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Advancing robust multi-objective optimisation applied to complex model-based water-related problems
Group: Hydroinformatics, IHE Delft
Promotor: Professor Dimitri Solomatine
Co-promotor: Dr. Leonardo Alfonso Segura
This research considers the problem of robust optimization, and develops the technique called Robust Optimization and Probabilistic Analysis of Robustness (ROPAR). It has been developed for finding robust optimum solutions of a particular class in model-based multi-objective optimization problems (i.e. when the objective function is not known analytically), where some of the parameters to this model are assumed to be uncertain. A Monte Carlo simulation framework is used. It can be straightforwardly implemented in a distributed computing environment. The technique is exemplified in three kinds of case studies: a) a benchmark problem commonly used to test multi-objective optimization algorithms, and b) design problem of storm drainage systems, and c) design problem of water distribution systems. It is shown that the design found by ROPAR can adequately cope with parameter uncertainties. The approach can be useful for assisting in a wide range of risk-based decisions.
By September we will present an overview of SENSE dissertations on this page, with links to the full texts of the dissertations.