Predicting tourist flow is crucial for making effective management decisions at scenic spots. To further improve the accuracy of scenic passenger flow prediction, the current study proposed a data preprocessing approach that combines variational modal decomposition (VMD) with multiscale weighted permutation entropy (MWPE). Additionally, a tourist flow prediction method was introduced, utilizing a combination of bi-layer convolution, bidirectional long short-term memory, and an attention mechanism (CNN-BiLSTM-Att). First, the historical passenger flow data was decomposed into multiple intrinsic modal functions using VMD. Second, the IMF subsequence was measured using the MWPE metric, leading to the construction of a new feature matrix. Finally, the CNN-BiLSTM prediction model, incorporating an attention mechanism and optimized using a genetic algorithm, was applied to generate the final prediction results. Using Lushan Scenic Area (China) as a case study, our experimental results demonstrate that the proposed model significantly outperforms benchmark approaches including XGBoost, LSTM and CNN-BiLSTM. The comparative analysis reveals consistent improvements across all evaluation metrics: achieving a minimum 7.46% reduction in the MAPE, at least 7.2% decrease in BRMSE, and a minimum 1.74% enhancement in the R². These statistically significant improvements validate the model’s superior predictive capability for tourist flow forecasting. The demonstrated performance, combined with its methodological robustness, suggests strong practical applicability and promising potential for implementation across similar tourism management contexts.

To address the path-planning challenge for unmanned aerial vehicles (UAVs) in complex environments, this study presents an improved pelican optimization algorithm enhanced with multiple strategies (MIPOA). The proposed method introduces four main improvements: (1) using chaotic mapping to spread the initial search points more evenly, thereby increasing population variety; (2) incorporating a random Lévy-flight strategy to improve the exploration of the search space; (3) integrating a differential evolution approach based on Cauchy mutation to strengthen individual diversity and overall optimization ability; and (4) adopting an adaptive disturbance factor to speed up convergence and fine-tune solutions. To evaluate MIPOA, comparative tests were carried out against classical and modern intelligent algorithms using the CEC2017 and CEC2022 benchmark sets, along with a custom UAV environmental model. Results show that MIPOA converges faster and achieves more accurate solutions than the original pelican optimization algorithm (POA). On CEC2017 in 30-, 50-, and 100-dimensional cases, MIPOA attained the best average ranks of 1.57, 2.37, and 2.90, respectively, and achieved the top results on 26, 21, and 19 test functions, outperforming both POA and other advanced algorithms. For CEC2022 (20 dimensions), MIPOA obtained the highest Friedman average rank of 1.42, demonstrating its effectiveness in complex UAV path-planning tasks. The method enables the generation of faster, shorter, safer, and collision-free flight paths for UAVs, underscoring the robustness and wide applicability of MIPOA in real-world UAV path-planning scenarios.