Matthew Knowling
Michael N. Fienen
Adam Siade
Otis Rea
Guillermo Martinez
Jeremy White
2022
<div id="abstracts" class="Abstracts u-font-serif"><div id="abs0010" class="abstract author" lang="en"><div id="abssec0010"><p id="abspara0010">An open-source tool has been developed to facilitate constrained single- and multi-objective optimization under uncertainty (CMOU) analyses. The tool uses the well-known PEST interface protocols to communicate with the underlying forward simulation, making it non-intrusive. The tool contains a built-in parallel run manager to make use of heterogeneous and<span> </span>distributed computing<span> resources. Several popular and well-known evolutionary algorithms are implemented and can be combined with a range of approaches to represent uncertainty in model-derived constraint/objective values. These attributes serve to address the current barrier to adopt advanced CMOU analyses for a wide range of decision-support problems across the environmental modeling spectrum. We demonstrate the capabilities of the CMOU tool on a well-known analytical benchmark problem that we augmented to include uncertainty, as well as on a synthetic density-dependent coastal groundwater management benchmark problem. Both demonstrations highlight the importance of explicitly accounting for uncertainty to convey risk and reliability in pareto-optimal design.</span></p></div></div></div>
application/pdf
10.1016/j.envsoft.2022.105316
en
Elsevier
A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty
article