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The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems
This paper introduces an expanded version of the Invasive Weed Optimization algorithm (exIWO) distinguished by the hybrid strategy of the search space exploration proposed by the authors. The algorithm is evaluated by solving three well-known optimization problems: minimization of numerical function...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3977471/ https://www.ncbi.nlm.nih.gov/pubmed/24955420 http://dx.doi.org/10.1155/2014/831691 |
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author | Josiński, Henryk Kostrzewa, Daniel Michalczuk, Agnieszka Świtoński, Adam |
author_facet | Josiński, Henryk Kostrzewa, Daniel Michalczuk, Agnieszka Świtoński, Adam |
author_sort | Josiński, Henryk |
collection | PubMed |
description | This paper introduces an expanded version of the Invasive Weed Optimization algorithm (exIWO) distinguished by the hybrid strategy of the search space exploration proposed by the authors. The algorithm is evaluated by solving three well-known optimization problems: minimization of numerical functions, feature selection, and the Mona Lisa TSP Challenge as one of the instances of the traveling salesman problem. The achieved results are compared with analogous outcomes produced by other optimization methods reported in the literature. |
format | Online Article Text |
id | pubmed-3977471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-39774712014-06-22 The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems Josiński, Henryk Kostrzewa, Daniel Michalczuk, Agnieszka Świtoński, Adam ScientificWorldJournal Research Article This paper introduces an expanded version of the Invasive Weed Optimization algorithm (exIWO) distinguished by the hybrid strategy of the search space exploration proposed by the authors. The algorithm is evaluated by solving three well-known optimization problems: minimization of numerical functions, feature selection, and the Mona Lisa TSP Challenge as one of the instances of the traveling salesman problem. The achieved results are compared with analogous outcomes produced by other optimization methods reported in the literature. Hindawi Publishing Corporation 2014 2014-03-19 /pmc/articles/PMC3977471/ /pubmed/24955420 http://dx.doi.org/10.1155/2014/831691 Text en Copyright © 2014 Henryk Josiński et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Josiński, Henryk Kostrzewa, Daniel Michalczuk, Agnieszka Świtoński, Adam The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems |
title | The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems |
title_full | The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems |
title_fullStr | The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems |
title_full_unstemmed | The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems |
title_short | The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems |
title_sort | expanded invasive weed optimization metaheuristic for solving continuous and discrete optimization problems |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3977471/ https://www.ncbi.nlm.nih.gov/pubmed/24955420 http://dx.doi.org/10.1155/2014/831691 |
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