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A New algorithm for adapting the configuration of subcomponents in large-scale optimization with cooperative coevolution

Trunfio, Giuseppe Andrea and Topa, Paweł and Wąs, Jarosław (2016) A New algorithm for adapting the configuration of subcomponents in large-scale optimization with cooperative coevolution. Information Sciences, Vol. 372 , p. 773-795. ISSN 0020-0255. Article.

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DOI: 10.1016/j.ins.2016.08.080

Abstract

The cooperative coevolutionary (CC) approach can be very effective in solving problems of large-scale continuous optimization (LSGO) through their decomposition into lower-dimensional subcomponents. However, it is well known that the CC performance can be significantly influenced by the adopted decomposition. Moreover, since the method may require evolving a number of populations, also the size of the latter can largely affect the optimization process. In this article, focusing on equally sized decompositions, we present the results of an in-depth investigation concerning the effects of both the size of populations and the dimensionality of subcomponents on the performance of a CC optimizer. According to our study, in several cases only a small set of suitable configurations corresponds to a high optimization performance. Furthermore, we propose a new CC algorithm in which part of the available computational budget is spent for adapting both the dimensionality of subcomponents and the number of evolved individuals during the optimization process. Using a rich set of benchmark problems, we show that the proposed approach can outperform a state-of-the art algorithm based on adaptive equally sized decompositions.

Item Type:Article
ID Code:11467
Status:Published
Refereed:Yes
Uncontrolled Keywords:Large scale optimization, cooperative coevolution, adaptation, differential evolution
Subjects:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 Sistemi di elaborazione delle informazioni
Divisions:001 Università di Sassari > 01-a Nuovi Dipartimenti dal 2012 > Architettura, Design e Urbanistica
Publisher:Elsevier
ISSN:0020-0255
Deposited On:19 Oct 2016 16:34

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