Intelligent multi-UAV path planning in complex environments: a hybrid optimization approach with direction-assisted search and adaptive refinement

Multi-UAV cooperative path planning in complex urban environments faces critical challenges in balancing exploration efficiency, convergence precision, and computational practicality. This study proposes a Hybrid Direction-Assisted Polar Light Optimization (HDPLO) algorithm that integrates three key innovations: directional heuristic mechanisms for intelligent search space expansion, adaptive crossover strategies for diversity maintenance, and CMA-ES integration for high-precision local refinement. Experimental validation using CEC2017 benchmarks and realistic multi-UAV scenarios (obstacle densities 7.71%-77.11%) demonstrates significant improvements: 38.9% better solution quality on benchmark functions, 45.7% faster convergence, 15.2% reduced path costs in UAV planning, and 11.1%-38.5% fewer turning maneuvers compared to state-of-the-art algorithms. Statistical analysis confirms performance reliability (p<0.001), while ablation studies verify each component’s essential contribution. These findings establish HDPLO as a robust solution for intelligent UAV systems requiring high reliability and efficiency in real-world deployments.