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

页码：201 / 228

页数：28

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