Aug 2026· Frontiers in Future Transportation· Vol 7· 0 citations· 30 references
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.
A hybrid heuristic population initialization strategy combining emergency-order priority and spatial scanning rules is introduced to increase the proportion of high-quality feasible solutions in the initial population, and the synergistic effects of the four improvement mechanisms effectively enhance the multi-objective optimization performance and the quality of the nondominated solution set.
In temporary emergency communication coverage scenarios where terrestrial communication infrastructure is damaged or lacks sufficient capacity, UAVs equipped with base stations have emerged as an effective solution due to their flexible deployment and rapid response capability. However, in multi-UAV networks, the three-dimensional deployment of UAVs significantly affects air-to-ground link quality, while power allocation further determines the level of system interference and throughput performance. To address this issue, this paper considers a multi-UAV communication system and jointly takes into account user link reliability and service requirement satisfaction, thereby establishing a joint optimization model for QoS-constrained coverage and network throughput. To address the non-convex joint optimization problem, a problem-tailored dual-population cooperative NSGA-II framework, termed IDPC-NSGA-II, is developed. By coupling dual-population evolution, adaptive mutation, uncovered-user-guided local search, and interference-aware repair with the characteristics of multi-UAV emergency communications, the proposed method improves the trade-off between QoS-constrained coverage and network throughput. Simulation results in a representative emergency communication scenario show that the proposed method achieves a favorable trade-off between QoS-constrained coverage and throughput, and outperforms the compared algorithms under the considered network setting.
Gui-Fen Chen, Ruiyang Liu· Digital Signal and Computer...· 0 citations
The increasing integration of Unmanned Aerial Vehicles (UAVs) into US Army logistics operations has introduced complex multi-objective scheduling challenges that conventional optimization methods struggle to address efficiently. This study presents a metaheuristic-based optimization framework for scheduling fleets of military UAVs tasked with last-mile resupply missions in dynamic, contested operational environments. A hybrid approach combining a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) is proposed to minimize total mission completion time, fuel consumption, and operational risk while satisfying strict military delivery constraints. The model incorporates stochastic demand, no-fly-zone avoidance, payload capacity limitations, and UAV endurance parameters derived from US Army field logistics doctrine. Computational experiments were conducted on simulated battlefield scenarios with fleet sizes ranging from 5 to 50 UAVs across terrain grids representing forward operating bases. The proposed GA-ACO hybrid achieved an average improvement of 23.4% in mission completion time and 18.7% in fuel efficiency compared with single-algorithm baselines, while reducing the constraint violation rate to 1.8%. The framework also demonstrated superior adaptability to real-time route re-planning under dynamic threat scenarios and converged within the 10-minute Army tactical planning window for fleets of up to 50 UAVs. These findings suggest that hybrid metaheuristic optimization offers a robust and scalable approach to multi-UAV mission scheduling, with significant implications for enhancing Army supply-chain agility and operational readiness.
A. Barrie· Global Journal of Engineerin...· 0 citations
A QoS-aware joint optimization model for UAV deployment, integrating air-to-ground (A2G) channel modeling with resource allocation, where upper-level position optimization is coordinated with lower-level frequency allocation and power control through a hierarchical decomposition strategy is developed.
Chaofeng Wang, Longfei Zhang, Jie Luo et al.· Drones· 0 citations
Cooperative jamming task allocation for UAV swarms must jointly consider radar priority, the effectiveness of different jamming modes, and heterogeneous resource limits. This paper establishes a radar threat assessment model from radar operating parameters and evaluates each candidate jamming assignment in the time, frequency, and power domains. Based on these evaluations, a binary integer programming model is formulated to jointly determine radar selection, UAV assignment, and jamming mode under coverage, capacity, and mode availability constraints. To solve the model, an improved multi-population genetic algorithm (IMPGA) is developed using crossover on radar task blocks, mutation at the task level, stochastic feasibility repair, and cooperative evolution among multiple subpopulations. Experimental comparisons are conducted under identical function evaluation budgets, and each representative scenario is evaluated through 100 independent Monte Carlo runs. The results show that the IMPGA reliably obtains exact or near-optimal solutions and provides improved solution quality and consistency, particularly as the problem scale and resource coupling increase. The scalability experiments further demonstrate that the algorithm maintains small optimality gaps in larger instances. Ablation results confirm that the radar task operators and stochastic repair make important contributions to the final solution quality and convergence process.
Nan Sun, Xin Zhao, Bing He et al.· Drones· 0 citations
In post-disaster environments, the failure of terrestrial communication infrastructure necessitates the rapid deployment of unmanned aerial vehicles (UAVs) as aerial base stations to restore wireless connectivity. This paper addresses the joint UAV activation-and-placement problem in continuous space, with the objective of minimizing the number of deployed UAVs while satisfying coverage and minimum-separation constraints. To solve this problem, we propose a Hybrid K-means Quantum-Inspired Evolutionary Algorithm (HKQEA) that combines K-means-guided initialization, a calibrated penalty-based feasibility objective, non-elitist evolutionary search, and a quantum-inspired learning update. Experimental results over 50 independent runs show that HKQEA attains a best fully feasible solution with 8 UAVs, while achieving average values of 98.94% for coverage, 99.94% for non-overlap, and 99.68% for minimum-distance satisfaction. Comparative evaluation against standard Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization algorithm (PSO) and an elitist variant of HKQEA further shows that the proposed method provides a more favorable balance among exploration, convergence behavior, and reliable feasibility preservation in constrained deployment problems. An illustrative procurement-level cost analysis also indicates that reducing the fleet from 10 UAVs to 8 can yield a 20% reduction in hardware count, corresponding to a simplified savings ratio of 25% for the studied deployment setting. These results demonstrate the potential of the proposed framework for resource-efficient post-disaster communication restoration.
Fatima Azzahraa Amarcha, Lahcen Hassine, R. Saadane et al.· Journal of King Saud Univers...· 0 citations
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