Optimal parameter estimation for Muskingum model based on Gray-encoded accelerating genetic algorithm
作者： Jianjun Chen , Xiaohua Yang
DOI: 10.1016/J.CNSNS.2005.06.005
关键词:
摘要: Abstract In order to reduce the computational amount and improve precision for parameter optimization of Muskingum model, a new algorithm, Gray-encoded accelerating genetic algorithm (GAGA) is proposed. With shrinking searching range, method gradually directs an optimal result with excellent individuals obtained by Gray (GGA). The global convergence analyzed algorithm. Its efficiency verified application model. Compared nonlinear programming methods, least residual square test method, GAGA has higher precision. And compared GGA BGA (binary-encoded algorithm), rapider convergent speed.
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