Multi-objective SMA for Medical Supply Delivery Using Drones and Generative AI in Post-disasters

As natural disasters rise, traditional infrastructure may be damaged or ineffective, complicating the delivery of medical supplies. Using drones to deliver these supplies to affected areas is increasingly seen as an effective and necessary solution. However, managing drones under these constraints quickly becomes a complex problem requiring intelligent methods. This paper presents a novel approach to address the Medical Supply Drone Delivery (MSDD) problem in post-disaster scenarios by modeling it as a multi-objective optimization problem. We introduce the Slime Mould Algorithm (SMA), designed to optimize drone management during post-disaster situations. Using generative AI techniques, specifically ChatGPT, we augment limited COVID-19 datasets from Algeria to create realistic scenarios. We compare the results of the proposed approach with those obtained using advanced versions of the Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) algorithms. The analysis of the experiment indicates that the proposed multi-objective SMA for the MSDD issue is highly competitive and provides significant performance enhancements.