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A benchmark dataset for ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue
This paper introduces a benchmark dataset to the research article entitled “Ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue - a case study of dynamic optimization problems”, by Zhang et al. [7]. Rescue ensemble that consists of rescue simulator and rescu...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7232107/ https://www.ncbi.nlm.nih.gov/pubmed/32435682 http://dx.doi.org/10.1016/j.dib.2020.105686 |
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author | Zhang, HongGuang Liang, ZiHan Liu, HuaJian Wang, Rui Liu, YuanAn |
author_facet | Zhang, HongGuang Liang, ZiHan Liu, HuaJian Wang, Rui Liu, YuanAn |
author_sort | Zhang, HongGuang |
collection | PubMed |
description | This paper introduces a benchmark dataset to the research article entitled “Ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue - a case study of dynamic optimization problems”, by Zhang et al. [7]. Rescue ensemble that consists of rescue simulator and rescue algorithm is characterized by supporting the dynamic simulation of forest fire rescue. The purpose of rescue algorithm is to minimize the longest flight time of aircraft group II and the newly-increased burnt forest cost in one period, simultaneously. The map information in our dataset is from Google map and relevant parameters are also from the actual situation data. The benchmark contains 10 different maps that researchers can use to evaluate their own algorithms and compare their performance with our algorithm. |
format | Online Article Text |
id | pubmed-7232107 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-72321072020-05-20 A benchmark dataset for ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue Zhang, HongGuang Liang, ZiHan Liu, HuaJian Wang, Rui Liu, YuanAn Data Brief Decision Science This paper introduces a benchmark dataset to the research article entitled “Ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue - a case study of dynamic optimization problems”, by Zhang et al. [7]. Rescue ensemble that consists of rescue simulator and rescue algorithm is characterized by supporting the dynamic simulation of forest fire rescue. The purpose of rescue algorithm is to minimize the longest flight time of aircraft group II and the newly-increased burnt forest cost in one period, simultaneously. The map information in our dataset is from Google map and relevant parameters are also from the actual situation data. The benchmark contains 10 different maps that researchers can use to evaluate their own algorithms and compare their performance with our algorithm. Elsevier 2020-05-13 /pmc/articles/PMC7232107/ /pubmed/32435682 http://dx.doi.org/10.1016/j.dib.2020.105686 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Decision Science Zhang, HongGuang Liang, ZiHan Liu, HuaJian Wang, Rui Liu, YuanAn A benchmark dataset for ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue |
title | A benchmark dataset for ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue |
title_full | A benchmark dataset for ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue |
title_fullStr | A benchmark dataset for ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue |
title_full_unstemmed | A benchmark dataset for ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue |
title_short | A benchmark dataset for ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue |
title_sort | benchmark dataset for ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue |
topic | Decision Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7232107/ https://www.ncbi.nlm.nih.gov/pubmed/32435682 http://dx.doi.org/10.1016/j.dib.2020.105686 |
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