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

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URI: http://hdl.handle.net/10016/18425
ISSN: 0167-6377
DOI: 10.1016/j.orl.2011.01.002
UXXI: AR/0000009127
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addressing_ORL_2011_ps.pdf (530.03 KB)
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2011-03
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Elsevier
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Abstract
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.
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Multi-objective Optimization, Estimation of Distribution Algorithms, Model building, Growing neural gas
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Operations Research Letters (2011), 39 (2), pp. 150-154
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