The optimization algorithm of PSO-DE cooperated with mobile monitoring platform was studied to solve the inverse problem of pollution, which included inversion of the position of the single point and multiple-point stationary sources. The inverse problem of pollution source was transformed into nonlinear optimization problem. The pollutant concentration of waters were detected and recorded by N mobile platforms; the coordinate of mobile platform was denoted by pbest, and they were corresponded one by one, there would be N pbest altogether. The pollutant concentration of waters which attained by the mobile platform would be compared with each other, and the coordinate of maximum pollutant concentration would be chosen and marked as gbest. First, the gbest would be the initial population for the PSO optimization. Second, the population would be used for DE optimization. Finally, the gbest would be chosen from the high concentration of both until the highest point of pollutant concentration was obtained, which was the initial point of pollutant. The calculation results of examples showed that the algorithm could attained a high precision inversion results for pollutant source traceability problem of two-dimensional waters.
曹宏桂, 贠卫国. 基于PSO-DE算法的突发水域污染溯源研究[J]. 中国环境科学, 2017, 37(10): 3807-3812.
CAO Hong-gui, YUN Wei-guo. Research of the abrupt waters pollution source based on optimization algorithm of PSO-DE. CHINA ENVIRONMENTAL SCIENCECE, 2017, 37(10): 3807-3812.
Gurhan G, Halil K. Solving inverse problems of groundwaterpollution-source identification using a differential evolution algorithm[J]. Hydrogeology Journal, 2015,23:1109-1119.
[3]
Jyoti C, Deepak K. Groundwater pollution source identification through inverse modeling[J]. Water Resources and River Engineering, 2015.
Jong C, Meng G W, et al. Structural reliability-based optimization design using PSO-DE hybrid algorithm[J]. Journal of South China University of Technology, 2014,42(9):41-45.
[13]
Ednah O, Akpofure T, John N. Groundwater pollution source identification by optimization and the green element method[J]. World Environmental and Water Resources Congress, 2016, 309-318.
[14]
MT A. A hybrid simulation-optimization approach for solving the areal groundwater pollution source identification problems[J]. Journal of Hydrology, 2016,538:161-176.
Yu F Y, Chen X, Guo L. Hybrid algorithm based on particle swarm optimization and differential evolution[M]. Journal of computational information system, 2012.
[18]
Singh R M, Datta B. Identification of groundwater pollution sources using GA-based linked simulation optimization model[J]. Journal of Hydrologic Engineering, 2006,11(2):1216-1227.