Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design Article Swipe
James E. Bevins
,
Rachel Slaybaugh
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1080/00295450.2018.1496692
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1080/00295450.2018.1496692
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 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. This novel algorithm was specifically developed to optimize complex nuclear design problems; the motivating research problem was the design of material stack-ups to modify neutron energy spectra to specific targeted spectra for applications in nuclear medicine, technical nuclear forensics, nuclear physics, etc. However, there are a wider range of potential applications for this algorithm both within the nuclear community and beyond. To demonstrate Gnowee's behavior for a variety of problem types, comparisons between Gnowee and several well-established metaheuristic algorithms are made for a set of eighteen continuous, mixed-integer, and combinatorial benchmarks. These results demonstrate Gnoweee to have superior flexibility and convergence characteristics over a wide range of design spaces. We anticipate this wide range of applicability will make this algorithm desirable for many complex engineering applications.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1080/00295450.2018.1496692
- OA Status
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https://openalex.org/W2796645819Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/00295450.2018.1496692Digital Object Identifier
- Title
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Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer DesignWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-08-23Full publication date if available
- Authors
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James E. Bevins, Rachel SlaybaughList of authors in order
- Landing page
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https://doi.org/10.1080/00295450.2018.1496692Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1804.05429Direct OA link when available
- Concepts
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Metaheuristic, Computer science, Mathematical optimization, Tabu search, Algorithm, Combinatorial optimization, Heuristics, Optimization problem, MathematicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2137148026, https://openalex.org/W4253572765, https://openalex.org/W1987494769, https://openalex.org/W1965081037, https://openalex.org/W641467550, https://openalex.org/W1595159159, https://openalex.org/W2085080956, https://openalex.org/W1976744965, https://openalex.org/W1975671633, https://openalex.org/W2208403925, https://openalex.org/W2115367795, https://openalex.org/W2155014525, https://openalex.org/W2074869363, https://openalex.org/W603729728, https://openalex.org/W1997869834, https://openalex.org/W2023106695, https://openalex.org/W2136610504, https://openalex.org/W1503369191, https://openalex.org/W2083334804, https://openalex.org/W2077345223, https://openalex.org/W2084792706, https://openalex.org/W1977523613, https://openalex.org/W2131077880, https://openalex.org/W2010059576, https://openalex.org/W2168750171, https://openalex.org/W2185612927, https://openalex.org/W2101103648, https://openalex.org/W2048421273, https://openalex.org/W2001331635, https://openalex.org/W2100184918, https://openalex.org/W2042986967, https://openalex.org/W2094174254, https://openalex.org/W2138309709, https://openalex.org/W17748033, https://openalex.org/W2152195021, https://openalex.org/W2039911195, https://openalex.org/W2004322850, https://openalex.org/W2020009149, https://openalex.org/W2335540726, https://openalex.org/W2121780193, https://openalex.org/W2023452756, https://openalex.org/W1765427872, https://openalex.org/W1918578756, https://openalex.org/W2151554678, https://openalex.org/W2543580944, https://openalex.org/W1771326151 |
| referenced_works_count | 46 |
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| abstract_inverted_index.We | 185 |
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| citation_normalized_percentile.is_in_top_10_percent | False |