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Artificial Bee Colony Algorithm Based on K -Means Clustering for Multiobjective Optimal Power Flow Problem
Sun LL(孙丽玲); Hu JT(胡静涛); Chen HN(陈瀚宁)
作者部门信息服务与智能控制技术研究室
发表期刊Mathematical Problems in Engineering
ISSN1024-123X
2015
卷号2015页码:1-18
收录类别SCI ; EI
EI收录号20152100879775
WOS记录号WOS:000355822100001
产权排序1
资助机构Key Research Program of the Chinese Academy of Sciences [KGZD-EW-302]
摘要An improved multiobjective ABC algorithm based on K -means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees' phase is modified. For keeping the population diversity, the multiswarm technology based on K -means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iteration, the population will be reclustered to facilitate information exchange among different clusters. Application of the new CMOABC on several multiobjective benchmark functions shows a marked improvement in performance over the fast nondominated sorting genetic algorithm (NSGA-II), the multiobjective particle swarm optimizer (MOPSO), and the multiobjective ABC (MOABC). Finally, the CMOABC is applied to solve the real-world optimal power flow (OPF) problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.
语种英语
WOS标题词Science & Technology ; Technology ; Physical Sciences
WOS类目Engineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications
关键词[WOS]BIOGEOGRAPHY-BASED OPTIMIZATION ; EMISSION LOAD DISPATCH ; GENETIC ALGORITHMS ; ABC ALGORITHM ; COST ; STRATEGY ; OPF
WOS研究方向Engineering ; Mathematics
引用统计
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/16152
专题信息服务与智能控制技术研究室
通讯作者Hu JT(胡静涛)
作者单位1.Department of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
3.School of Computer Science and Software, Tianjin Polytechnic University, Tianjin, China
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GB/T 7714
Sun LL,Hu JT,Chen HN. Artificial Bee Colony Algorithm Based on K -Means Clustering for Multiobjective Optimal Power Flow Problem[J]. Mathematical Problems in Engineering,2015,2015:1-18.
APA Sun LL,Hu JT,&Chen HN.(2015).Artificial Bee Colony Algorithm Based on K -Means Clustering for Multiobjective Optimal Power Flow Problem.Mathematical Problems in Engineering,2015,1-18.
MLA Sun LL,et al."Artificial Bee Colony Algorithm Based on K -Means Clustering for Multiobjective Optimal Power Flow Problem".Mathematical Problems in Engineering 2015(2015):1-18.
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