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A benchmark dataset for ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue

机译:通过使用自然启发算法,为早期森林火灾救援使用自然启发算法的基准数据集

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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. . 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.
机译:本文介绍了一个标题的研究文章的基准数据集“通过使用自然灵感算法为早期森林火灾救援的自然启发算法 - 动态优化问题的案例研究”,Zhang等人。 。由救援模拟器和救援算法组成的救援集合是通过支持森林火灾救援的动态模拟来特征。救援算法的目的是最大限度地减少飞机组II的最长飞行时间,并同时在一个时期的新增燃烧的森林成本。我们数据集中的地图信息来自Google地图,相关参数也来自实际情况数据。基准测试人员可以使用10个不同的地图来评估自己的算法并使用我们的算法进行比较它们的性能。

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