Improved multi-objective artificial bee colony algorithm for optimal power flow problem
来源期刊：中南大学学报(英文版)2014年第11期
论文作者：MA Lian-bo(马连博) HU Kun-yuan(胡琨元) ZHU Yun-long(朱云龙) CHEN Han-ning(陈瀚宁)
文章页码：4220 - 4227
Key words：cooperative artificial colony algorithm; optimal power flow; multi-objective optimization
Abstract: The artificial bee colony (ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow (OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.
MA Lian-bo(马连博)1, 2, HU Kun-yuan(胡琨元)1, ZHU Yun-long(朱云龙)1, CHEN Han-ning(陈瀚宁)1
(1. Department of Information Service and Intelligent Control, Shenyang Institute of Automation,
Chinese Academy of Sciences, Shenyang 110016, China;
2. University of Chinese Academy of Sciences, Beijing 100039, China)
Abstract:The artificial bee colony (ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow (OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.
Key words:cooperative artificial colony algorithm; optimal power flow; multi-objective optimization
