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DOE OSTI · 2001237

ParMOO: A Python library for parallel multiobjective simulation optimization

Chang, Tyler H.·Wild, Stefan M.
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Abstract

A multiobjective optimization problem (MOOP) is an optimization problem in which multiple objectives are optimized simultaneously. The goal of a MOOP is to find solutions that describe the tradeoff between these (potentially conflicting) objectives. Such a tradeoff surface is called the Pareto front. Real-world MOOPs may also involve constraints – additional hard rules that every solution must adhere to. In a multiobjective simulation optimization problem, the objectives are derived from the outputs of one or more computationally expensive simulations. Such problems are ubiquitous in science and engineering.

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BibTeXRIS

Chang, Tyler H., Wild, Stefan M.. 2023-02-03. ParMOO: A Python library for parallel multiobjective simulation optimization. https://doi.org/10.21105/joss.04468

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