Multi-hive bee foraging algorithm for multi-objective optimal power flow considering the cost, loss, and emission

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	Multi-hive bee foraging algorithm for multi-objective optimal power flow considering the cost, loss, and emission
	Chen HN(陈瀚宁); Ma LB(马连博); Zhu YL(朱云龙)
刊名	International Journal of Electrical Power and Energy Systems
	2014
卷号	60 页码:203-220
关键词	Acoustic generators Benchmarking Electric load flow Functions Multiobjective optimization Particle swarm optimization (PSO) Topology
ISSN号	0142-0615
通讯作者	朱云龙
产权排序	1
中文摘要	This paper proposes a multi-hive multi-objective bee algorithm (M 2OBA) for optimal power flow (OPF) in power systems. The proposed M2OBA extend original artificial bee colony (ABC) algorithm to multi-objective and cooperative mode by combining external archive, comprehensive learning, greedy selection, crowding distance, and cooperative search strategy. Our algorithm uses the concept of Pareto dominance and comprehensive learning mechanism to determine the flight direction of a bee and maintains nondominated solution vectors in external archive based on greedy selection and crowing distance strategies. With cooperative search approaches, the single population ABC has been extended to interacting multi-hive model by constructing colony-level interaction topology and information exchange strategies. With six mathematical benchmark functions, M2OBA is proved to have significantly better performance than three successful multi-objective optimizers, namely the fast non-dominated sorting genetic algorithm (NSGA-II), the multi-objective particle swarm optimizer (MOPSO), and the multi-objective ABC (MOABC), for solving complex multi-objective optimization problems. M2OBA is then used for solving the real-world 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, which are also compared to NSGA-II, MOPSO, and MOABC, are presented to illustrate the effectiveness and robustness of the proposed method. © 2014 Elsevier Ltd. All rights reserved.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Engineering, Electrical & Electronic
研究领域[WOS]	Engineering
关键词[WOS]	BIOGEOGRAPHY-BASED OPTIMIZATION ; PARTICLE SWARM OPTIMIZATION ; GENETIC ALGORITHM ; EVOLUTIONARY ALGORITHM ; GLOBAL OPTIMIZATION ; COLONY ALGORITHM ; NSGA-II ; DISPATCH ; SEARCH
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000336340400021
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/14744]
专题	沈阳自动化研究所_信息服务与智能控制技术研究室
推荐引用方式 GB/T 7714	Chen HN,Ma LB,Zhu YL. Multi-hive bee foraging algorithm for multi-objective optimal power flow considering the cost, loss, and emission[J]. International Journal of Electrical Power and Energy Systems,2014,60:203-220.
APA	Chen HN,Ma LB,&Zhu YL.(2014).Multi-hive bee foraging algorithm for multi-objective optimal power flow considering the cost, loss, and emission.International Journal of Electrical Power and Energy Systems,60,203-220.
MLA	Chen HN,et al."Multi-hive bee foraging algorithm for multi-objective optimal power flow considering the cost, loss, and emission".International Journal of Electrical Power and Energy Systems 60(2014):203-220.