| 摘要: |
| 通过8个复杂函数对一种异构多种群粒子群优化算法进行仿真验证，并与传统单种群粒子群优化算法进行对比。针对水位流量关系拟合中相关参数难以确定的不足，利用异构多种群粒子群优化算法优化水位流量关系相关参数，以云南省龙潭站、西洋站水位流量关系拟合为例进行实例研究，并与粒子群优化算法、最小二乘法拟合结果进行对比。结果表明：异构多种群粒子群优化算法收敛精度远远优于粒子群优化算法，具有较好的计算鲁棒性和全局寻优能力。该算法对龙潭站和西洋站水位流量关系拟合的平均相对误差绝对值分别仅为0.27%和0.50%，拟合精度优于粒子群优化算法和最小二乘法。利用异构多种群粒子群优化算法优化水位流量关系可以获得更好的拟合效果。 |
| 关键词: 水位流量关系 异构多种群粒子群优化算法 参数优化 盘龙河 西洋江 |
| DOI： |
| 分类号:P337-3 |
| 基金项目: |
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| Application of convergent heterogeneous particle swarm optimization to fitting stage-discharge relation |
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CUI Dong-wen
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Wenshan Water Conservancy Bureau of Yunnan Province, Wenshan
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| Abstract: |
| By use of 8 complex functions, a heterogeneous multi group particle swarm optimization algorithm is simulated, comparing with the traditional single population particle swarm optimization algorithm. Aiming at the deficiencies of the stage discharge relation fitting in it is difficult to determine the parameters, using a variety of the heterogeneous particle swarm optimization algorithm for optimizing the relative parameters of the stage-discharge relationship, taking stage-discharge relation fitting of Yunnan Province Longtan station, Xiyang station as case studies, and the particle swarm optimization algorithm and least squares fitting results are compared in the study. The analysis results show that the convergence accuracy of the heterogeneous multi group particle swarm optimization algorithm is much better than the particle swarm optimization algorithm,with good computational robustness and global optimization ability. The relative error absolute values of the fitting for the relationships between the water level of the Longtan railway station and the Xiyang station are only 0.27% and 0.50% respectively. The fitting accuracy is better than that of the particle swarm optimization and the least square method. The better fitting effect can be obtained by optimizing the water level flow relationship by using the heterogeneous multi group particle swarm optimization algorithm. |
| Key words: Stage-discharge relation fitting convergent heterogeneous particle swarm optimization algorithm parameter optimization Panlong River Xiyang River |
