A New Multiobjective Evolutionary Algorithm Based on Decomposition of the Objective Space for Multiobjective Optimization

被引：12

作者：

Dai, Cai ^{[1
]}

Wang, Yuping ^{[1
]}

机构：

[1] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China

来源：

JOURNAL OF APPLIED MATHEMATICS | 2014年

基金：

中国国家自然科学基金;

关键词：

MOEA/D; PERFORMANCE;

D O I：

10.1155/2014/906147

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In order to well maintain the diversity of obtained solutions, a new multiobjective evolutionary algorithm based on decomposition of the objective space for multiobjective optimization problems (MOPs) is designed. In order to achieve the goal, the objective space of a MOP is decomposed into a set of subobjective spaces by a set of direction vectors. In the evolutionary process, each subobjective space has a solution, even if it is not a Pareto optimal solution. In such a way, the diversity of obtained solutions can be maintained, which is critical for solving some MOPs. In addition, if a solution is dominated by other solutions, the solution can generate more new solutions than those solutions, which makes the solution of each subobjective space converge to the optimal solutions as far as possible. Experimental studies have been conducted to compare this proposed algorithm with classic MOEA/D and NSGAII. Simulation results on six multiobjective benchmark functions show that the proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms.

引用

页数：9

共 42 条

[11]

Deb K, 2006, GECCO 2006: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2, P635

[12] Asynchronous Master-Slave Parallelization of Differential Evolution for Multi-Objective Optimization [J].

Depolli, Matjaz ;

Trobec, Roman ;

Filipic, Bogdan .

EVOLUTIONARY COMPUTATION, 2013, 21 (02) :261-291

[13] A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design [J].

Goh, C. K. ;

Tan, K. C. ;

Liu, D. S. ;

Chiam, S. C. .

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2010, 202 (01) :42-54

[14]

Goldberg DE., 1989, GENETIC ALGORITHMS S, V13

[15] Multiobjective immune algorithm with nondominated neighbor-based selection [J].

Gong, Maoguo ;

Jiao, Licheng ;

Du, Haifeng ;

Bo, Liefeng .

EVOLUTIONARY COMPUTATION, 2008, 16 (02) :225-255

[16] A review of multiobjective test problems and a scalable test problem toolkit [J].

Huband, Simon ;

Hingston, Phil ;

Barone, Luigi ;

While, Lyndon .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2006, 10 (05) :477-506

[17] Evolutionary Many-Objective Optimization by NSGA-II and MOEA/D with Large Populations [J].

Ishibuchi, Hisao ;

Sakane, Yuji ;

Tsukamoto, Noritaka ;

Nojima, Yusuke .

2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9, 2009, :1758-1763

[18] Effects of Using Two Neighborhood Structures on the Performance of Cellular Evolutionary Algorithms for Many-Objective Optimization [J].

Ishibuchi, Hisao ;

Sakane, Yuji ;

Tsukamoto, Noritaka ;

Nojima, Yusuke .

2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5, 2009, :2508-2515

[19]

Ishibuchi H, 2009, LECT NOTES COMPUT SC, V5467, P438, DOI 10.1007/978-3-642-01020-0_35

[20] Multi-objective optimization using genetic algorithms: A tutorial [J].

Konak, Abdullah ;

Coit, David W. ;

Smith, Alice E. .

RELIABILITY ENGINEERING & SYSTEM SAFETY, 2006, 91 (09) :992-1007

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