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题名: Multi-hive bee foraging algorithm for multi-objective optimal power flow considering the cost, loss, and emission
作者: Chen HN(陈瀚宁) ; Ma LB(马连博) ; Zhu YL(朱云龙)
作者部门: 信息服务与智能控制技术研究室
关键词: Acoustic generators ; Benchmarking ; Electric load flow ; Functions ; Multiobjective optimization ; Particle swarm optimization (PSO) ; Topology
刊名: International Journal of Electrical Power and Energy Systems
ISSN号: 0142-0615
出版日期: 2014
卷号: 60, 页码:203-220
收录类别: SCI ; EI
产权排序: 1
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记录号: WOS:000336340400021
WOS标题词: Science & Technology ; Technology
类目[WOS]: Engineering, Electrical & Electronic
关键词[WOS]: BIOGEOGRAPHY-BASED OPTIMIZATION ; PARTICLE SWARM OPTIMIZATION ; GENETIC ALGORITHM ; EVOLUTIONARY ALGORITHM ; GLOBAL OPTIMIZATION ; COLONY ALGORITHM ; NSGA-II ; DISPATCH ; SEARCH
研究领域[WOS]: Engineering
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/14744
Appears in Collections:信息服务与智能控制技术研究室_期刊论文

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