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

被引：873

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

页码：205 / 230

页数：26

共 50 条

[41] Multi-objective optimization modeling and genetic algorithms designing for logistics distribution problems (ID: 3-039) [J].

Sun Jiangsu ;

Zhou Hong ;

Tan Xiaowei .

PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-5: INDUSTRIAL ENGINEERING AND MANAGEMENT INNOVATION IN NEW-ERA, 2006, :1153-1158

[42] Multi-objective genetic algorithms in the study of the genetic code's adaptability [J].

de Oliveira, Lariza Laura ;

Freitas, Alex A. ;

Tinos, Renato .

INFORMATION SCIENCES, 2018, 425 :48-61

[43] A Hybrid Cellular Genetic Algorithm for Multi-objective Crew Scheduling Problem [J].

Jolai, Fariborz ;

Assadipour, Ghazal .

HYBRID ARTIFICIAL INTELLIGENCE SYSTEMS, PT 1, 2010, 6076 :359-367

[44] BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems [J].

Li, Xiang ;

Du, Gang .

COMPUTERS & OPERATIONS RESEARCH, 2013, 40 (01) :282-302

[45] Multi-objective test problems, linkages, and evolutionary methodologies [J].

Deb, Kalyanmoy ;

Sinha, Ankur ;

Kukkonen, Saku .

GECCO 2006: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2, 2006, :1141-+

[46] Multi-objective evolutionary algorithms and pattern search methods for circuit design problems [J].

Biondi, Tonio ;

Ciccazzo, Angelo ;

Cutello, Vincenzo ;

D'Antona, Santo ;

Nicosia, Giuseppe ;

Spinella, Salvatore .

JOURNAL OF UNIVERSAL COMPUTER SCIENCE, 2006, 12 (04) :432-449

[47] Discussion of Parallel Model of Multi-Objective Genetic Algorithms on Heterogeneous Computational Resources [J].

Hiroyasu, Tomoyuki ;

Yoshii, Kengo ;

Miki, Mitsunori .

GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2, 2007, :904-904

[48] Blast furnace charging optimization using multi-objective evolutionary and genetic algorithms [J].

Mitra, Tamoghna ;

Pettersson, Frank ;

Saxen, Henrik ;

Chakraborti, Nirupam .

MATERIALS AND MANUFACTURING PROCESSES, 2017, 32 (10) :1179-1188

[49] Genetic algorithms based multi-objective optimization of an iron making rotary kiln [J].

Mohanty, Debashis ;

Chandra, Arnab ;

Chakraborti, Nirupam .

COMPUTATIONAL MATERIALS SCIENCE, 2009, 45 (01) :181-188

[50] Optimal power system generation scheduling by multi-objective genetic algorithms with preferences [J].

Zio, E. ;

Baraldi, P. ;

Pedroni, N. .

RELIABILITY ENGINEERING & SYSTEM SAFETY, 2009, 94 (02) :432-444

← 1 2 3 4 5 →