An Adaptive Sliding-Window Ant Colony Optimization Approach for Multi-UAV Delivery Routing with Safety and Lateness Objectives

Station Deployment and Unmanned Aerial Vehicles Path Planning for Wind Farm Inspection Using Adaptive Bees Optimization Algorithm

Dynamic-robustness-oriented hierarchical task planning for UAV swarm based on improved pigeon-inspired optimization

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.

A multi-strategy improved dung beetle optimization algorithm for UAV path planning

A Multi-Objective Optimization Model for Collaborative UAV–Rider Meal Delivery Routing Using an Enhanced NSGA-II Algorithm

Urban instant-delivery platforms increasingly require efficient, punctual, and low-carbon delivery services. Unmanned aerial vehicles (UAVs) can reduce dependence on congested ground traffic in meal delivery and further improve overall delivery efficiency through coordinated operations with ground riders at rendezvous points. However, existing studies mainly focus on ground-based routing or simplify UAV-assisted delivery as a single-objective problem, limiting their ability to balance cost, completion time, carbon emissions, and service quality. To address this limitation, this paper investigates a collaborative UAV–rider meal delivery routing problem and formulates a multi-objective optimization model integrating restaurant pickup, UAV transfer, rider last-mile delivery, and soft customer time windows. An Enhanced NSGA-II algorithm is then developed, where hybrid initialization improves solution quality and diversity, adaptive operators balance exploration and exploitation, local search refines route structures, structural repair maintains feasibility, and diversity preservation supports a well-distributed Pareto front. Comparative experiments against NSGA-II, MOPSO, NSGA-III, MOEA/D, MODE, and IMODE, together with scalability, ablation, sensitivity, and case analyses, show that E-NSGA-II provides stronger Pareto-front approximation. The results support its use as a decision-support method for service-aware and low-carbon UAV–rider meal delivery, while also revealing additional computational cost.

A Dynamic Multi-Priority Unmanned Aerial Vehicle Assignment Algorithm Integrating an Improved Discrete Particle Swarm Optimization and Greedy Strategy

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.

Dynamic Multiobjective Co-Optimization of Unmanned Aerial Vehicle Placement, Task Scheduling, and Resource Allocation for Remote Sensing

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.