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A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty

Jeremy White·Matthew Knowling·Michael N. Fienen·Adam Siade·Otis Rea·Guillermo Martinez
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Abstract

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 distributed computing 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.

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Jeremy White, Matthew Knowling, Michael N. Fienen, Adam Siade, Otis Rea, Guillermo Martinez. 2022. A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty. https://doi.org/10.1016/j.envsoft.2022.105316

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