Advancing Robust Multi-Objective Optimisation Applied to Complex Model-Based Water-Related Problems

Advancing Robust Multi-Objective Optimisation Applied to Complex Model-Based Water-Related Problems
Author :
Publisher : CRC Press
Total Pages : 164
Release :
ISBN-10 : 9781000043143
ISBN-13 : 1000043142
Rating : 4/5 (142 Downloads)

Book Synopsis Advancing Robust Multi-Objective Optimisation Applied to Complex Model-Based Water-Related Problems by : Oscar Osvaldo Marquez Calvo

Download or read book Advancing Robust Multi-Objective Optimisation Applied to Complex Model-Based Water-Related Problems written by Oscar Osvaldo Marquez Calvo and published by CRC Press. This book was released on 2020-02-07 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


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