Improved multi-objective artificial bee colony algorithm for optimal power flow problem  

作　　者：马连博 胡琨元 朱云龙 陈瀚宁 

机构地区：[1]Department of Information Service and Intelligent Control Shenyang Institute of Automation Chinese Academy of Sciences [2]University of Chinese Academy of Sciences

出　　处：《Journal of Central South University》2014年第11期4220-4227,共8页中南大学学报（英文版）

基　　金：Projects(61105067,61174164)supported by the National Natural Science Foundation of China

摘　　要：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.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.

关 键 词：cooperative artificial colony algorithm optimal power flow multi-objective optimization 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]