标题:Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm
作者:Xu, Haidong; Jiang, Mingyan; Xu, Kun
作者机构:[Xu, Haidong; Jiang, Mingyan; Xu, Kun] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Peoples R China.
通讯作者:Jiang, Mingyan
通讯作者地址:[Jiang, MY]Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Peoples R China.
来源:JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS
出版年:2015
卷:26
期:2
页码:388-396
DOI:10.1109/JSEE.2015.00045
关键词:artificial bee colony (ABC) algorithm; Archimedean copula estimation of; distribution algorithm (ACEDA); ACEDA based on artificial bee colony; (ACABC) algorithm; numerical function optimization
摘要:The artificial bee colony (ABC) algorithm is a competitive stochastic population-based optimization algorithm. However, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to insufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm (ACEDA) is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA called Archimedean copula estimation of distribution based on the artificial bee colony (ACABC) algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six benchmark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimental results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best (gbest)-guided ABC (GABC) algorithm in most of the experiments.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:2
Scopus被引频次:3
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938574854&doi=10.1109%2fJSEE.2015.00045&partnerID=40&md5=0484450f4f032b308de80507078eb7a3
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