Accurate calibration of triaxial magnetometers is critical for magnetic anomaly detection, yet existing optimization‐based calibration methods often suffer from premature convergence and suboptimal parameter estimation. To address these limitations, this article proposes a Tent map and Selection‐phase enhanced African Vulture Optimization Algorithm (TS‐AVOA) for efficient magnetometer error correction. The proposed TS‐AVOA enhances global search diversity and local convergence accuracy by integrating a Bald Eagle Search (BES)‐inspired selection mechanism and embedding Tent chaotic mapping into both the exploration and exploitation phases. Numerical simulations demonstrate that the maximum absolute error between the calibrated and theoretical magnetic field amplitude is reduced to just 1.1 nT, with parameter estimation accuracy exceeding 99.9%. Comparative analysis against PSO, GA, and original AVOA confirms that TS‐AVOA outperforms all baselines in convergence speed and calibration accuracy. Field experiments further validate that total geomagnetic fluctuation is suppressed from 282.47 to 9.79 nT, achieving a 28.86‐fold reduction. These results highlight the practical potential of TS‐AVOA in enhancing the reliability of fluxgate magnetometers for geomagnetic applications.
Zhang et al. (Thu,) studied this question.