Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm Articles uri icon

publication date

  • agosto 2013

start page

  • 247

end page

  • 273

issue

  • 4

volume

  • 68

international standard serial number (ISSN)

  • 1012-2443

electronic international standard serial number (EISSN)

  • 1573-7470

abstract

  • The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.

keywords

  • multi-objective optimization; estimation of distribution algorithms; adaptive resonance theory; many-objective optimization; evolutionary algorithm; neural-network; hypervolume; model