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This study presents a three-dimensional trajectory optimization model with dynamic altitude adjustment and an Improved Multi-Objective Artificial Hummingbird Algorithm (IMOAHA) for energy-efficient distributed UAV systems in multi-agent cooperative networks, addressing critical limitations of conventional approaches that fail to adapt to complex field environments with obstacle avoidance and signal coverage requirements while suffering from slow convergence and uneven solution distribution in multi-objective optimization. By developing a parallel computation model incorporating both Air-to-Air (A2A) and Air-to-Ground (A2G) channels, we jointly optimize 3D trajectories, power allocation, and resource scheduling to minimize total system energy consumption while guaranteeing worst-link transmission rates. The enhanced IMOAHA framework integrates K-means clustering for improved initial solution quality, Differential Evolution (DE) for local search intensification, and Particle Swarm Optimization (PSO) for global exploration efficiency. MATLAB simulations in a farmland scenario demonstrate IMOAHA’s superior performance: 6.6% reduction in total energy consumption (26.75 kJ), 13.9% shorter travel distance (1021.5 m), while maintaining high transmission rates (16.28 Mbps), outperforming 10 benchmark algorithms including GA and NSGA-II. These advancements provide both theoretical foundations and practical solutions for energy-efficient UAV coordination in complex environments, with significant engineering applicability and scalability across diverse operational scenarios.

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Distributed Multi-agent Coordination for Energy-Efficient UAV Systems: A Parallel Optimization Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Puqin Han,
  • Zhiqiang Liu,
  • Xu Zhang,
  • Wenjing Li

摘要

This study presents a three-dimensional trajectory optimization model with dynamic altitude adjustment and an Improved Multi-Objective Artificial Hummingbird Algorithm (IMOAHA) for energy-efficient distributed UAV systems in multi-agent cooperative networks, addressing critical limitations of conventional approaches that fail to adapt to complex field environments with obstacle avoidance and signal coverage requirements while suffering from slow convergence and uneven solution distribution in multi-objective optimization. By developing a parallel computation model incorporating both Air-to-Air (A2A) and Air-to-Ground (A2G) channels, we jointly optimize 3D trajectories, power allocation, and resource scheduling to minimize total system energy consumption while guaranteeing worst-link transmission rates. The enhanced IMOAHA framework integrates K-means clustering for improved initial solution quality, Differential Evolution (DE) for local search intensification, and Particle Swarm Optimization (PSO) for global exploration efficiency. MATLAB simulations in a farmland scenario demonstrate IMOAHA’s superior performance: 6.6% reduction in total energy consumption (26.75 kJ), 13.9% shorter travel distance (1021.5 m), while maintaining high transmission rates (16.28 Mbps), outperforming 10 benchmark algorithms including GA and NSGA-II. These advancements provide both theoretical foundations and practical solutions for energy-efficient UAV coordination in complex environments, with significant engineering applicability and scalability across diverse operational scenarios.