony algorithm was proposed for data clustering aiming at overcoming the shortcomings of being trapped in local optimum and slow convergence rate in this paper

called artificial colonyclustering algorithm based on global information (GI-ABC). On the one hand

the modified algorithm added the average richness of food to the updating equation of food source

taking advantage of the intermediate clustering consequence to update the food source better. On the other hand

it utilized global information to improve the search efficiency of the onlooker bees

which contributed to getting the global optimum. Meanwhile

abundant experiments were conducted to evaluate it. In the experiments

four of topical real data sets selected from the UCI Machine Learning Repository were used to test the performance of the strategies compared with the other clustering algorithms

such as ABC algorithm

Particle Swarm Optimization (PSO) algorithm and K-means algorithm. The results indicate that the modified algorithm can generate better results than other algorithms and has better general performance.

关键词

Keywords

references

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Omran M G H, Salman A, Engelbrecht A P. Dynamic clustering using particle swarm optimization with application in image segmentation[J]. Pattern Analysis and Applications, 2006, 8(4): 332-344. DOI: 332

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