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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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A wave of new U.S.-U.K. deals ahead of Trump’s state visit
President Trump will arrive in the United Kingdom this week for a state visit that promises to include the usual pomp and ceremony alongside the signing of a landmark new agreement on U.S.-U.K. nuclear collaboration.
James E. Bevins, R. N. Slaybaugh
Nuclear Technology | Volume 205 | Number 4 | April 2019 | Pages 542-562
Technical Paper | doi.org/10.1080/00295450.2018.1496692
Articles are hosted by Taylor and Francis Online.
This paper introduces Gnowee, a modular, Python-based, open-source hybrid metaheuristic optimization algorithm (available from https://github.com/SlaybaughLab/Gnowee). Gnowee is designed for rapid convergence to nearly globally optimum solutions for complex, constrained nuclear engineering problems with mixed-integer (MI) and combinatorial design vectors and high-cost, noisy, discontinuous, black box objective function evaluations. Gnowee’s hybrid metaheuristic framework is a new combination of a set of diverse, robust heuristics that appropriately balance diversification and intensification strategies across a wide range of optimization problems. There are many potential applications for this novel algorithm both within the nuclear community and beyond. Given that a set of well-known and studied nuclear benchmarks does not exist for the purpose of testing optimization algorithms, comparisons between Gnowee and several well-established metaheuristic algorithms are made for a set of 18 established continuous, MI, and combinatorial benchmarks representing a wide range of types of engineering problems and solution space behaviors. These results demonstrate Gnoweee to have superior flexibility and convergence characteristics over this diverse set of design spaces. We anticipate this wide range of applicability will make this algorithm desirable for many complex engineering applications.