Abstract With the rapid development of Unmanned Aerial Vehicle (UAV) swarm technology, achieving high-precision, multi-constraint cooperative path planning in complex mountainous environments has become a prominent research focus in intelligent control. However, traditional optimization algorithms often suffer from slow convergence, premature stagnation, and limited global search capabilities when addressing high-dimensional, nonlinear spaces. To overcome these challenges, this paper proposes a novel Adaptive Elite Grey Wolf-guided Crested Porcupine Optimizer (AEGWCPO), designed to enhance cooperative path planning for multi-UAV systems in complex 3D environments. The method first constructs an Adaptive Crested Porcupine Optimizer (ACPO) by introducing a Good Point Set initialization strategy to improve population uniformity. It also uses a fitness-state-based perturbation mechanism to adaptively adjust step sizes and incorporates periodic Lévy disturbances to enhance local escape capability, improving search accuracy and robustness. AEGWCPO integrates a dynamic convergence trend detection mechanism that activates the Elite Grey Wolf-guided Optimization (EGWO) during stagnation phases. By using elite solution centroids, it performs jump-based updates in global and local exploitation stages, strengthening the algorithm’s guidance across multiple search phases. By combining local perturbation and dynamic elite guidance, AEGWCPO effectively balances exploration and exploitation in high-dimensional, complex path planning, enhancing both convergence speed and global optimization. Simulation results show that AEGWCPO outperforms multiple state-of-the-art algorithms in 3D mountainous environments in terms of convergence rate, path quality, and adaptability, offering an efficient and robust solution for multi-objective UAV swarm path planning.
Gao et al. (Thu,) studied this question.