Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, HongGuang, Liang, ZiHan, Liu, HuaJian, Wang, Rui, Liu, YuanAn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
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
_version_ 1783535311486189568
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
work_keys_str_mv AT zhanghongguang abenchmarkdatasetforensembleframeworkbyusingnatureinspiredalgorithmsfortheearlystageforestfirerescue
AT liangzihan abenchmarkdatasetforensembleframeworkbyusingnatureinspiredalgorithmsfortheearlystageforestfirerescue
AT liuhuajian abenchmarkdatasetforensembleframeworkbyusingnatureinspiredalgorithmsfortheearlystageforestfirerescue
AT wangrui abenchmarkdatasetforensembleframeworkbyusingnatureinspiredalgorithmsfortheearlystageforestfirerescue
AT liuyuanan abenchmarkdatasetforensembleframeworkbyusingnatureinspiredalgorithmsfortheearlystageforestfirerescue
AT zhanghongguang benchmarkdatasetforensembleframeworkbyusingnatureinspiredalgorithmsfortheearlystageforestfirerescue
AT liangzihan benchmarkdatasetforensembleframeworkbyusingnatureinspiredalgorithmsfortheearlystageforestfirerescue
AT liuhuajian benchmarkdatasetforensembleframeworkbyusingnatureinspiredalgorithmsfortheearlystageforestfirerescue
AT wangrui benchmarkdatasetforensembleframeworkbyusingnatureinspiredalgorithmsfortheearlystageforestfirerescue
AT liuyuanan benchmarkdatasetforensembleframeworkbyusingnatureinspiredalgorithmsfortheearlystageforestfirerescue