| 新安江模型参数优选的改进粒子群算法 |
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| 引用本文： | 江燕,刘昌明,胡铁松,武夏宁. 新安江模型参数优选的改进粒子群算法[J]. 水利学报, 2007, 38(10): 1200-1206 |
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| 作者姓名： | 江燕 刘昌明 胡铁松 武夏宁 |
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| 作者单位： | 北京师范大学,水科学研究院,北京,100875;武汉大学,资源与水电工程科学国家重点实验室,湖北,武汉,430072;中国水利水电建设集团公司,北京,100044 |
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| 摘 要： | 借鉴竞争演化和多种群混合的思想,对粒子群算法(PSO)进行改进,建立并行种群混合进化的粒子群算法(PMSE-PSO)和序列主-从种群混合进行的粒子群算法(SMSE-PSO)。数值模拟结果表明,这两种改进的粒子群算法具有较高的计算效率、较强的自适应性和稳定性。将PMSE-PSO和SMSE-PSO应用于新安江模型的参数优选中,通过与PSO和SCE-UA的比较可以看出,PMSE-PSO和SMSE-PSO不仅具有较好的全局优化性能和稳定性,而且在调用目标函数次数相同的情况下精度较高,是一种有效的新安江模型参数优选方法。
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| 关 键 词： | 参数优选 新安江模型 全局优化 粒子群算法 多种群混合进化 |
| 文章编号： | 0559-9350（2007）10-1200-07 |
| 修稿时间： | 2006-09-26 |
Improved particle swarm optimization for parameter calibration of Xin'anjiang model |
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| JIANG Yan. Improved particle swarm optimization for parameter calibration of Xin'anjiang model[J]. Journal of Hydraulic Engineering, 2007, 38(10): 1200-1206 |
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| Authors: | JIANG Yan |
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| Affiliation: | 1. Beijing Normal University, Beoijing 100875, China ; 2. Wuhan University, Wuhan 430072, China 3. Sinohydro Corporation Limited, Beifing 100044 China |
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| Abstract: | Two improved particle swarm optimization algorithm(PSO) including the parallel-swarms shuffling evolution algorithm(PMSE-PSO) and serial master-slaver swarms shuffing evolution algorithm(SMSE-PSO) were established by combining the particle swarm optimization with competitive evolution and concept of complex shuffling.The comparison with traditional PSO and shuffled complex evaluation algorithm(SCE-UA) shows that both these new algorithms can improve the efficiency,self-adaptability and stability of computation.The application of these improved algorithms to parameters optimization of Xin'anjiang model shows that both PMSE-PSO and SMSE-PSO remarkably improves the computation efficiency and accuracy. |
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| Keywords: | parameter optimization Xin'anjiang model improved particle swarm optimization algorithm(PSO) parallel-swamis shuffling evolution |
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