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An integrated cuckoo search optimizer for single and multi-objective optimization problems

Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. This article proposes an integrated cuckoo search optimizer (ICSO) for single objective optimization problems, which incorporates the multiple strategies into th...

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Detalles Bibliográficos
Autores principales: Qi, Xiangbo, Yuan, Zhonghu, Song, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959647/
https://www.ncbi.nlm.nih.gov/pubmed/33817020
http://dx.doi.org/10.7717/peerj-cs.370
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author Qi, Xiangbo
Yuan, Zhonghu
Song, Yan
author_facet Qi, Xiangbo
Yuan, Zhonghu
Song, Yan
author_sort Qi, Xiangbo
collection PubMed
description Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. This article proposes an integrated cuckoo search optimizer (ICSO) for single objective optimization problems, which incorporates the multiple strategies into the cuckoo search (CS) algorithm. The paper also considers the proposal of multi-objective versions of ICSO called MOICSO. The two algorithms presented in this paper are benchmarked by a set of benchmark functions. The comprehensive analysis of the experimental results based on the considered test problems and comparisons with other recent methods illustrate the effectiveness of the proposed integrated mechanism of different search strategies and demonstrate the performance superiority of the proposed algorithm.
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spelling pubmed-79596472021-04-02 An integrated cuckoo search optimizer for single and multi-objective optimization problems Qi, Xiangbo Yuan, Zhonghu Song, Yan PeerJ Comput Sci Algorithms and Analysis of Algorithms Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. This article proposes an integrated cuckoo search optimizer (ICSO) for single objective optimization problems, which incorporates the multiple strategies into the cuckoo search (CS) algorithm. The paper also considers the proposal of multi-objective versions of ICSO called MOICSO. The two algorithms presented in this paper are benchmarked by a set of benchmark functions. The comprehensive analysis of the experimental results based on the considered test problems and comparisons with other recent methods illustrate the effectiveness of the proposed integrated mechanism of different search strategies and demonstrate the performance superiority of the proposed algorithm. PeerJ Inc. 2021-03-11 /pmc/articles/PMC7959647/ /pubmed/33817020 http://dx.doi.org/10.7717/peerj-cs.370 Text en © 2021 Qi et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Qi, Xiangbo
Yuan, Zhonghu
Song, Yan
An integrated cuckoo search optimizer for single and multi-objective optimization problems
title An integrated cuckoo search optimizer for single and multi-objective optimization problems
title_full An integrated cuckoo search optimizer for single and multi-objective optimization problems
title_fullStr An integrated cuckoo search optimizer for single and multi-objective optimization problems
title_full_unstemmed An integrated cuckoo search optimizer for single and multi-objective optimization problems
title_short An integrated cuckoo search optimizer for single and multi-objective optimization problems
title_sort integrated cuckoo search optimizer for single and multi-objective optimization problems
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959647/
https://www.ncbi.nlm.nih.gov/pubmed/33817020
http://dx.doi.org/10.7717/peerj-cs.370
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