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遗传算法用于水资源工程中的理论和应用研究
引用本文:金菊良,丁晶. 遗传算法用于水资源工程中的理论和应用研究[J]. 计算机工程与应用, 2003, 39(35): 209-211,232
作者姓名:金菊良  丁晶
作者单位:1. 合肥工业大学土木建筑工程学院,合肥,230009
2. 四川大学水电学院,成都,610065
基金项目:教育部优秀青年教师资助计划(教人司犤2002犦350),四川大学高速水力学国家重点实验室开放基金(编号:0201),安徽省优秀青年科技基金,安徽省自然科学基金(编号:01045102)
摘    要:基于遗传算法的运行机理和交叉思想,文章把遗传算法的基本原理、分析技术和先进算法较完整地引入水资源工程领域,研制了适用于水资源工程问题的标准遗传算法的3种改进方案,即基于二进制编码的加速遗传算法(AGA)、基于实数编码的加速遗传算法(RAGA)和基于整数编码的单亲遗传算法(IPGA),建立了基于AGA的门限自回归模型、基于AGA的门限回归模型、基于AGA的双线性模型、基于RAGA的逻辑斯谛曲线等级评价模型、基于RAGA的投影寻踪等级评价模型。并把这些模型成功地用于地下水预测、海洋冰情预测、河道洪水预测、径流预测、旱涝序列预测、洪水灾情评估、水质综合评价等实际问题中。研究结果说明,遗传算法在处理实际水资源工程复杂优化问题中具有广泛的应用价值,值得进一步研究和探索。

关 键 词:遗传算法  水资源工程  优化  投影寻踪  时间序列分析  预测  评价
文章编号:1002-8331-(2003)35-0209-03

Study of Theory and Application of Genetic Algorithm for Water Resources Engineering
Jin Juliang Ding Jing. Study of Theory and Application of Genetic Algorithm for Water Resources Engineering[J]. Computer Engineering and Applications, 2003, 39(35): 209-211,232
Authors:Jin Juliang Ding Jing
Affiliation:Jin Juliang 1 Ding Jing 21
Abstract:The basis principle,analysis technique and advance algorithm of genetic algorithm are systemically introduced into water resources engineering in this paper based on the knowledge of the running mechanism of genetic algorithm and the idea of crossover.Three schemes of improved simple genetic algorithm are developed,which are binary number coded accelerating genetic algorithm(AGA),real coded accelerating genetic algorithm(RAGA),and integer coded partheno-genetic algorithm(IPGA).On the basis of AGA or RAGA,threshold auto-regressive model,threshold regressive model,bilinear time series model,logistic curve grade evaluation model,projection pursuit grade evaluation model can be established very conveniently.The above models have been successfully applied to prediction of groundwater level,marine ice condition,river flood and runoff,drought and flood series,grade evaluation of flood disaster loss and water quality.The above results show that genetic algorithm can be widely applied to complex practical problems of water resources engineering,which is worth further advanced research.
Keywords:Genetic algorithm  Water resources engineering  Optimal  Projection pursuit  Analysis of time series  Prediction  Evaluation
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