Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems

被引：846
|
作者：
Deb, Kalyanmoy [1 ]
机构：
[1] Indian Inst Technol, Dept Mech Engn, Kanpur Genet Algorithms Lab KanGAL, Kanpur 208016, Uttar Pradesh, India
来源：
EVOLUTIONARY COMPUTATION | 1999年 / 7卷 / 03期
关键词：
Genetic algorithms; multi-objective optimization; niching; pareto-optimality; problem difficulties; test problems;
D O I：
10.1162/evco.1999.7.3.205
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult features of single-objective problems (such as multi-modality, isolation, or deception) to be directly transferred to the corresponding multi-objective problem. In addition, test problems having features specific to multi-objective optimization are also constructed. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization.
引用
收藏
页码：205 / 230
页数：26
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