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

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-build...

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Bibliographic Details
Published in:Operations research letters Vol. 39; no. 2; pp. 150 - 154
Main Authors: Martí, Luis, García, Jesús, Berlanga, Antonio, Coello Coello, Carlos A., Molina, José M.
Format: Journal Article
Language:English
Published: Oxford Elsevier B.V 01.03.2011
Elsevier
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Computer science; control theory; systems
Dealing
Decision theory. Utility theory
Empirical analysis
Estimation of distribution algorithm
Exact sciences and technology
Growing neural gas
Mathematical programming
Model building
Multi-objective optimization
Networks
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
Theoretical computing
Multi-objective optimization
Model building
Growing neural gas
Estimation of distribution algorithm
Evolutionary algorithm
Empirical method
Multiobjective programming
Neural network
Modeling
Optimization
Mathematical programming
ISSN:0167-6377, 1872-7468
Online Access:Get full text
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0167-6377
1872-7468
DOI:10.1016/j.orl.2011.01.002