Adaptive differential evolution for efficient unmanned aerial vehicle path planning in different environments 自适应差分进化算法在不同环境下实现高效无人机路径规划

Chen D., Chen X., Meng Z., Bahattin T.

Chinese Journal of Intelligent Science and Technology, cilt.8, sa.2, ss.250-263, 2026 (Scopus)

Özet

The unmanned aerial vehicle (UAV) path planning problem is highly challenging due to the high dimensionality of the search space and presence of complex operational constraints. To address these limitations, an enhanced differential evolution (DE) algorithm was proposed that integrates three synergistic strategies. First, a Poisson distribution based mechanism was introduced to estimate the mutation success rate in the next generation, enabling adaptive allocation of search resources toward more effective mutation strategies. Second, cosine similarity constraint was employed to guide the evolutionary direction of the population, thereby reducing directional divergence and improving convergence efficiency. Third, a staged adaptive parameter control scheme was designed, which enhances global exploration in the early phase and accelerates convergence in later phase while maintaining population diversity. By collaboratively integrating adaptive strategy selection, evolutionary direction guidance, and dynamic parameter adjustment, the proposed method achieves a balanced trade off between exploration and exploitation. Experimental results of the CEC2017 benchmark suite and multiple UAV path planning simulation scenarios constructed from real digital elevation model (DEM) data demonstrate that the proposed algorithm achieves competitive performance in convergence speed, stability, and solution quality.