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Review and analysis for the Red Deer Algorithm

In this paper, the Red Deer algorithm (RDA), a recent population-based meta-heuristic algorithm, is thoroughly reviewed. The RD algorithm combines the survival of the fittest principle from the evolutionary algorithms and the productivity and richness of heuristic search techniques. Different varian...

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Detalles Bibliográficos
Autores principales: Zitar, Raed Abu, Abualigah, Laith, Al-Dmour, Nidal A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8605784/
https://www.ncbi.nlm.nih.gov/pubmed/34840618
http://dx.doi.org/10.1007/s12652-021-03602-1
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author Zitar, Raed Abu
Abualigah, Laith
Al-Dmour, Nidal A.
author_facet Zitar, Raed Abu
Abualigah, Laith
Al-Dmour, Nidal A.
author_sort Zitar, Raed Abu
collection PubMed
description In this paper, the Red Deer algorithm (RDA), a recent population-based meta-heuristic algorithm, is thoroughly reviewed. The RD algorithm combines the survival of the fittest principle from the evolutionary algorithms and the productivity and richness of heuristic search techniques. Different variants and hybrids of this algorithm are presented and investigated. All the applications that were solved with this algorithm are presented. It is crucial to analyze the performance of this algorithm, therefore, the paper sheds light on the algorithm unique features and weaknesses covering the applications that are primarily suitable for it. The conclusions are presented, and further recommendations are suggested based on the review and analysis covered. The readers of this paper will have an understanding of the RD algorithm and its variants and, consequently, decide how suitable this algorithm is for their own business, research, or industrial applications.
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spelling pubmed-86057842021-11-22 Review and analysis for the Red Deer Algorithm Zitar, Raed Abu Abualigah, Laith Al-Dmour, Nidal A. J Ambient Intell Humaniz Comput Original Research In this paper, the Red Deer algorithm (RDA), a recent population-based meta-heuristic algorithm, is thoroughly reviewed. The RD algorithm combines the survival of the fittest principle from the evolutionary algorithms and the productivity and richness of heuristic search techniques. Different variants and hybrids of this algorithm are presented and investigated. All the applications that were solved with this algorithm are presented. It is crucial to analyze the performance of this algorithm, therefore, the paper sheds light on the algorithm unique features and weaknesses covering the applications that are primarily suitable for it. The conclusions are presented, and further recommendations are suggested based on the review and analysis covered. The readers of this paper will have an understanding of the RD algorithm and its variants and, consequently, decide how suitable this algorithm is for their own business, research, or industrial applications. Springer Berlin Heidelberg 2021-11-20 2023 /pmc/articles/PMC8605784/ /pubmed/34840618 http://dx.doi.org/10.1007/s12652-021-03602-1 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Zitar, Raed Abu
Abualigah, Laith
Al-Dmour, Nidal A.
Review and analysis for the Red Deer Algorithm
title Review and analysis for the Red Deer Algorithm
title_full Review and analysis for the Red Deer Algorithm
title_fullStr Review and analysis for the Red Deer Algorithm
title_full_unstemmed Review and analysis for the Red Deer Algorithm
title_short Review and analysis for the Red Deer Algorithm
title_sort review and analysis for the red deer algorithm
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8605784/
https://www.ncbi.nlm.nih.gov/pubmed/34840618
http://dx.doi.org/10.1007/s12652-021-03602-1
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