To improve the optimization performance of the original Bounty Hunter Optimizer (BHO) in complex search environments, this paper proposes a quantum computing and adaptive mechanism-based BHO, named QCAMBHO. The proposed algorithm integrates three complementary strategies: quantum-computing-enhanced initialization, adaptive Lévy flight, and an adaptive differential operator. These mechanisms are designed to improve population diversity, strengthen global exploration, and enhance later-stage exploitation. The performance of QCAMBHO is evaluated on the CEC2017 and CEC2022 benchmark test suites. Experimental results show that QCAMBHO achieves competitive or superior optimization performance compared with several advanced algorithms in terms of convergence accuracy, stability, and robustness. Ablation experiments further confirm the positive contribution of each strategy and the synergistic effect of their integration. To examine its practical applicability, QCAMBHO is further used to optimize the key parameters of Kernel Extreme Learning Machine (KELM), and a QCAMBHO-KELM model is constructed for enterprise bankruptcy prediction. The results show that QCAMBHO-KELM achieves better overall classification performance than BHO-KELM and other comparison models across multiple evaluation metrics, including accuracy, Matthews correlation coefficient, sensitivity, specificity, precision, recall, and F1-score. These findings indicate that QCAMBHO not only provides an effective optimizer for complex numerical problems but also offers a promising decision-support tool for improving the accuracy and reliability of enterprise bankruptcy early warning.
He et al. (Thu,) studied this question.