Multi-Objective Optimization of Edge Server Placement in UAV Ad Hoc Networks

In low-altitude Internet of Things (IoT) systems, uncrewed aerial vehicles (UAVs) are increasingly deployed as agile and distributed platforms. In scenarios where infrastructure support is limited or unavailable, UAVs can form ad hoc networks that enable flexible and self-organizing communication, making them well-suited for delivering real-time edge intelligence. A key challenge in such UAV ad hoc networks lies in edge server placement to ensure low-latency data transmission while balancing the computation load among servers. In this paper, we provide insights into the design of UAV ad hoc network-assisted IoT systems by characterizing the trade-off between data transmission latency and computational load. To this end, we formulate a bi-objective optimization problem that jointly minimizes the worst-case transmission latency and the load imbalance, subject to constraints on edge UAV server selection and data assignment. To solve this problem, we propose a directed-evolution non-dominated sorting genetic algorithm (DNSGA) that removes conventional crossover operations and incorporates two problem-specific heuristics: (i) a K-means-based task assignment module to reduce latency, and (ii) an efficiency-driven load migration strategy to balance server-side workloads. Simulation results verify that the proposed DNSGA converges significantly faster than non-dominated sorting genetic algorithm II (NSGA-II), while providing a more diverse set of non-dominated solutions with up to 13.3% broader result range. Compared to multi-objective particle swarm optimization (MOPSO), DNSGA achieves up to 18.8% reduction in worst-case latency, and compared to NSGA-II, it reduces the load imbalance by up to 41.2%. These advantages highlight its effectiveness for high-efficiency UAV ad hoc network-assisted IoT systems.