智能系统学报2011，Vol.6Issue(5)：405-414,10.DOI:10.3969/j.issn.1673-4785.2011.05.004
谱图聚类算法研究进展
A survey of clustering algorithms based on spectra of graphs
摘要
Abstract
Differential evolution algorithms have gradually become one of the most popular types of stochastic search algorithms in the area of evolutionary computation. They have been successfully applied to solve various problems in real-world applications. Since their performance often depends heavily on the parameter settings, the design of parameter control and adaptation strategies is one of the current hot topics of research in differential evolution. Although numerous parameter control schemes have been proposed, systematic overviews and analysis are still lacking. In this paper, first the basic principles and operations of the differential evolution algorithm were briefly introduced, and then a detailed overview was provided on different parameter control and adaptation strategies by dividing them into the following four classes: empirical parameter settings, randomized parameter adaptation strategies, randomized parameter adaptation strategies with statistical learning, and parameter self-adaptation strategies. The overview emphasized the latter two classes. To study the efficacy of these parameter control and adaptation strategies, experiments with the background of real-valued function optimization were conducted to compare their efficiency and practicability further. The results showed that the parameter self-adaptation is one of the most effective strategies so far.关键词
谱图聚类/图割目标函数/谱宽松方法/相似图构建/半监督学习Key words
evolutionary computation/ differential evolution/ parameter control/ adaptation strategies/ self-adaptation分类
信息技术与安全科学引用本文复制引用
李建元,周脚根,关佶红,周水庚..谱图聚类算法研究进展[J].智能系统学报,2011,6(5):405-414,10.基金项目
国家自然科学基金资助项目(60873040). （60873040）
