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A self-adaptive binary differential evolution algorithm for large scale binary optimization problems

This study proposes a new self-adaptive binary variant of a differential evolution algorithm, based on measure of dissimilarity and named SabDE. It uses an adaptive mechanism for selecting how new trial solutions are generated, and a chaotic process for adapting parameter values. SabDE is compared a...

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Main Authors: Banitalebi, A., Aziz, M. I. A., Aziz, Z. A.
Format: Article
Published: Elsevier Inc. 2016
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/71960/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975795338&doi=10.1016%2fj.ins.2016.05.037&partnerID=40&md5=1d8f502e2d8139abbf69da217b115b32
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id my.utm.71960
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spelling my.utm.719602017-11-23T06:19:25Z http://eprints.utm.my/id/eprint/71960/ A self-adaptive binary differential evolution algorithm for large scale binary optimization problems Banitalebi, A. Aziz, M. I. A. Aziz, Z. A. QA Mathematics This study proposes a new self-adaptive binary variant of a differential evolution algorithm, based on measure of dissimilarity and named SabDE. It uses an adaptive mechanism for selecting how new trial solutions are generated, and a chaotic process for adapting parameter values. SabDE is compared against a number of existing state of the art algorithms, on a set of benchmark problems including high dimensional knapsack problems with up to 10,000 dimensions as well as on the 15 learning based problems of the Congress on Evolutionary Computation (CEC 2015). Experimental results reveal that the proposed algorithm performs competitively and in some cases is superior to the existing algorithms. Elsevier Inc. 2016 Article PeerReviewed Banitalebi, A. and Aziz, M. I. A. and Aziz, Z. A. (2016) A self-adaptive binary differential evolution algorithm for large scale binary optimization problems. Information Sciences, 367-36 . pp. 487-511. ISSN 0020-0255 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975795338&doi=10.1016%2fj.ins.2016.05.037&partnerID=40&md5=1d8f502e2d8139abbf69da217b115b32
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Banitalebi, A.
Aziz, M. I. A.
Aziz, Z. A.
A self-adaptive binary differential evolution algorithm for large scale binary optimization problems
description This study proposes a new self-adaptive binary variant of a differential evolution algorithm, based on measure of dissimilarity and named SabDE. It uses an adaptive mechanism for selecting how new trial solutions are generated, and a chaotic process for adapting parameter values. SabDE is compared against a number of existing state of the art algorithms, on a set of benchmark problems including high dimensional knapsack problems with up to 10,000 dimensions as well as on the 15 learning based problems of the Congress on Evolutionary Computation (CEC 2015). Experimental results reveal that the proposed algorithm performs competitively and in some cases is superior to the existing algorithms.
format Article
author Banitalebi, A.
Aziz, M. I. A.
Aziz, Z. A.
author_facet Banitalebi, A.
Aziz, M. I. A.
Aziz, Z. A.
author_sort Banitalebi, A.
title A self-adaptive binary differential evolution algorithm for large scale binary optimization problems
title_short A self-adaptive binary differential evolution algorithm for large scale binary optimization problems
title_full A self-adaptive binary differential evolution algorithm for large scale binary optimization problems
title_fullStr A self-adaptive binary differential evolution algorithm for large scale binary optimization problems
title_full_unstemmed A self-adaptive binary differential evolution algorithm for large scale binary optimization problems
title_sort self-adaptive binary differential evolution algorithm for large scale binary optimization problems
publisher Elsevier Inc.
publishDate 2016
url http://eprints.utm.my/id/eprint/71960/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975795338&doi=10.1016%2fj.ins.2016.05.037&partnerID=40&md5=1d8f502e2d8139abbf69da217b115b32
_version_ 1643656323316842496
score 13.211869

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