An Osprey-Sparrow Optimization Algorithm Suitable for Unmanned Surface Vehicles Path Planning - Gu, Gaoquan; Li, Haitao (2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL), 2024)
Addressing the complex task of path planning for unmanned surface Vehicles (USVs), this study introduces an advanced Osprey-Sparrow Optimization Algorithm (OSOA), which infuses the strategic elements of the osprey's hunting behavior into the sparrow search paradigm. The algorithm enhances the traditional sparrow search mechanism by incorporating global exploration and vigilance strategies derived from the osprey optimization algorithm. Notably, the introduction of a Cauchy mutation strategy into the follower update process significantly improves the algorithm's exploratory capabilities and diversifies the solution space. The superior performance of OSOA is ascertained through a comparative analysis against five established optimization algorithms, employing 12 distinct benchmark test functions across various dimensions. Statistical validation is achieved through Wilcoxon rank-sum and Friedman tests. Furthermore, the application of OSOA to the practical problem of USVs path planning, benchmarked against the existing osprey algorithm, demonstrates exceptional performance and robustness when addressing real-world complexities. These findings not only illustrate the theoretical advancement of OSOA but also offer a potent new tool for the practical application in autonomous naval navigation systems.