A Data-Driven Hybrid Optimization Model Integrating an Improved Zebra Optimization Algorithm and BP Neural Network

Data-driven applications need advanced predictive modeling to manage nonlinear relationships and high-dimensional datasets. To address these challenges, this research presents a novel hybrid optimization model integrating an Improved Zebra Optimization Algorithm (IZOA) with a Back Propagation (BP) neural network to enhance predictive performance in complex datasets. The IZOA addresses inherent limitations in traditional optimization methods by employing a Logistic chaotic initialization technique that increases population diversity. Furthermore, a Golden Sine optimization strategy is incorporated to balance exploration and exploitation effectively. The model’s architecture leverages the strengths of both IZOA and BP neural networks, allowing for refined local adjustments while maintaining global search capabilities. Experimental evaluations demonstrate that the IZOA-BP model significantly outperforms conventional approaches, achieving superior metrics such as a Root Mean Squared Error (RMSE) of 2.271, Mean Absolute Error (MAE) of 0.891, and an R² value of 0.935. These advancements highlight the model’s robustness in capturing nonlinear interactions and adapting to high-dimensional data, positioning the IZOA-BP framework as a transformative tool for various predictive applications.