Abstract

Unmanned aerial vehicle (UAV) is highly flexible and versatile, ranging from monitoring and surveying to rescue and military applications, but finding the best path requires a large amount of computing resources. Through intelligent path planning algorithms, UAV can find the best path according to task requirements and environmental conditions, avoid obstacles, and bypass dangerous areas, thereby effectively reducing the risks and errors of task execution. However, ordinary heuristic algorithms often do not achieve satisfactory results. To address this problem, A multi-mechanism balanced advanced sparrow search algorithm (BALSSA) is proposed. To achieve a balance between exploration and exploitation in the algorithm, we introduce two innovative techniques: an adaptive weight jumping mechanism and a suicide mutation perturbation. Then a balanced advanced learning is proposed. this approach enhances the evolutionary learning capabilities of SSA and aids the algorithm in timely escaping from local optima, and then propose a spiral factor improved progressive learning to improve the mutual learning performance between individuals, tested on 23 benchmark test functions, CEC2017 and CEC2022 The set is compared with algorithms in recent years and proposed algorithm variants, and the results show that BALSSA has better optimization and robustness. Finally, the proposed BALSSA is applied to UAV path planning. Compared with the variant SSA in recent years, BALSSA shows more stable and accurate optimization performance to verify the practicability of the improved algorithm.

中文翻译：

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无人机路径规划的多机制平衡高级学习麻雀搜索算法

摘要

无人机（UAV）具有高度灵活性和多功能性，范围从监测和测量到救援和军事应用，但寻找最佳路径需要大量的计算资源。通过智能路径规划算法，无人机可以根据任务需求和环境条件找到最佳路径，避开障碍物，绕过危险区域，从而有效降低任务执行的风险和错误。然而，普通的启发式算法往往不能达到令人满意的结果。针对这一问题，提出了一种多机制平衡高级麻雀搜索算法（BALSSA）。为了在算法中实现探索和利用之间的平衡，我们引入了两种创新技术：自适应权重跳跃机制和自杀突变扰动。然后提出了平衡的高级学习。该方法增强了SSA的进化学习能力，帮助算法及时逃离局部最优，然后提出螺旋因子改进的渐进学习来提高个体之间的相互学习性能，并在23个基准测试函数、CEC2017和CEC2022集上进行了测试与近年来的算法以及提出的算法变体进行比较，结果表明BALSSA具有更好的优化性和鲁棒性。最后，将所提出的 BALSSA 应用于无人机路径规划。与近年来的变体SSA相比，BALSSA表现出更稳定、更准确的优化性能，验证了改进算法的实用性。