This paper simulates the cuckoo incubation process and flight path to optimize the Wavelet Neural Network (WNN) model, and proposes a parking prediction algorithm based on WNN and improved Cuckoo Search (CS) algorithm. First, the initialization parameters are provided to optimize the WNN using the improved CS. The traditional CS algorithm adopts the strategy of overall update and evaluation, but does not consider its own information, so the convergence speed is very slow. The proposed algorithm employs the evaluation strategy of group update, which not only retains the advantage of fast convergence of the dimension-by-dimension update evaluation strategy, but also increases the mutual relationship between the nests and reduces the overall running time. Then, we use the WNN model to predict parking information. The proposed algorithm is compared with six different heuristic algorithms in five experiments. The experimental results show that the proposed algorithm is superior to other algorithms in terms of running time and accuracy.

中文翻译：

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改进的CS算法及其在停车位预测中的应用

通过仿真布谷鸟的孵化过程和飞行路径，优化小波神经网络模型，提出了一种基于布谷鸟网络和改进的布谷鸟搜索算法的停车预测算法。首先，使用改进的CS提供初始化参数以优化WNN。传统的CS算法采用整体更新和评估的策略，但不考虑自身的信息，因此收敛速度很慢。提出的算法采用分组更新的评估策略，不仅保留了逐维更新评估策略快速收敛的优点，而且增加了嵌套之间的相互关系，减少了整体运行时间。然后，我们使用WNN模型来预测停车信息。在五个实验中，将所提出的算法与六种不同的启发式算法进行了比较。实验结果表明，该算法在运行时间和准确性上均优于其他算法。