An improved fusion crossover genetic algorithm for a time-weighted maximal covering location problem for sensor siting under satellite-borne monitoring

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 GAs 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.