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About this paper
Xinrun Xu, Zhanbiao Lian, Yurong Wu, Manying Lv, Zhiming Ding, Jian Yan, Shang Jiang
A hybrid optimization algorithm, the Multi-Objective Shuffled Gray-Wolf Frog Leaping Model (MSGW-FLM), is presented for dynamic emergency resource allocation in IoT environments. This algorithm reduced system loss faster and improved resource utilization in simulated disaster scenarios compared to established multi-objective algorithms.
Key insights
- Hybridizing the Grey Wolf Optimizer (GWO) and the Shuffled Frog Leaping Algorithm (SFLA) effectively combines their strengths for multi-objective optimization, leveraging GWO's exploration and SFLA's local search capabilities.
- Integrating mechanisms like NSGA-II ranking, Levy flight strategy, and memeplex structures enhances solution diversity and convergence speed in complex, dynamic problem landscapes.
- A rolling time-domain planning approach, continuously updated with real-time IoT data, allows for adaptive and efficient resource reallocation in highly dynamic emergency scenarios.
Summary by alphaXiv.
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