Chaos high efficient genetic algorithm for parameter optimization of Muskingum routing model

In this paper, the chaos high efficient genetic algorithm (CHEGA) is proposed for parameter optimization of Muskingum routing model, in which the initial population are generated by chaos algorithm, and the new chaos mutation operation is used for the shrinking of searching range. CHEGA gradually directs to an optimal result with the excellent individuals obtained by real-value genetic algorithm. It is very efficient in maintaining the population diversity during the evolution process of genetic algorithm. Its efficiency is verified by application of five test functions compared with standard binary-encoded genetic algorithm and improved genetic algorithm. Compared with real-valued accelerating genetic algorithm and traditional optimization methods, CHEGA can get to the whole searching range, it has rapider convergent speed and higher calculation precision. It is efficient for the global optimization in the practical hydrological models.