MB-GNG: Addressing drawbacks in multi-objective optimization estimation of distribution algorithms

被引：22
|
作者：
Marti, Luis [1 ]
Garcia, Jesus [1 ]
Berlanga, Antonio [1 ]
Coello Coello, Carlos A. [2 ]
Molina, Jose M. [1 ]
机构：
[1] Univ Carlos III Madrid, Dept Informat, Grp Appl Artificial Intelligence, Madrid 28270, Spain
[2] IPN, CINVESTAV, Dept Comp Sci, Mexico City 07360, DF, Mexico
来源：
OPERATIONS RESEARCH LETTERS | 2011年 / 39卷 / 02期
关键词：
Multi-objective optimization; Estimation of distribution algorithm; Model building; Growing neural gas;
D O I：
10.1016/j.orl.2011.01.002
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：
We examine the model-building issue related to multi-objective estimation of distribution algorithms (MOEDAs) and show that some of their, as yet overlooked, characteristics render most current MOEDAs unviable when addressing optimization problems with many objectives. We propose a novel model-building growing neural gas (MB-GNG) network that is specially devised for properly dealing with that issue and therefore yields a better performance. Experiments are conducted in order to show from an empirical point of view the advantages of the new algorithm. (c) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码：150 / 154
页数：5
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