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An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas
China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical mo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4578960/ https://www.ncbi.nlm.nih.gov/pubmed/26394148 http://dx.doi.org/10.1371/journal.pone.0137880 |
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author | Shao, Jing Yang, Lina Peng, Ling Chi, Tianhe Wang, Xiaomeng |
author_facet | Shao, Jing Yang, Lina Peng, Ling Chi, Tianhe Wang, Xiaomeng |
author_sort | Shao, Jing |
collection | PubMed |
description | China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC)-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining “replace” and “alter” operations, and the third is a “swap” strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China), and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing) was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures. |
format | Online Article Text |
id | pubmed-4578960 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-45789602015-10-01 An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas Shao, Jing Yang, Lina Peng, Ling Chi, Tianhe Wang, Xiaomeng PLoS One Research Article China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC)-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining “replace” and “alter” operations, and the third is a “swap” strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China), and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing) was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures. Public Library of Science 2015-09-22 /pmc/articles/PMC4578960/ /pubmed/26394148 http://dx.doi.org/10.1371/journal.pone.0137880 Text en © 2015 Shao et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Shao, Jing Yang, Lina Peng, Ling Chi, Tianhe Wang, Xiaomeng An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas |
title | An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas |
title_full | An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas |
title_fullStr | An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas |
title_full_unstemmed | An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas |
title_short | An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas |
title_sort | improved artificial bee colony-based approach for zoning protected ecological areas |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4578960/ https://www.ncbi.nlm.nih.gov/pubmed/26394148 http://dx.doi.org/10.1371/journal.pone.0137880 |
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