Sep 2026· IEEE Internet of Things Journal· Vol 13, pp. 41177-41194· 0 citations· 51 references
Abstract
This article investigates the dynamic multiobjective co-optimization problem in unmanned aerial vehicle (UAV)-assisted remote sensing systems, aiming to jointly optimize UAV placement, task scheduling strategies, and computing/communication resource allocation to minimize the system’s average processing latency and the total energy consumption of UAVs. Addressing the shortcomings of existing research, which often overlooks the computational capabilities of UAVs, optimizes only a single aspect, and fails to account for environmental dynamics, this work formulates the problem as a dynamic multiobjective optimization problem. A hybrid optimization framework named DSG, integrating swarm intelligence and evolutionary algorithms, is proposed. The framework first derives a closed-form optimal resource allocation solution for given deployment and scheduling strategies through theoretical analysis. It then employs an improved dynamic multiobjective evolutionary algorithm (DMOEA) to co-optimize UAV positions (continuous variables) and task scheduling (discrete variables). Experimental results demonstrate that DSG achieves significantly better normalized hypervolume performance than comparative algorithms across various system scales [number of UAVs, access points (APs), and sensors] while exhibiting good stability and scalability. This provides an effective solution for the efficient co-optimization of UAV-assisted edge computing in dynamic environments.
To address the fundamental trade-off between real-time responsiveness to high-priority missions and long-term overall economic efficiency of the system in multi-UAV dynamic task assignment, we propose a hybrid intelligent scheduling algorithm abbreviated as DPSO-Greedy. The algorithm performs periodic global batch optimization for regular orders using an improved discrete particle swarm optimization (DPSO) method, and realizes instantaneous allocation of emergency orders via an adaptive multi-factor Greedy strategy, thus enabling efficient collaborative processing of differentiated tasks. Targeting the trade-off between real-time response and long-term system efficiency, this paper proposes a hybrid DPSO-Greedy algorithm with decoupled task scheduling mechanisms. Comparative simulation results demonstrate that compared with mainstream metaheuristic algorithms (Greedy, SSA, GWO and RHS), the proposed method reduces the average response time of emergency orders by 33.2–68.2%, achieves an emergency order completion rate exceeding 90%, and improves system load balancing performance by 24–35% in dynamic scenarios characterized by burst and tidal demands. This study provides a promising solution for dynamic UAV assignment problems and offers valuable insights for a broader range of real-time resource collaborative decision-making applications.
Mei You, Huihui Xu, Zhangsong Shi et al.· Drones· 0 citations
To address the challenges of collaborative task allocation and path planning for multiple logistics unmanned aerial vehicles (UAVs) in urban low-altitude environments, this paper proposes a bilevel nested joint optimization method based on reinforcement learning and a graph search algorithm to enhance the efficiency of collaborative last-mile delivery by multiple logistics UAVs while reducing flight risks. The proposed method constructs a bilevel architecture system based on a task allocation and decision-making model and a path planning model. The upper-level model holistically considers the demands of three stakeholders—government (safety), customers (timeliness), and UAV enterprises (economy)—at the macro level. Then, based on real-time order information and UAV status, a multi-objective optimization and constraint model is constructed under complex dynamic environments. A multi-agent proximal policy optimization algorithm is employed to achieve rapid dynamic task allocation and decision-making. The lower-layer model utilizes the upper-level allocation results combined with detailed environmental information to plan safe and efficient flight paths for each UAV at the micro level. It employs an improved jumping-point search algorithm for refined path optimization. A loop feedback mechanism is designed to facilitate information exchange between layers, thereby coupling the task allocation and path planning processes to achieve collaborative optimization of upper- and lower-level task allocation and decision-making. This method effectively addresses complex logistics delivery scenarios, enhancing the overall efficiency and robustness of the delivery system. Simulation experiments comprehensively consider path influences from flexible open-area delivery, varying numbers of distribution centers and UAVs, and on-demand rush orders. Tests conducted in medium- and high-density environments demonstrate the proposed model and algorithm’s significant superiority in dynamic complex scenarios. Even when confronted with complex environments and dynamic order scenarios, it consistently generates highly applicable UAV flight paths.
Zongwei Li, Guang Zhang, Heyun Gao· Journal of Vibration and Con...· 0 citations
This paper analyzes the factors affecting communication interactions between UAVs and proposes a bidding-based grouping method to eliminate ineffective communication interactions, and introduces a network simplification algorithm based on reducing the number of triangular network topologies to optimize the communication network structure.
Wei-xing Xia, Peng Chen, Feifei Song et al.· Drones· 0 citations
A hierarchical joint optimization algorithm is developed within a multi-agent deep reinforcement learning (MADRL) framework to coordinate UAVs and MTs in a distributed manner and outperforms other benchmarks under varying network scales and capabilities by jointly optimizing UAV operations and resource utilization.
Tiankui Zhang, Wenlong Xu, Tianyi Shi et al.· IEEE Internet of Things Jour...· 0 citations
Simulation results demonstrate that, compared with P-DQN, SAC, and PADDPG, the proposed framework achieves superior joint performance, thereby verifying its effectiveness for multi-UAV ISAC joint optimization.
Guifen Chen, Zeli Gong· Digital Signal and Computer...· 0 citations
Simulation results show that the proposed hierarchical task planning framework significantly outperforms traditional approaches in efficiency, robustness, and scalability, highlighting its strong potential for UAV swarm mission planning in complex environments.
Yalan Peng, Haibin Duan, Ming Li et al.· Science China Technological...· 0 citations
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