针对标准河马优化算法（hippopotamus optimization algorithm, HOA）在复杂优化问题中全局探索不足与信息共享单一的缺陷，提出了一种改进型河马优化算法（improved hippopotamus optimization algorithm, IHOA）。该算法通过引入动态探索因子增强全局搜索能力，采用多信息交互策略提升信息共享效率，并优化个体位置更新机制，提高了寻优精度与收敛速度。在9个基准测试函数的验证中，IHOA展现出卓越的性能。将IHOA应用于MLP神经网络（multilayer perceptron neural networks）的积液量预测任务，并与未优化模型对比，结果显示，决定系数R² （coefficient of determination）提高6%，均方根误差（root mean square error, RMSE）下降7%，平均绝对百分比误差（mean absolute percentage error, MAPE）下降2.21%，显著提升了模型的预测精度与寻优效率。研究为模型超参数调优及实际预测任务提供了有效的对策，具有较高的应用价值和理论意义。

To overcome the limitations of the standard hippopotamus optimization algorithm (HOA), such as inadequate global exploration and limited information sharing in complex optimization problems, an improved hippopotamus optimization algorithm (IHOA) was developed. The global search capability was enhanced by introducing a dynamic exploration factor, the efficiency of information sharing was improved through a multi-information interaction strategy, and the individual position update mechanism was optimized to increase both the accuracy of optimization and the convergence speed. The effectiveness of IHOA was verified through validation on nine benchmark test functions, where superior optimization performance was demonstrated. When applied to the task of liquid loading prediction using multilayer perceptron (MLP) neural networks, and compared with the unoptimized model, the coefficient of determination(R²) was increased by 6%, the root mean square error (RMSE) was reduced by 7%, and the mean absolute percentage error(MAPE) was decreased by 2.21%. These results confirmed that the prediction accuracy and optimization efficiency of the model were significantly improved. The proposed method provides a practical and effective solution for hyperparameter tuning and predictive modeling, offering high potential for engineering application and theoretical research value.