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An Improved Cockroach Swarm Optimization
Hunger component is introduced to the existing cockroach swarm optimization (CSO) algorithm to improve its searching ability and population diversity. The original CSO was modelled with three components: chase-swarming, dispersion, and ruthless; additional hunger component which is modelled using pa...
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/PMC4052085/ https://www.ncbi.nlm.nih.gov/pubmed/24959611 http://dx.doi.org/10.1155/2014/375358 |
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author | Obagbuwa, I. C. Adewumi, A. O. |
author_facet | Obagbuwa, I. C. Adewumi, A. O. |
author_sort | Obagbuwa, I. C. |
collection | PubMed |
description | Hunger component is introduced to the existing cockroach swarm optimization (CSO) algorithm to improve its searching ability and population diversity. The original CSO was modelled with three components: chase-swarming, dispersion, and ruthless; additional hunger component which is modelled using partial differential equation (PDE) method is included in this paper. An improved cockroach swarm optimization (ICSO) is proposed in this paper. The performance of the proposed algorithm is tested on well known benchmarks and compared with the existing CSO, modified cockroach swarm optimization (MCSO), roach infestation optimization RIO, and hungry roach infestation optimization (HRIO). The comparison results show clearly that the proposed algorithm outperforms the existing algorithms. |
format | Online Article Text |
id | pubmed-4052085 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-40520852014-06-23 An Improved Cockroach Swarm Optimization Obagbuwa, I. C. Adewumi, A. O. ScientificWorldJournal Research Article Hunger component is introduced to the existing cockroach swarm optimization (CSO) algorithm to improve its searching ability and population diversity. The original CSO was modelled with three components: chase-swarming, dispersion, and ruthless; additional hunger component which is modelled using partial differential equation (PDE) method is included in this paper. An improved cockroach swarm optimization (ICSO) is proposed in this paper. The performance of the proposed algorithm is tested on well known benchmarks and compared with the existing CSO, modified cockroach swarm optimization (MCSO), roach infestation optimization RIO, and hungry roach infestation optimization (HRIO). The comparison results show clearly that the proposed algorithm outperforms the existing algorithms. Hindawi Publishing Corporation 2014 2014-05-14 /pmc/articles/PMC4052085/ /pubmed/24959611 http://dx.doi.org/10.1155/2014/375358 Text en Copyright © 2014 I. C. Obagbuwa and A. O. Adewumi. 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 Obagbuwa, I. C. Adewumi, A. O. An Improved Cockroach Swarm Optimization |
title | An Improved Cockroach Swarm Optimization |
title_full | An Improved Cockroach Swarm Optimization |
title_fullStr | An Improved Cockroach Swarm Optimization |
title_full_unstemmed | An Improved Cockroach Swarm Optimization |
title_short | An Improved Cockroach Swarm Optimization |
title_sort | improved cockroach swarm optimization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4052085/ https://www.ncbi.nlm.nih.gov/pubmed/24959611 http://dx.doi.org/10.1155/2014/375358 |
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