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A new selection metric for multiobjective hydrologic model calibration
Author(s) -
Asadzadeh Masoud,
Tolson Bryan A.,
Burn Donald H.
Publication year - 2014
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2013wr014970
Subject(s) - metric (unit) , pareto principle , benchmark (surveying) , mathematical optimization , selection (genetic algorithm) , multi objective optimization , calibration , regular polygon , convex hull , computer science , optimization problem , mathematics , statistics , artificial intelligence , engineering , geometry , operations management , geodesy , geography
A novel selection metric called Convex Hull Contribution (CHC) is introduced for solving multiobjective (MO) optimization problems with Pareto fronts that can be accurately approximated by a convex curve. The hydrologic model calibration literature shows that many biobjective calibration problems with a proper setup result in such Pareto fronts. The CHC selection approach identifies a subset of archived nondominated solutions whose map in the objective space forms convex approximation of the Pareto front. The optimization algorithm can sample solely from these solutions to more accurately approximate the convex shape of the Pareto front. It is empirically demonstrated that CHC improves the performance of Pareto Archived Dynamically Dimensioned Search (PA‐DDS) when solving MO problems with convex Pareto fronts. This conclusion is based on the results of several benchmark mathematical problems and several hydrologic model calibration problems with two or three objective functions. The impact of CHC on PA‐DDS performance is most evident when the computational budget is somewhat limited. It is also demonstrated that 1,000 solution evaluations (limited budget in this study) is sufficient for PA‐DDS with CHC‐based selection to achieve very high quality calibration results relative to the results achieved after 10,000 solution evaluations.