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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...

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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
Descripción
Sumario: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.