TY  - JOUR
PY  - 2020//
TI  - A benchmark dataset for ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue
JO  - Data in brief
A1  - Zhang, HongGuang
A1  - Liang, ZiHan
A1  - Liu, HuaJian
A1  - Wang, Rui
A1  - Liu, YuanAn
SP  - e105686
EP  - e105686
VL  - 31
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2352-3409
UR  - http://dx.doi.org/10.1016/j.dib.2020.105686
ID  - ref1
ER  -