Wang Qinyong, Xu Minghai, Hu Zhongyi
School of Artificial Intelligence, Zhejiang College of Security Technology, Wenzhou 325016, China.
School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China.
Biomimetics (Basel). 2024 Jun 26;9(7):388. doi: 10.3390/biomimetics9070388.
The Sine-Levy tuna swarm optimization (SLTSO) algorithm is a novel method based on the sine strategy and Levy flight guidance. It is presented as a solution to the shortcomings of the tuna swarm optimization (TSO) algorithm, which include its tendency to reach local optima and limited capacity to search worldwide. This algorithm updates locations using the Levy flight technique and greedy approach and generates initial solutions using an elite reverse learning process. Additionally, it offers an individual location optimization method called golden sine, which enhances the algorithm's capacity to explore widely and steer clear of local optima. To plan UAV flight paths safely and effectively in complex obstacle environments, the SLTSO algorithm considers constraints such as geographic and airspace obstacles, along with performance metrics like flight environment, flight space, flight distance, angle, altitude, and threat levels. The effectiveness of the algorithm is verified by simulation and the creation of a path planning model. Experimental results show that the SLTSO algorithm displays faster convergence rates, better optimization precision, shorter and smoother paths, and concomitant reduction in energy usage. A drone can now map its route far more effectively thanks to these improvements. Consequently, the proposed SLTSO algorithm demonstrates both efficacy and superiority in UAV route planning applications.
正弦-莱维金枪鱼群优化（SLTSO）算法是一种基于正弦策略和莱维飞行引导的新型方法。它是针对金枪鱼群优化（TSO）算法的缺点而提出的，这些缺点包括其倾向于陷入局部最优以及在全局搜索能力有限。该算法使用莱维飞行技术和贪婪方法更新位置，并通过精英反向学习过程生成初始解。此外，它还提供了一种名为黄金正弦的个体位置优化方法，增强了算法广泛探索和避免局部最优的能力。为了在复杂的障碍物环境中安全有效地规划无人机飞行路径，SLTSO算法考虑了地理和空域障碍物等约束条件，以及飞行环境、飞行空间、飞行距离·、角度、高度和威胁级别等性能指标。通过仿真和创建路径规划模型验证了该算法的有效性。实验结果表明，SLTSO算法具有更快的收敛速度、更好的优化精度、更短和平滑的路径，以及随之而来的能源使用减少。由于这些改进，无人机现在可以更有效地绘制其路线。因此，所提出的SLTSO算法在无人机路径规划应用中展示了有效性和优越性。
