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基于和声搜索改进的蚁群算法在无人机路径规划中的应用
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Abstract:
在无人机路径规划中，针对传统蚁群算法收敛速度慢、易陷入局部最优、拐点多等缺点，提出了基于和声搜索改进的蚁群算法。利用和声搜索算法和声扰动的思想，对每一代蚂蚁选择的路径进行扰动处理，保留更优路径，增加算法前期路径选择的多样性，提高收敛速度，减少局部最优。对路径进行平滑处理，删除不必要节点，缩短路径距离，为无人机平衡控制减少风险。仿真实验证明，相比于传统蚁群算法，基于和声搜索改进的蚁群算法收敛速度更快，路径更优，且在较为复杂的栅格环境中，效果更为突出。
In UAV path planning, in order to solve the slow convergence speed and easily falling into local optimization, an improved ant colony algorithm based on harmony search is proposed. Drawing on the idea of the harmony search algorithm, the algorithm performs disturbance processing on the paths selected by each generation of ants and retains the better paths, which increases the diversity of path selection in the early stage of the algorithm and improves the convergence speed. Path smoothing is used in the algorithm to remove unnecessary nodes, reduce path length, and reduce risks for UAV balance control. Simulation experiments show that, compared with the traditional ant colony algorithm, the improved ant colony algorithm based on harmony search has faster convergence speed and better path, and in a more complex grid environment, the effect is more prominent.
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