Using multi-objective evolutionary algorithms for single-objective optimization

被引：55
|
作者：
Segura, Carlos [1 ]
Coello Coello, Carlos A. [2 ]
Miranda, Gara [3 ]
Leon, Coromoto [3 ]
机构：
[1] CINVESTAV IPN, Dpto Comp, UMI LAFMIA CNRS 3175, Mexico City, DF, Mexico
[2] CINVESTAV IPN, Dpto Comp, Evolutionary Computat Grp, Mexico City, DF, Mexico
[3] Univ La Laguna, Dpto Estadist IO & Comp, Tenerife, Spain
来源：
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH | 2013年 / 11卷 / 03期
关键词：
Single-objective optimization; Multi-objective optimization; Constrained optimization; Multiobjectivization; Diversity preservation; CONSTRAINED OPTIMIZATION; DIFFERENTIAL EVOLUTION; HELPER-OBJECTIVES; DIVERSITY; ROBOTICS;
D O I：
10.1007/s10288-013-0248-x
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
In recent decades, several multi-objective evolutionary algorithms have been successfully applied to a wide variety of multi-objective optimization problems. Along the way, several new concepts, paradigms and methods have emerged. Additionally, some authors have claimed that the application of multi-objective approaches might be useful even in single-objective optimization. Thus, several guidelines for solving single-objective optimization problems using multi-objective methods have been proposed. This paper offers a survey of the main methods that allow the use of multi-objective schemes for single-objective optimization. In addition, several open topics and some possible paths of future work in this area are identified.
引用
收藏
页码：201 / 228
页数：28
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