Immune-enhanced NSGA-II for multi-objective multi-UAV disaster relief material distribution

Abstract

In disaster-relief logistics, disrupted ground transportation networks and the limited payload and endurance of UAVs make it difficult to allocate emergency materials across geographically dispersed demand points. This study formulates a capacity-constrained bi-objective multi-UAV material distribution problem that simultaneously minimizes total flight distance and workload imbalance. Each demand point is served by one UAV in a single-trip route, and route feasibility is evaluated under payload-capacity and maximum route-distance constraints. To solve this constrained discrete optimization problem, we propose an immune-enhanced NSGA-II algorithm, referred to as INSGA-II. The algorithm represents each solution using an integer assignment vector and a priority vector for route decoding, and integrates immune cloning, stimulation-guided clone allocation, and a linearly decreasing mutation probability to improve the exploration of non-dominated allocation-routing solutions. Comparative experiments were conducted over 30 independent runs against standard NSGA-II, MOEA/D, Weighted-GA, and Weighted-ACO using hypervolume (HV), inverted generational distance (IGD), and runtime as evaluation metrics. INSGA-II achieved the highest mean HV of 0.895 and the lowest mean IGD of 0.184 among the compared algorithms, showing favorable average Pareto-front approximation performance in the tested scenario. Its average runtime was higher than NSGA-II and MOEA/D due to additional immune and repair operations, but lower than Weighted-GA and Weighted-ACO in the tested setting. The results suggest that INSGA-II provides quality-oriented Pareto trade-off solutions for capacity-constrained multi-UAV disaster-relief material distribution.