Multi-objective optimization for conceptual hydrological models
摘要
Traditionally,conceptual hydrological models are calibrated by optimizing the model parameters using a single objective function,which makes is difficult to properly consider all behavior of a natural hydrological system.To circumvent this problem,we propose a novel Multi-objective Culture Shuffled Complex Differential Evolution(MOCSCDE) algorithm for model calibration based on multi-objective functions in this paper.The MOCSCDE algorithm takes the Cultural Algorithms(CA) as the evolving framework and adopts the Shuffled Complex Evolution(SCE-UA) in the population space.This evolution strategy can make use of the problem-solving knowledge obtained along with the evolution process to guide the algorithm toward the optimization direction.Meanwhile,as the simplex search operator in the SCE-UA algorithm cannot use the whole information of the individuals,a Differential Evolution(DE) algorithm is employed to serve as a substitute of the simplex search operator.The DE algorithm can more thoroughly utilize the information of the population and enhance the search efficacy of the algorithm.The performance of the MOCSCDE algorithm is tested for parameter estimation of a conceptual hydrological model(Xinanjiang model).The MOCSCDE results are compared to those obtained with the NSGA-II and SPEA2 algorithms.It can be found that the MOCSCDE can get better convergence and spread performance,and can provide more reliable and comprehensive solutions in practical applications.