A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems

被引：167
|
作者：
Qu, B. Y. [1 ,2 ]
Zhu, Y. S. [1 ,3 ]
Jiao, Y. C. [1 ]
Wu, M. Y. [1 ]
Suganthan, P. N. [4 ]
Liang, J. J. [1 ,3 ]
机构：
[1] Zhongyuan Univ Technol, Sch Elect & Informat Engn, Zhengzhou 450007, Henan, Peoples R China
[2] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Henan, Peoples R China
[3] Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, Peoples R China
[4] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
来源：
SWARM AND EVOLUTIONARY COMPUTATION | 2018年 / 38卷
基金：
中国国家自然科学基金; 新加坡国家研究基金会;
关键词：
Evolutionary algorithms (EAs); Multi-objective evolutionary algorithms (MOEAs); Environmental economic dispatch (EED); Wind power; Electric vehicles; Multi-objective optimization; ECONOMIC EMISSION DISPATCH; PARTICLE SWARM OPTIMIZATION; LEARNING-BASED OPTIMIZATION; RENEWABLE ENERGY-SOURCES; BEE COLONY ALGORITHM; POWER DISPATCH; GENETIC ALGORITHM; LOAD DISPATCH; WIND; SEARCH;
D O I：
10.1016/j.swevo.2017.06.002
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Development of efficient multi-objective evolutionary algorithms (MOEAs) has provided effective tools to solve environmental/economic dispatch (EED) problems. EED is a highly constrained complex bi-objective optimization problem. Since 1990s, numerous publications have reported the applications of MOEAs to solve the EED problems. This paper surveys the state-of-the-art of research related to this direction. It covers topics of typical MOEAs, classical EED problems, Dynamic EED problems, EED problems incorporating wind power, EED problems incorporating electric vehicles and EED problems within micro-grids. In addition, some potential directions for future research are also presented.
引用
收藏
页码：1 / 11
页数：11
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