An Improved Honey Badger Algorithm Based on Urban Traffic-Inspired Strategies for Global Optimization and Financial Corporate Bankruptcy Forecasting

To address the limitations of the original Honey Badger Algorithm (HBA), including premature convergence, limited search directionality, and insufficient local escape capability in high-dimensional complex optimization problems, this paper proposes an improved Honey Badger Algorithm based on a traffic-driven strategy, namely the Traffic-driven Covariance Honey Badger Algorithm (TCHBA). The proposed algorithm introduces three synergistic evolutionary mechanisms. First, Elite Covariance Rotation Guidance learns correlated search directions from the current elite subset and injects a truncated covariance-based step into the HBA update. Second, Urban Traffic-Inspired Search uses population density and an iteration-dependent signal to regulate attraction and diversion. Third, Stagnation-Aware Lens Opposition Mutation is activated after unsuccessful updates and combines lens opposition with a heavy-tailed Cauchy perturbation to restore search mobility. Extensive experiments are conducted on the CEC2017 (100-dimensional) and CEC2022 (10- and 20-dimensional) benchmark suites. The results demonstrate that TCHBA significantly outperforms nine state-of-the-art optimization algorithms, including VPPSO, EGWO, GJO, RIME, ALA, HBO, MO, PWO, and the original HBA, in terms of solution accuracy, convergence speed, and statistical robustness. Furthermore, TCHBA is applied to the problem of Taiwanese enterprise bankruptcy prediction, a representative financial risk classification task. By optimizing the key parameters of the K-nearest neighbors (KNN) classifier, a TCHBA-KNN prediction model is constructed. Experimental results on real-world datasets show that the proposed model achieves superior performance in terms of accuracy, Matthews correlation coefficient (MCC), recall, and F1-score, thereby validating the effectiveness and practical potential of the proposed algorithm for real-world engineering and financial decision-making problems.