The article introduces the Multi-Objective Shuffled Gray-Wolf Frog Leaping Model (MSGW-FLM) for effective resource allocation in emergency rescue scenarios. Leveraging IoT and spatio-temporal data analytics, the model outperforms established models like NSGA-II, IBEA, and MOEA/D. It addresses complex multi-cycle emergency rescue scenarios with numerous constraints and objectives. The study emphasizes the importance of data-driven decision-making in optimizing resource distribution during emergencies. By combining Grey Wolf Optimization Algorithm (GWOA) and Shuffled Frog Leaping Algorithm (SFLA), MSGW-FLM offers a novel approach to dynamic multi-cycle emergency response planning.
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by Xinrun Xu,Zh... at arxiv.org 03-18-2024
https://arxiv.org/pdf/2403.10299.pdfDeeper Inquiries