A Honey-Bee Mating Optimization Clustering Algorithm

LUO Ke; LI Lian; ZHOU Bo-xiang

doi:10.3969/j.issn.0372-2112.2014.12.015

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A Honey-Bee Mating Optimization Clustering Algorithm

更新时间：2025-07-16

- A Honey-Bee Mating Optimization Clustering Algorithm
- Acta Electronica Sinica Vol. 42, Issue 12, Pages: 2435-2441(2014)
- 作者机构：
  
  长沙理工大学计算机与通信工程学院,湖南,长沙,410114
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.11171095, No.71371065);Hengyang Joint Fund of Natural Science foundation of Hunan Province (No.10JJ8008);Program of Science and Technology Project of Hunan Province (No.2013SK3146)
- DOI：10.3969/j.issn.0372-2112.2014.12.015
  CLC： TP18
- Published：2014
- 稿件说明：
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LUO Ke, LI Lian, ZHOU Bo-xiang. A Honey-Bee Mating Optimization Clustering Algorithm[J]. Acta Electronica Sinica, 2014, 42(12): 2435-2441.
DOI：

LUO Ke, LI Lian, ZHOU Bo-xiang. A Honey-Bee Mating Optimization Clustering Algorithm[J]. Acta Electronica Sinica, 2014, 42(12): 2435-2441. DOI： 10.3969/j.issn.0372-2112.2014.12.015.

摘要

K-means算法因简单、高速等特点而被广泛应用

但该算法仍然存在依赖于初始聚类中心、易陷入局部最优等缺陷.为此

提出了一种蜜蜂交配优化聚类算法.该算法利用密度和距离初始化蜂群

并将局部搜索能力较强的粗糙集聚类算法作为工蜂的一种编码

以增强算法的局部搜索能力

最后在迭代过程中不断引入随机种群

增加种群的多样性

提高算法的全局寻优能力.实验结果表明

该算法不仅能有效抑制早熟收敛

而且具有较强的稳定性

较好的聚类效果.

Abstract

K-means algorithm is the most widely used method due to its easy understanding and fast speed.However

this method has the disadvantage that the clustering results depend on the selection of the initial clustering center and it is easy to fall into local optimal.For this reason

this paper proposed a honey-bee mating optimization clustering algorithm.It generates initial swarm by density and distance

and regards rough set clustering algorithm which has strong local search ability as a code of the works to enhance the local search ability of the algorithm.At last

in order to improve the diversity level of the swarm and the global optimization ability of the algorithm

random swarm population are introduced continuously in the iterative process.Our experiments show that the proposed algorithm not only can effectively suppress premature convergence

but also has strong stability and produces good clustering results.

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