Multi-Strategy Enhanced COA for Path Planning in Autonomous Navigation
Yifei Wang, Jacky Keung, Haiying Xu, Yuchen Cao, Zhenyu Mao · 2025
Autonomous navigation is reshaping various domains in people’s life by enabling safe and efficient movement in complex environments. Reliable navigation requires path planning algorithms that compute optimal or near-optimal trajectories while satisfying task-specific constraints and ensuring obstacle avoidance. However, existing algorithms struggle with slow convergence and suboptimal solutions, particularly in complex environments, limiting their real-world applicability. To address these limitations, this paper presents the Multi-Strategy Enhanced Crayfish Optimization Algorithm (MCOA), a novel approach integrating three strategies: 1) Refractive Learning to enhance diversity and global exploration, 2) Stochastic Centroid-Guided Exploration to balance global and local search, and 3) Adaptive Competition-Based Selection to accelerate convergence and improve solution quality. Experimental results show that MCOA significantly improves the performance of 3D UAV path planning, reducing computation time by 69.2% and trajectory cost by 67.0% compared to 11 baseline algorithms, which demonstrates its effectiveness in autonomous navigation within complex environments.