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Nature-Inspired Chemical Reaction Optimisation Algorithms
Nature-inspired meta-heuristic algorithms have dominated the scientific literature in the areas of machine learning and cognitive computing paradigm in the last three decades. Chemical reaction optimisation (CRO) is a population-based meta-heuristic algorithm based on the principles of chemical reac...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5552861/ https://www.ncbi.nlm.nih.gov/pubmed/28845200 http://dx.doi.org/10.1007/s12559-017-9485-1 |
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author | Siddique, Nazmul Adeli, Hojjat |
author_facet | Siddique, Nazmul Adeli, Hojjat |
author_sort | Siddique, Nazmul |
collection | PubMed |
description | Nature-inspired meta-heuristic algorithms have dominated the scientific literature in the areas of machine learning and cognitive computing paradigm in the last three decades. Chemical reaction optimisation (CRO) is a population-based meta-heuristic algorithm based on the principles of chemical reaction. A chemical reaction is seen as a process of transforming the reactants (or molecules) through a sequence of reactions into products. This process of transformation is implemented in the CRO algorithm to solve optimisation problems. This article starts with an overview of the chemical reactions and how it is applied to the optimisation problem. A review of CRO and its variants is presented in the paper. Guidelines from the literature on the effective choice of CRO parameters for solution of optimisation problems are summarised. |
format | Online Article Text |
id | pubmed-5552861 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-55528612017-08-25 Nature-Inspired Chemical Reaction Optimisation Algorithms Siddique, Nazmul Adeli, Hojjat Cognit Comput Article Nature-inspired meta-heuristic algorithms have dominated the scientific literature in the areas of machine learning and cognitive computing paradigm in the last three decades. Chemical reaction optimisation (CRO) is a population-based meta-heuristic algorithm based on the principles of chemical reaction. A chemical reaction is seen as a process of transforming the reactants (or molecules) through a sequence of reactions into products. This process of transformation is implemented in the CRO algorithm to solve optimisation problems. This article starts with an overview of the chemical reactions and how it is applied to the optimisation problem. A review of CRO and its variants is presented in the paper. Guidelines from the literature on the effective choice of CRO parameters for solution of optimisation problems are summarised. Springer US 2017-06-17 2017 /pmc/articles/PMC5552861/ /pubmed/28845200 http://dx.doi.org/10.1007/s12559-017-9485-1 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Siddique, Nazmul Adeli, Hojjat Nature-Inspired Chemical Reaction Optimisation Algorithms |
title | Nature-Inspired Chemical Reaction Optimisation Algorithms |
title_full | Nature-Inspired Chemical Reaction Optimisation Algorithms |
title_fullStr | Nature-Inspired Chemical Reaction Optimisation Algorithms |
title_full_unstemmed | Nature-Inspired Chemical Reaction Optimisation Algorithms |
title_short | Nature-Inspired Chemical Reaction Optimisation Algorithms |
title_sort | nature-inspired chemical reaction optimisation algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5552861/ https://www.ncbi.nlm.nih.gov/pubmed/28845200 http://dx.doi.org/10.1007/s12559-017-9485-1 |
work_keys_str_mv | AT siddiquenazmul natureinspiredchemicalreactionoptimisationalgorithms AT adelihojjat natureinspiredchemicalreactionoptimisationalgorithms |