针对温差发电系统（TEG）最大功率点跟踪（MPPT）中传统控制算法易陷入局部最优及动态响应超调等问题，提出一种增强型哈里斯鹰混合优化算法（GEG-HHO）。通过建立Bi-Sb-S基温差发电器仿真与实测模型，对比分析恒定电压法（CVT）、樽海鞘群算法（SSA）、标准哈里斯鹰算法（HHO）与GEG-HHO在热端温度恒定工况、热端温度发生突变和模块数量发生突变下的跟踪性能。结果表明：在三种工况条件下，GEG-HHO在0.052±1s内都可进入稳定状态，平均跟踪精度达98.27%，无震荡超调现象，相较于对比算法其功率振荡抑制能力显著提升，提高动态响应速度。

Aiming at the problems of traditional control algorithms in maximum power point tracking (MPPT) for thermoelectric generation systems (TEG), such as easy trapping in local optima and overshoot in dynamic response, an enhanced Harris Hawk hybrid optimization algorithm (GEG-HHO) is proposed. By establishing simulation and experimental models of Bi-Sb-S based thermoelectric generators, the tracking performances of Constant Voltage Tracking (CVT), Salp Swarm Algorithm (SSA), standard Harris Hawk Optimization (HHO), and GEG-HHO are compared and analyzed under the conditions of constant hot-end temperature、sudden mutation of hot-end temperature and Sudden Change in Module Quantity. The results show that under both working conditions, GEG-HHO can enter a stable state within 0.052±1s, with an average tracking accuracy of 98.27%, and there is no oscillation or overshoot. Compared with the comparative algorithms, its power oscillation suppression capability is significantly improved, and the dynamic response speed is enhanced.