Back calculation of source intensity and position based on a combined Genetic-Nelder Mead Simplex Algorithm

Shiliang Zhang
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Abstract

I the pollutant control process, it is required to locate the source of pollution first, know the strength of the pollutant leakage source, in order to quickly and effectively prevent the further diffusion of pollution. Atmospheric diffusion model is widely used in traceability of pollutant leakage source. Through the inversion of this model, the source and intensity of leakage can be determined. However, it is always a challenging problem how to carry out reverse optimization quickly and accurately. No matter it is based on probability statistics theory or optimization theory, a single optimization method has its own unavoidable defects. In this study, the genetic algorithm and Nelder Mead simplex algorithm were combined. Firstly, the genetic algorithm was used to accurately narrow the search field, and then the Nelder Mead simplex algorithm was used to quickly obtain the optimal solution, effectively overcoming the defects of the slow convergence speed of the genetic algorithm and the poor convergence quality of the Nelder Mead simplex algorithm. Combined with the experimental data, it is found that the algorithm is fast and accurate in the traceability reverse calculation of the diffusion of pollutants in the atmosphere and the long-distance leakage of dangerous gases, which is not affected by the selection of initial values, and the optimization efficiency and robustness have been significantly improved.
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基于遗传- nelder - Mead联合单纯形算法的光源强度和位置反求
在污染物控制过程中,要求首先定位污染源,了解污染物泄漏源的强度,以便快速有效地防止污染的进一步扩散。大气扩散模型在污染物泄漏源溯源中得到广泛应用。通过该模型的反演,可以确定泄漏源和泄漏强度。然而,如何快速准确地进行逆向优化一直是一个具有挑战性的问题。无论是基于概率统计理论还是基于优化理论,单一的优化方法都有其不可避免的缺陷。本研究将遗传算法与Nelder Mead单纯形算法相结合。首先利用遗传算法精确缩小搜索范围,然后利用Nelder Mead单纯形算法快速获得最优解,有效克服了遗传算法收敛速度慢和Nelder Mead单纯形算法收敛质量差的缺陷。结合实验数据发现,该算法在大气中污染物扩散和危险气体远距离泄漏的溯源逆计算中快速准确,且不受初值选取的影响,优化效率和鲁棒性得到显著提高。
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