Enhanced adaptive zebra optimization algorithm optimized kernel extreme learning machine for bankruptcy prediction problems

In the context of global economic integration, corporate bankruptcy risk poses a significant threat to economic stability, making accurate bankruptcy prediction critically important. As a binary classification problem, traditional statistical approaches struggle to handle nonlinear features, resulting in limited predictive accuracy. While machine learning-based intelligent models have improved prediction performance, the effectiveness of Kernel Extreme Learning Machine (KELM) heavily depends on the selection of the penalty parameter (C) and kernel parameter (γ). However, conventional optimization methods often suffer from low efficiency and a tendency to fall into local optima. The Zebra Optimization Algorithm (ZOA), a newly developed swarm intelligence algorithm, also faces limitations such as rapid loss of population diversity and weak local exploitation capabilities. To address these issues, this paper proposes an Enhanced Archive-based Zebra Optimization Algorithm (EAZOA). By incorporating a Levy mutation strategy guided by elite individuals, a dynamic elite archive mechanism, and a hybrid boundary handling technique, the proposed algorithm significantly improves optimization performance. EAZOA is then employed to optimize the parameters of KELM, resulting in the EAZOA-KELM bankruptcy prediction model. Experimental results on the CEC2020 and CEC2022 benchmark suites demonstrate that EAZOA outperforms several state-of-the-art algorithms in terms of convergence accuracy, stability, and scalability. Moreover, experiments on the Wieslaw financial dataset show that EAZOA-KELM achieves superior performance across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. These results validate the model’s effectiveness in corporate bankruptcy prediction and highlight its potential as a powerful tool for financial risk early warning.