Print Email Facebook Twitter Advancing Robust Multi-Objective Optimisation Applied to Complex Model-Based Water-Related Problems Title Advancing Robust Multi-Objective Optimisation Applied to Complex Model-Based Water-Related Problems Author Marquez Calvo, O.O. (TU Delft Water Resources) Contributor Solomatine, D.P. (promotor) Alfonso, Leonardo (copromotor) Degree granting institution Delft University of Technology Date 2020-01-15 Abstract The exercise of solving engineering problems that require optimisation procedures can be seriously affected by uncertain variables, resulting in potential underperforming solutions. Although this is a well-known problem, important knowledge gaps are still to be addressed. For example, concepts of robustness largely differ from study to study, robust solutions are generally provided with limited information about their uncertainty, and robust optimisation is difficult to apply as it is a computationally demanding task.The proposed research aims to address the mentioned challenges and focuses on robust optimisation of multiple objectives and multiple sources of probabilistically described uncertainty. This is done by the development of the Robust Optimisation and Probabilistic Analysis of Robustness algorithm (ROPAR), which integrates widely accepted robustness metrics into a single flexible framework. In this thesis, ROPAR is not only tested in benchmark functions, but also in engineering problems related to the water sector, in particular the design of urban drainage and water distribution systems.ROPAR allows for employing practically any existing multi-objective optimisation algorithm as its internal optimisation engine, which enables its applicability to other problems as well. Additionally, ROPAR can be straightforwardly parallelized, allowing for fast availability of results. Subject Robust optimisationRobust optimizationOptimisationOptimizationDrainage systemWater distribution system To reference this document use: http://resolver.tudelft.nl/uuid:1cbc3ec2-7297-4a6a-8fdf-dce9e271c76f Publisher CRC Press / Balkema - Taylor & Francis Group ISBN 978-0-367-46043-3 Part of collection Institutional Repository Document type doctoral thesis Rights © 2020 O.O. Marquez Calvo Files PDF 2020_IHE_PHD_THESIS_MARQU ... ALVO_i.pdf 11.81 MB Close viewer /islandora/object/uuid:1cbc3ec2-7297-4a6a-8fdf-dce9e271c76f/datastream/OBJ/view