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首页> 外文期刊>Computers & geosciences >An improved fusion crossover genetic algorithm for a time-weighted maximal covering location problem for sensor siting under satellite-borne monitoring
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An improved fusion crossover genetic algorithm for a time-weighted maximal covering location problem for sensor siting under satellite-borne monitoring

机译：卫星监测下传感器选址的时间加权最大覆盖位置问题的改进融合交叉遗传算法

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摘要

Traditional location problems usually focus on spatial and temporal impacts of facilities, but few studies have focused on sensor siting under satellite-borne monitoring in a space-ground integrated sensor network. Given the partial coverage and the requirement for continuous coverage in space and time, a time-weighted maximal covering location problem with partial coverage (TMCLP-PC) model is introduced. This model is solved using a modified genetic algorithm (GA)-based approach that utilizes the spatio-temporal characteristics of potential facility sites for faster convergence. The performance of the new GA is tested on a precipitation station siting problem in the Jinsha River Basin on the upper reaches of the Yangtze River in China. The results demonstrate that the proposed GA can significantly reduce the computation time compared with CPLEX, a commercial integer programming solver, and can outperform the greedy algorithm and the GM with one-point, two-point, fusion, and uniform crossover operators. The applicability of the proposed method and exploration of the design in the new GA are also discussed.
机译：传统的位置问题通常集中在设施的时空影响上，但是很少有研究集中在空地一体化传感器网络中卫星监测下的传感器选址。考虑到部分覆盖以及在空间和时间上连续覆盖的要求，引入了具有部分覆盖的时间加权最大覆盖位置问题（TMCLP-PC）模型。使用改进的基于遗传算法（GA）的方法解决了该模型，该方法利用了潜在设施场所的时空特性来加快收敛速度​​。在中国长江上游金沙江流域的降水站选址问题上测试了新遗传算法的性能。结果表明，与商用整数规划求解器CPLEX相比，所提出的GA可以显着减少计算时间，并且通过单点，两点，融合和均匀交叉算子可以优于贪婪算法和GM。还讨论了所提出方法的适用性和在新遗传算法中的设计探索。

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