Szczegóły publikacji

Opis bibliograficzny

A new algorithm for adapting the configuration of subcomponents in large-scale optimization with cooperative coevolution / Giuseppe A. Trunfio, Paweł TOPA, Jarosław WĄS // Information Sciences ; ISSN 0020-0255. — 2016 — vol. 372, s. 773–795. — Bibliogr. s. 794–795, Abstr. — Publikacja dostępna online od: 2016-08-28


Autorzy (3)


Słowa kluczowe

differential evolutionlarge scale optimizationadaptationcooperative coevolution

Dane bibliometryczne

ID BaDAP101226
Data dodania do BaDAP2016-10-15
Tekst źródłowyURL
DOI10.1016/j.ins.2016.08.080
Rok publikacji2016
Typ publikacjiartykuł w czasopiśmie
Otwarty dostęptak
Czasopismo/seriaInformation Sciences

Abstract

The cooperative coevolutionary (CC) approach can be very effective in solving problems of large-scale continuous optimization (LSGO) through their decomposition into lowerdimensional 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. © 2016 Elsevier Inc. All rights reserved.

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