Genetic algorithm (GA) is often used to solve the optimization model of groundwater pollution source identification. However, GA is prone to premature convergence and fall into local optimum. Thus, homotopy algorithm and GA are used in combination to improve the disadvantage. Then hybrid homotopy-genetic algorithm (HGA) was applied to solve the optimization model. A 0–1 mixed integer nonlinear optimization model (0–1MINLP) based on kriging surrogate model was used to simultaneously identify hydraulic conductivity, location, and release history of pollution sources. The results showed that the 0–1MINLP based on a kriging surrogate model could simultaneously identify the hydraulic conductivity and information of pollution sources, while maintaining a certain level of precision and reducing calculation load. The combination of homotopy algorithm and GA can improve the shortcomings of GA that is easy to fall into premature convergence. The identification results obtained by the HGA were closer to the true values of the pollution source characteristics compared with GA.

中文翻译：

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基于混合同伦-遗传算法和仿真优化的地下水污染源识别

遗传算法（GA）常用于求解地下水污染源识别的优化模型。然而，遗传算法容易过早收敛，陷入局部最优。因此，结合使用同伦算法和遗传算法来改善这一缺点。然后应用混合同伦遗传算法（HGA）对优化模型进行求解。基于克里金代理模型的 0-1 混合整数非线性优化模型 (0-1MINLP) 用于同时识别污染源的水力传导率、位置和释放历史。结果表明，基于克里金代理模型的0-1MINLP可以同时识别水力传导率和污染源信息，同时保持一定的精度，减少计算量。同伦算法和遗传算法的结合可以改善遗传算法容易陷入早熟收敛的缺点。与GA相比，HGA得到的识别结果更接近污染源特征的真实值。