群居蜘蛛优化算法在水文频率分析中的应用  

Hydrologic Frequency Analysis Using SSO Algorithm

作　　者：王文川[1,2] 雷冠军[1,2] 刘灿灿[3] 徐冬梅[1,2] 

机构地区：[1]华北水利水电大学水利学院,河南郑州450011 [2]水资源高效利用与保障工程河南省协同创新中心,河南郑州450011 [3]华南农业大学水利与土木工程学院,广东广州510642

出　　处：《水文》2016年第3期34-39,共6页Journal of China Hydrology

基　　金：国家自然科学基金项目(51509088);河南省高校科技创新团队(14IRTSTHN028);水利部公益性行业科研专项(200501008);河南省重点科技攻关计划项目(132102110046);华北水利水电大学研究生教育创新计划基金资助(YK2014-07)

摘　　要：水文频率分析在参数估计过程中常采用智能优化适线法,如蚁群算法、遗传算法、粒子群算法、模拟退火算法等,但这些算法模型参数难以有效确定,导致寻优结果存在不稳定的不足。为了克服传统优化适线法的缺陷,在系统阐述群居蜘蛛优化算法基本原理的基础上,将群居蜘蛛优化算法用于水文频率曲线的参数确定中,并与传统的参数估计方法(矩法、权函数法、概率权重矩法、遗传算法)加以比较。实例结果表明,该方法搜索效率高,寻优结果稳定,能较好获得参数的最优解。The optimization of the parameter estimation of optimal line method in Hydrological frequency analysis, such as the ap-plication of the artificial intelligence algorithms which includes the ant colony algorithm, genetic algorithm, particle swarm optimiza-tion, simulated annealing algorithm and other methods, is difficult to determine the model parameters and the optimization result is not stable, namely, the defect of premature convergence. This paper expounded the basic principle of the social spiders optimization algorithm systematically, and put forward that the social spider optimization algorithm should be applied to hydrological parameters of frequency curve, and at the same time be compared with the traditional parameter estimation method（moment method, weight function method, probability weighted moment method, and genetic algorithm）. It was showed that in this method the search effi-ciency is high, the result is stable and the global optimal solution of parameters can be can found out.

关 键 词：优化适线法 群居蜘蛛优化算法 水文频率分析 

分 类 号：P333[天文地球—水文科学]