| 考虑拥挤度的多目标粒子群优化算法在马斯京根参数估计中的应用 |
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| 引用本文： | 宋万祯,雷晓辉,黄晓敏,唐兵,蒋云钟.考虑拥挤度的多目标粒子群优化算法在马斯京根参数估计中的应用[J].水电能源科学,2013,31(1):38-41. |
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| 作者姓名： | 宋万祯 雷晓辉 黄晓敏 唐兵 蒋云钟 |
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| 作者单位： | 天津大学 建筑工程学院， 天津 300072； 中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室， 北京 100038;国水利水电科学研究院 流域水循环模拟与调控国家重点实验室， 北京 100038;中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室， 北京 100038； 东华大学 环境科学与工程学院， 上海 201620;国水利水电科学研究院 流域水循环模拟与调控国家重点实验室， 北京 100038;国水利水电科学研究院 流域水循环模拟与调控国家重点实验室， 北京 100038 |
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| 基金项目： | 中国水利水电科学研究院科研专项基金资助项目（资集1037）；水利部公益性行业科研专项经费基金资助项目（200901031，201001024，201101026，201101024） |
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| 摘 要： | 采用考虑拥挤度的多目标粒子群优化算法进行马斯京根模型参数估计,介绍了考虑拥挤度的多目标粒子群优化算法的计算步骤,用外部精英档案保存非支配解,并通过计算拥挤度维持解的多样性,以海河流域南运河称钩湾至临清段的一次洪水过程为例,选取高流量、低流量和时段内总量差比三个优化目标对优化结果进行了评价。结果表明,高流量与低流量之间为正比关系,而与总量差比之间存在制约关系;高流量和低流量目标值最小时模拟结果较好。
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| 关 键 词： | 马斯京根模型 参数估计 多目标粒子群优化算法 拥挤度 |
Application of Multi-objective Particle Swarm Optimization in Muskingum Parameter Estimation Considering Crowding Distance |
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| SONG Wanzhen,LEI Xiaohui,HUANG Xiaomin,TANG Bing and JIANG Yunzhong.Application of Multi-objective Particle Swarm Optimization in Muskingum Parameter Estimation Considering Crowding Distance[J].International Journal Hydroelectric Energy,2013,31(1):38-41. |
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| Authors: | SONG Wanzhen LEI Xiaohui HUANG Xiaomin TANG Bing and JIANG Yunzhong |
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| Affiliation: | School of Civil Engineering, Tianjin University, Tianjin 300072, China; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China;State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China;2. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; 3. School of Environmental Science and Engineering, Donghua University, Shanghai 201620, China;State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China;State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China |
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| Abstract: | Multi-objective particle swarm optimization algorithm with crowding distance (MOPSO-CD) is applied to optimize the parameters of Muskingum model. The implementation step of MOPSO-CD is presented. And then the non-dominated solutions are saved with external elite strategy and the crowding distance is calculated to maintain individual diversity. Taking flood runoff process between Chenggouwan to Linqing segment in Nanyunhe River of Haihe Basin for an example, the high flow objective, low flow objective and the ratio of total difference are selected to evaluate optimal solutions. The results show that there is a direct proportion relationship between the high flow and low flow objectives while they all have constrained relation with the ratio of total difference; when the value of high and low flow objective are minimal, the simulated results are reasonable. |
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| Keywords: | Muskingum model parameter estimation multi-objective particle swarm optimization crowding distance |
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