渗透系数空间变异程度对进化算法优化结果影响评价

南京大学学报(自然科学版) ›› 2015, Vol. 51 ›› Issue (1): 60–66.

渗透系数空间变异程度对进化算法优化结果影响评价

骆乾坤*，吴剑锋2，杨运3，钱家忠1

出版日期:2015-01-04 发布日期:2015-01-04
作者简介:(1.合肥工业大学资源与环境工程学院,安徽省,合肥市,230009 2.南京大学地球科学与工程学院水科学系,江苏省,南京市,210093 3.淮河水利委员会,安徽省,蚌埠市,233001)
基金资助:
中央高校基本科研业务费专项资金资助（J2014HGBZ0186）；国家自然科学基金资助项目（41072175，41372235）

Effect assessment of spatial variation of hydraulic conductivity on optimization result of evolutionary algorithm

Luo Qian-Kun1, Wu Jan-Feng2, Yang Yun3, Qian Jia-Zhong1

Online:2015-01-04 Published:2015-01-04
About author:(1. School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, 230009, China; 2.Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210093, China; 3.Huai River Water Resources Commission, Bengbu 233001, China)

摘要/Abstract

摘要： 噪声遗传算法（Noisy genetic algorithm, NGA）是近年来引入到地下水领域处理参数空间变异性的新方法。本文针对渗透系数空间变异程度对基于NGA策略的进化算法求解效果影响展开研究，探索NGA策略适用范围。研究结果表明：当渗透系数对数方差()小于1.0时，采用NGA取样策略，提高算法计算效率的同时不会影响优化结果可靠性；当增加到2.0甚至3.0, 5.0时，算法优化结果不再具有高可靠性。通过增加NGA最大取样数可以提高算法求解精度，有效降低优化结果的不确定性。但随着最大取样数的增加，优化结果精度和可靠性将不再有明显提高。此时需寻求其他方法，如增加资金投入，获取渗透系数条件点，降低渗透系数场不确定性，从而获得更加准确可靠的优化方案。

Abstract: The noisy genetic algorithm (NGA) is a new approach to manage the spatial variation of parameters in recent years. In this study, in order to explore the application range of NGA strategy, it commenced research to study the performance of NGAstrategy under different spatial variation of hydraulic conductivity. The study results show that the optimization results of NGA strategynot only have high reliability but also have high computational efficiency when little than 1.0. However, when increased to 2.0, or even to 3.0, 5.0, the optimization results of NGA strategyno longer have high reliability. In this case, increasing the maximum sampling number of NGA can improve the accuracy of Pareto optimal solutions and reduce the uncertainty of the optimization results. However, when the maximum sampling number of NGA reach a certain extent, the accuracy and reliability of the optimization results will not significantly increase. To further improve the accuracy of the optimization results, it needs to seek other method to describe the spatial variation of hydraulic conductivity, such as increasing remediation cost to get more hydraulic conductivity condition point to reduce the uncertainty of the hydraulic conductivity. Then, more accurate and reliable optimization strategy can be found

骆乾坤*，吴剑锋2，杨运3，钱家忠1. 渗透系数空间变异程度对进化算法优化结果影响评价[J]. 南京大学学报(自然科学版), 2015, 51(1): 60–66.

Luo Qian-Kun1, Wu Jan-Feng2, Yang Yun3, Qian Jia-Zhong1. Effect assessment of spatial variation of hydraulic conductivity on optimization result of evolutionary algorithm[J]. Journal of Nanjing University(Natural Sciences), 2015, 51(1): 60–66.

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