[1]李志明.飞蛾扑火优化算法在聚类分析中的应用[J].计算机技术与发展,2020,30(09):104-108.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 019]
　LI Zhi-ming.Application of Moth-flame Optimization Algorithm in Clustering Analysis[J].,2020,30(09):104-108.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 019]
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飞蛾扑火优化算法在聚类分析中的应用
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年09期
页码:
104-108
栏目:
智能、算法、系统工程
出版日期:
2020-09-10

文章信息/Info

Title:
Application of Moth-flame Optimization Algorithm in Clustering Analysis
文章编号:
1673-629X(2020)09-0104-05
作者:
李志明
广西科技师范学院 数学与计算机科学学院,广西 来宾 546199
Author(s):
LI Zhi-ming
School of Mathematics and Computer Science,Guangxi Science & Technology Normal University, Laibin 546199,China
关键词:
最优化; 飞蛾扑火优化; 单纯形法; 聚类分析; K 均值
Keywords:
optimization; moth-flame optimization; simplex method; clustering analysis; K-means
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 09. 019
摘要:
随着高科技等新兴产业的快速发展,如何处理海量的数据就成为了比较棘手的问题。 应用聚类分析技术从海量数据中提取有用的信息是解决该问题的关键。飞蛾扑火优化(MFO)算法是一种新颖的启发式优化算法,该算法的主要灵感来源于飞蛾在自然界中被称为横向定位的飞行方式,具有结构简单、可调节参数少、容易实现、鲁棒性强等优点。 在飞 蛾扑火优化算法中引入单纯形法,提出了一种基于单纯形法的飞蛾扑火优化算法(SMMFO)。 SMMFO 算法不仅克服了飞蛾扑火优化算法易陷入局部最优的缺陷,增加了算法的种群多样性,加强了其局部搜索能力,而且还提高了算法的执行效率,加快了算法的收敛速度,优化了飞蛾扑火优化算法对数据集的聚类分析性能。 结果证明,SMMFO 在聚类分析中是非常有效的。
Abstract:
With the rapid development of high-tech and other emerging industries,how to deal with massive data has become a more difficult problem. Applying cluster analysis technology to extract useful information from massive data is the key to solving this problem. The Moth-flame Optimization (MFO) algorithm is a novel heuristic algorithm,which is mainly inspired by the navigation method of moths in nature called transverse orientation,with advantages of simple structure,few adjustable parameters,easy implementation and strong robustness. The simplex method is introduced in the algorithm,based on which the SMMFO is proposed. SMMFO not only overcomes the defects that MFO is easy to fall into the local optimal,increases the population diversity of the algorithm,and strengthens its local search ability,but also improves the execution efficiency of the algorithm, accelerates its convergence rate,and improves the performance for clustering analysis of data set. The results have proved that SMMFO is effective in cluster analysis.

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更新日期/Last Update: 2020-09-10