Evolutionary multiobjective optimization: open research areas and some challenges lying ahead

被引：103
作者：
Coello Coello, Carlos A. [1 ]
Gonzalez Brambila, Silvia [1 ]
Figueroa Gamboa, Josue [1 ]
Castillo Tapia, Ma Guadalupe [2 ]
Hernandez Gomez, Raquel [3 ]
机构：
[1] UAM Azcapotzalco, Dept Sistemas, Av San Pablo Xalpa 180 Col, Mexico City 02200, DF, Mexico
[2] UAM Azcapotzalco, Dept Adm, Av San Pablo Xalpa 180 Col, Mexico City 02200, DF, Mexico
[3] IPN, CINVESTAV, Dept Comp Sci, Evolutionary Computat Grp, Ave IPN 2508 Col San Pedro Zacatenco, Mexico City 07360, DF, Mexico
来源：
COMPLEX & INTELLIGENT SYSTEMS | 2020年 / 6卷 / 02期
关键词：
Pythagorean fuzzy value; Pythagorean fuzzy sets; Similarity; MANY-OBJECTIVE OPTIMIZATION; FITNESS INHERITANCE; HYPER-HEURISTICS; PARETO FRONT; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHM; SMS-EMOA; SELECTION; SWARM; PERFORMANCE;
D O I：
10.1007/s40747-019-0113-4
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Evolutionary multiobjective optimization has been a research area since the mid-1980s, and has experienced a very significant activity in the last 20 years. However, and in spite of the maturity of this field, there are still several important challenges lying ahead. This paper provides a short description of some of them, with a particular focus on open research areas, rather than on specific research topics or problems. The main aim of this paper is to motivate researchers and students to develop research in these areas, as this will contribute to maintaining this discipline active during the next few years.
引用
收藏
页码：221 / 236
页数：16
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