Extracting a set of robust pareto-optimal parameters for hydrologie models using NSGA-II and SCEM

Alireza NAZEMI, Xin YAO, Andrew H. CHAN

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

25 Citations (Scopus)

Abstract

In this paper, we will present a heuristic method in order to combine the information about the parametric space of a conceptual hydrologie model from two different sources. On one hand, multi-objective evolutionary optimization algorithm NSGA-II is used to find a set of pareto optimal solutions. On the other hand, a Markov Chain Monte Carlo-based algorithm, i.e. Shuffled Complex Evolution Metropolis (SCEM) is used to highlight a set of parameters with higher posterior distribution. By covering the interval between the most crowded locations in the parametric space extracted by both algorithms, we will identify a set of pareto optimal solutions which is more robust than the initial non-dominated set extracted by only NSGA-IL © 2006 IEEE.
Original languageEnglish
Title of host publication2006 IEEE Congress on Evolutionary Computation, CEC 2006
Pages1901-1908
Number of pages8
Publication statusPublished - 2006
Externally publishedYes

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