3D path planning for UAV based on A hybrid algorithm of marine predators algorithm with quasi-oppositional learning and differential evolution

• Published in: Egyptian informatics journal 2024-12, Vol.28, p.100556, Article 100556
• Main Authors: Tu, Binbin, Wang, Fei, Han, Xiaowei
• Format: Article
• Language: English
• Subjects: 3D Path Planning; Differential Evolution; Marine Predators Algorithm; Quasi-oppositional Learning; Spherical Vector Coordinates; UAV
• ISSN: 1110-8665
• DOI: 10.1016/j.eij.2024.100556

3D path planning is a critical requirement for the autonomy of unmanned aerial vehicle (UAV) navigation systems. In this paper, we propose a novel approach, the differential evolution combined marine predators algorithm (DECMPA), specifically tailored to address the UAV 3D path planning problem in complex scenarios. To handle stringent constraints, DECMPA adopts a multi-step approach. Initially, it utilizes quasi-oppositional learning to generate a more uniformly distributed initial population. Throughout the iterative process, it probabilistically generates quasi-oppositional populations and merges them with existing populations, selecting the superior individuals for the next generation to mitigate local optima. Additionally, DECMPA integrates the variance, crossover, and selection processes of differential evolution into the marine predators algorithm iterations. The greedy selection of test and target vectors further accelerates population convergence. Numerical optimization experiments are conducted using the 10 and 20-dimensional CEC 2021 test suite. Compared to other algorithms, DECMPA exhibits superior solution accuracy and convergence speed, resulting in substantial performance enhancement, thus establishing itself as an efficient algorithm. Furthermore, the adoption of spherical vector coordinates in solution encoding ensures higher quality and facilitates the production of feasible solutions. To verify the effectiveness and practicality of the proposed algorithm, comprehensive path planning simulation experiments are conducted. DECMPA is compared against nine other state-of-the-art heuristic algorithms across six 3D scenarios of varying complexity. The experimental results demonstrate DECMPA’s superiority in terms of optimal cost acquisition, solution quality, and stability. In conclusion, DECMPA presents a promising solution for addressing the challenges of UAV 3D path planning in complex environments. Its innovative approach and superior performance underscore its potential for real-world application in autonomous UAV navigation systems.