| 水文地质参数反演的Hooke-Jeeves粒子群混合算法 |
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| 引用本文： | 江思珉,朱国荣,施小清,周念清.水文地质参数反演的Hooke-Jeeves粒子群混合算法[J].水科学进展,2010,21(5):606-612. |
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| 作者姓名： | 江思珉 朱国荣 施小清 周念清 |
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| 作者单位： | 1.同济大学水利工程系, 上海, 200092; |
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| 基金项目： | 国家自然科学基金资助项目 |
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| 摘 要： | 水文地质参数寻优结果的好坏会直接影响到地下水数值模拟的精度,而参数寻优结果很大程度上取决于寻优方法的选择。粒子群算法是一种基于群智能的随机全局寻优方法,算法的缺陷是后期搜索效率低劣。基于随机寻优算法的混合策略,引入有效的约束处理手段和粒子群算法惯性因子的动态非线性调整技术,有机融合粒子群算法与Hooke-Jeeves方法,提出一种适用于水文地质参数反演的HJPSO混合算法。应用研究表明,HJPSO混合算法在参数反演计算中求解精度高、收敛速度快、寻优性能强,是一种值得推广的水文地质参数识别方法。
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| 关 键 词： | 水文地质参数 粒子群算法 Hooke-Jeeves算法 参数反演 优化技术 |
| 收稿时间： | 2009-12-01 |
Inverse analysis of hydrogeological parameters using hybrid Hooke-Jeeves and particle swarm optimization method |
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| JIANG Si-min,ZHU Guo-rong,SHI Xiao-qing,ZHOU Nian-qing.Inverse analysis of hydrogeological parameters using hybrid Hooke-Jeeves and particle swarm optimization method[J].Advances in Water Science,2010,21(5):606-612. |
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| Authors: | JIANG Si-min ZHU Guo-rong SHI Xiao-qing ZHOU Nian-qing |
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| Affiliation: | 1.Department of Hydraulic Engineering, Tongji University, Shanghai 200092, China;2.Department of HydroSciences, Nanjing University, Nanjing 210093, China |
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| Abstract: | The performance of groundwater modeling relies largely on the accurate estmiation of hydrogeological parameters,which are often influenced by the choice of optimization methods used.The particle swarm optimization(PSO)is a swarm in telligence technique for global optimization.However,the search efficiency of PSO decreases as the iteration process approaching to the end.It is thus desirable to search for the enhanced version of PSO.In this study,a hybrid global optimization algorithm that uses the Hooke-Jeeves(HJ)method for the bcal optimization and PSO for the global optimization is proposed to address the inverse problems in ground water modeling.Hydrogeological parameters can be determined using the new HJPSO algorithm.The result of a case study shows that HJPSO is characterized as an algorithm with accuracy,fast convergence and high robustness in the estmiation of hydrogeological parameters,and applicable to hydrogeologic parameters identification. |
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| Keywords: | hydrogeological paramete rparticle swarm optimization Hooke-Jeeves search method inverse analysis optimization techniques |
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