A Regional Multi-objective Linear Programming Model for Initial Post-earthquake and Comparison of Three Meta-heuristic Algorithms
作者： Jinxing Zhao , Xiaoqing Long
DOI: 10.1109/ICCSSE50399.2020.9171983
关键词:
摘要: The earthquake, as one of the most harmful natural disasters to humanity, has been concerned for a long time. Considering some areas with underdeveloped economic and medical standards, it is great significance study regional post-earthquake rescue problem. In paper, multi-objective linear programming model (MLPM) initial stage designed according different degree road loss, demand materials number injured due difference population base on each disaster point in postearthquake period, method vehicle deployment. And solved by three introduced meta-heuristic algorithms which are ant colony optimization algorithm (ACOA), particle swarm (PSOA), tabu search (TSA). Through numerical experiments, feasibility verified, performances about solutions running time solving compared analyzed. feasible optimize model. solution obtained ACOA optimal, TSA shortest Under same ants particles, PSOA runs faster than ACOA, but superior PSOA.
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