An Enhanced Multi-Objective Evolutionary Algorithm for Adaptive-Formation Multi-UAV Task Allocation with Load-Dependent Constraints

As the demand for logistics continues to surge, traditional ground delivery methods fall short, prompting the advent of low-altitude unmanned aerial vehicle (UAV) delivery as a revolutionary solution for rapid and adaptable logistics services. This paper introduces the Adaptive-Formation Multi-UAV Task Allocation with Load-Dependent Constraints (AF-MUTALC), an approach that transcends the Capacitated Vehicle Routing Problem (CVRP) framework with the distinctive operational constraints of UAVs, addressing the burgeoning need for efficient low-altitude delivery systems. Our proposed model integrates the impact of payload on UAV performance, encompassing constraints such as limited power, endurance, payload capacity, and the intricate interplay between flight dynamics and energy consumption. Additionally, the model contemplates the inherent uncertainties in transit times during actual UAV operations. To effectively manage this multi-objective optimization challenge, we employ an Enhanced Multi-Objective Evolutionary Algorithm (MOEA) that combines the strengths of Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a refined local search strategy. This approach aims to optimize resource utilization by minimizing the UAV fleet size and reducing the maximum flight time of a single UAV, thus ensuring an optimal balance between operational efficiency and resource allocation. Our algorithm has undergone rigorous validation through simulations, showcasing its ability to surpass conventional methodologies in delivering high-quality solutions. Furthermore, corroborated by empirical trials in real-world settings, we attest to the feasibility of the algorithm for real-life implementation.