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

publication date

  • March 2011

start page

  • 150

end page

  • 154

issue

  • 2

volume

  • 39

international standard serial number (ISSN)

  • 0167-6377

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.

keywords

  • multi-objective optimization; estimation of distribution algorithm; model building; growing neural gas