Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained,\n Black Box, Combinatorial Mixed-Integer Design Article Swipe
James E. Bevins
,
Rachel Slaybaugh
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1804.05429
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1804.05429
This paper introduces Gnowee, a modular, Python-based, open-source hybrid\nmetaheuristic optimization algorithm (Available from\nhttps://github.com/SlaybaughLab/Gnowee). Gnowee is designed for rapid\nconvergence to nearly globally optimum solutions for complex, constrained\nnuclear engineering problems with mixed-integer and combinatorial design\nvectors and high-cost, noisy, discontinuous, black box objective function\nevaluations. Gnowee's hybrid metaheuristic framework is a new combination of a\nset of diverse, robust heuristics that appropriately balance diversification\nand intensification strategies across a wide range of optimization problems.\n This novel algorithm was specifically developed to optimize complex nuclear\ndesign problems; the motivating research problem was the design of material\nstack-ups to modify neutron energy spectra to specific targeted spectra for\napplications in nuclear medicine, technical nuclear forensics, nuclear physics,\netc. However, there are a wider range of potential applications for this\nalgorithm both within the nuclear community and beyond. To demonstrate Gnowee's\nbehavior for a variety of problem types, comparisons between Gnowee and several\nwell-established metaheuristic algorithms are made for a set of eighteen\ncontinuous, mixed-integer, and combinatorial benchmarks. These results\ndemonstrate Gnoweee to have superior flexibility and convergence\ncharacteristics over a wide range of design spaces. We anticipate this wide\nrange of applicability will make this algorithm desirable for many complex\nengineering applications.\n
Related Topics
Concepts
Metadata
- Type
- preprint
- Landing Page
- http://arxiv.org/abs/1804.05429
- https://arxiv.org/pdf/1804.05429
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4297197883
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4297197883Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.1804.05429Digital Object Identifier
- Title
-
Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained,\n Black Box, Combinatorial Mixed-Integer DesignWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2018Year of publication
- Publication date
-
2018-04-15Full publication date if available
- Authors
-
James E. Bevins, Rachel SlaybaughList of authors in order
- Landing page
-
https://arxiv.org/abs/1804.05429Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1804.05429Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1804.05429Direct OA link when available
- Concepts
-
Metaheuristic, Computer science, Mathematical optimization, Tabu search, Heuristics, Algorithm, Combinatorial optimization, Optimization problem, Range (aeronautics), Mathematics, Engineering, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4297197883 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.1804.05429 |
| ids.openalex | https://openalex.org/W4297197883 |
| fwci | 0.0 |
| type | preprint |
| title | Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained,\n Black Box, Combinatorial Mixed-Integer Design |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T13465 |
| topics[0].field.id | https://openalex.org/fields/25 |
| topics[0].field.display_name | Materials Science |
| topics[0].score | 0.9890999794006348 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2505 |
| topics[0].subfield.display_name | Materials Chemistry |
| topics[0].display_name | Graphite, nuclear technology, radiation studies |
| topics[1].id | https://openalex.org/T10597 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.986299991607666 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2202 |
| topics[1].subfield.display_name | Aerospace Engineering |
| topics[1].display_name | Nuclear reactor physics and engineering |
| topics[2].id | https://openalex.org/T13923 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9484999775886536 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2213 |
| topics[2].subfield.display_name | Safety, Risk, Reliability and Quality |
| topics[2].display_name | Nuclear and radioactivity studies |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C109718341 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7224864959716797 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1385229 |
| concepts[0].display_name | Metaheuristic |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5981953144073486 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C126255220 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5893466472625732 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[2].display_name | Mathematical optimization |
| concepts[3].id | https://openalex.org/C123370116 |
| concepts[3].level | 2 |
| concepts[3].score | 0.573978066444397 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1424540 |
| concepts[3].display_name | Tabu search |
| concepts[4].id | https://openalex.org/C127705205 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5274897813796997 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q5748245 |
| concepts[4].display_name | Heuristics |
| concepts[5].id | https://openalex.org/C11413529 |
| concepts[5].level | 1 |
| concepts[5].score | 0.5070021152496338 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[5].display_name | Algorithm |
| concepts[6].id | https://openalex.org/C52692508 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4989588260650635 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1333872 |
| concepts[6].display_name | Combinatorial optimization |
| concepts[7].id | https://openalex.org/C137836250 |
| concepts[7].level | 2 |
| concepts[7].score | 0.46465176343917847 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q984063 |
| concepts[7].display_name | Optimization problem |
| concepts[8].id | https://openalex.org/C204323151 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4176381528377533 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q905424 |
| concepts[8].display_name | Range (aeronautics) |
| concepts[9].id | https://openalex.org/C33923547 |
| concepts[9].level | 0 |
| concepts[9].score | 0.22122430801391602 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[9].display_name | Mathematics |
| concepts[10].id | https://openalex.org/C127413603 |
| concepts[10].level | 0 |
| concepts[10].score | 0.15349429845809937 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[10].display_name | Engineering |
| concepts[11].id | https://openalex.org/C146978453 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q3798668 |
| concepts[11].display_name | Aerospace engineering |
| keywords[0].id | https://openalex.org/keywords/metaheuristic |
| keywords[0].score | 0.7224864959716797 |
| keywords[0].display_name | Metaheuristic |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5981953144073486 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[2].score | 0.5893466472625732 |
| keywords[2].display_name | Mathematical optimization |
| keywords[3].id | https://openalex.org/keywords/tabu-search |
| keywords[3].score | 0.573978066444397 |
| keywords[3].display_name | Tabu search |
| keywords[4].id | https://openalex.org/keywords/heuristics |
| keywords[4].score | 0.5274897813796997 |
| keywords[4].display_name | Heuristics |
| keywords[5].id | https://openalex.org/keywords/algorithm |
| keywords[5].score | 0.5070021152496338 |
| keywords[5].display_name | Algorithm |
| keywords[6].id | https://openalex.org/keywords/combinatorial-optimization |
| keywords[6].score | 0.4989588260650635 |
| keywords[6].display_name | Combinatorial optimization |
| keywords[7].id | https://openalex.org/keywords/optimization-problem |
| keywords[7].score | 0.46465176343917847 |
| keywords[7].display_name | Optimization problem |
| keywords[8].id | https://openalex.org/keywords/range |
| keywords[8].score | 0.4176381528377533 |
| keywords[8].display_name | Range (aeronautics) |
| keywords[9].id | https://openalex.org/keywords/mathematics |
| keywords[9].score | 0.22122430801391602 |
| keywords[9].display_name | Mathematics |
| keywords[10].id | https://openalex.org/keywords/engineering |
| keywords[10].score | 0.15349429845809937 |
| keywords[10].display_name | Engineering |
| language | |
| locations[0].id | pmh:oai:arXiv.org:1804.05429 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/1804.05429 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/1804.05429 |
| indexed_in | arxiv |
| authorships[0].author.id | https://openalex.org/A5000502069 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6140-199X |
| authorships[0].author.display_name | James E. Bevins |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Bevins, James |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5027851362 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6296-6519 |
| authorships[1].author.display_name | Rachel Slaybaugh |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Slaybaugh, Rachel |
| authorships[1].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/1804.05429 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2022-09-27T00:00:00 |
| display_name | Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained,\n Black Box, Combinatorial Mixed-Integer Design |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T13465 |
| primary_topic.field.id | https://openalex.org/fields/25 |
| primary_topic.field.display_name | Materials Science |
| primary_topic.score | 0.9890999794006348 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2505 |
| primary_topic.subfield.display_name | Materials Chemistry |
| primary_topic.display_name | Graphite, nuclear technology, radiation studies |
| related_works | https://openalex.org/W2052469048, https://openalex.org/W4321504838, https://openalex.org/W2081475278, https://openalex.org/W2794855054, https://openalex.org/W2005169299, https://openalex.org/W150166259, https://openalex.org/W2606087467, https://openalex.org/W1540477665, https://openalex.org/W2314520391, https://openalex.org/W4375949242 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | pmh:oai:arXiv.org:1804.05429 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/1804.05429 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/1804.05429 |
| primary_location.id | pmh:oai:arXiv.org:1804.05429 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/1804.05429 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/1804.05429 |
| publication_date | 2018-04-15 |
| publication_year | 2018 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 4, 46, 62, 109, 128, 143, 161 |
| abstract_inverted_index.To | 124 |
| abstract_inverted_index.We | 167 |
| abstract_inverted_index.in | 98 |
| abstract_inverted_index.is | 14, 45 |
| abstract_inverted_index.of | 49, 51, 65, 86, 112, 130, 145, 164, 171 |
| abstract_inverted_index.to | 18, 74, 88, 93, 154 |
| abstract_inverted_index.and | 30, 33, 122, 136, 148, 158 |
| abstract_inverted_index.are | 108, 140 |
| abstract_inverted_index.box | 38 |
| abstract_inverted_index.for | 16, 23, 115, 127, 142, 178 |
| abstract_inverted_index.new | 47 |
| abstract_inverted_index.set | 144 |
| abstract_inverted_index.the | 79, 84, 119 |
| abstract_inverted_index.was | 71, 83 |
| abstract_inverted_index.This | 0, 68 |
| abstract_inverted_index.both | 117 |
| abstract_inverted_index.have | 155 |
| abstract_inverted_index.made | 141 |
| abstract_inverted_index.make | 174 |
| abstract_inverted_index.many | 179 |
| abstract_inverted_index.over | 160 |
| abstract_inverted_index.that | 55 |
| abstract_inverted_index.this | 169, 175 |
| abstract_inverted_index.wide | 63, 162 |
| abstract_inverted_index.will | 173 |
| abstract_inverted_index.with | 28 |
| abstract_inverted_index.These | 151 |
| abstract_inverted_index.black | 37 |
| abstract_inverted_index.novel | 69 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.range | 64, 111, 163 |
| abstract_inverted_index.there | 107 |
| abstract_inverted_index.wider | 110 |
| abstract_inverted_index.Gnowee | 13, 135 |
| abstract_inverted_index.a\nset | 50 |
| abstract_inverted_index.across | 61 |
| abstract_inverted_index.design | 85, 165 |
| abstract_inverted_index.energy | 91 |
| abstract_inverted_index.hybrid | 42 |
| abstract_inverted_index.modify | 89 |
| abstract_inverted_index.nearly | 19 |
| abstract_inverted_index.noisy, | 35 |
| abstract_inverted_index.robust | 53 |
| abstract_inverted_index.types, | 132 |
| abstract_inverted_index.within | 118 |
| abstract_inverted_index.Gnowee, | 3 |
| abstract_inverted_index.Gnoweee | 153 |
| abstract_inverted_index.balance | 57 |
| abstract_inverted_index.between | 134 |
| abstract_inverted_index.beyond. | 123 |
| abstract_inverted_index.complex | 76 |
| abstract_inverted_index.neutron | 90 |
| abstract_inverted_index.nuclear | 99, 102, 104, 120 |
| abstract_inverted_index.optimum | 21 |
| abstract_inverted_index.problem | 82, 131 |
| abstract_inverted_index.spaces. | 166 |
| abstract_inverted_index.spectra | 92, 96 |
| abstract_inverted_index.variety | 129 |
| abstract_inverted_index.Gnowee's | 41 |
| abstract_inverted_index.However, | 106 |
| abstract_inverted_index.complex, | 24 |
| abstract_inverted_index.designed | 15 |
| abstract_inverted_index.diverse, | 52 |
| abstract_inverted_index.globally | 20 |
| abstract_inverted_index.modular, | 5 |
| abstract_inverted_index.optimize | 75 |
| abstract_inverted_index.problems | 27 |
| abstract_inverted_index.research | 81 |
| abstract_inverted_index.specific | 94 |
| abstract_inverted_index.superior | 156 |
| abstract_inverted_index.targeted | 95 |
| abstract_inverted_index.algorithm | 10, 70, 176 |
| abstract_inverted_index.community | 121 |
| abstract_inverted_index.desirable | 177 |
| abstract_inverted_index.developed | 73 |
| abstract_inverted_index.framework | 44 |
| abstract_inverted_index.medicine, | 100 |
| abstract_inverted_index.objective | 39 |
| abstract_inverted_index.potential | 113 |
| abstract_inverted_index.problems; | 78 |
| abstract_inverted_index.solutions | 22 |
| abstract_inverted_index.technical | 101 |
| abstract_inverted_index.(Available | 11 |
| abstract_inverted_index.algorithms | 139 |
| abstract_inverted_index.anticipate | 168 |
| abstract_inverted_index.forensics, | 103 |
| abstract_inverted_index.heuristics | 54 |
| abstract_inverted_index.high-cost, | 34 |
| abstract_inverted_index.introduces | 2 |
| abstract_inverted_index.motivating | 80 |
| abstract_inverted_index.strategies | 60 |
| abstract_inverted_index.benchmarks. | 150 |
| abstract_inverted_index.combination | 48 |
| abstract_inverted_index.comparisons | 133 |
| abstract_inverted_index.demonstrate | 125 |
| abstract_inverted_index.engineering | 26 |
| abstract_inverted_index.flexibility | 157 |
| abstract_inverted_index.open-source | 7 |
| abstract_inverted_index.problems.\n | 67 |
| abstract_inverted_index.wide\nrange | 170 |
| abstract_inverted_index.applications | 114 |
| abstract_inverted_index.optimization | 9, 66 |
| abstract_inverted_index.specifically | 72 |
| abstract_inverted_index.Python-based, | 6 |
| abstract_inverted_index.applicability | 172 |
| abstract_inverted_index.appropriately | 56 |
| abstract_inverted_index.combinatorial | 31, 149 |
| abstract_inverted_index.metaheuristic | 43, 138 |
| abstract_inverted_index.mixed-integer | 29 |
| abstract_inverted_index.discontinuous, | 36 |
| abstract_inverted_index.mixed-integer, | 147 |
| abstract_inverted_index.physics,\netc. | 105 |
| abstract_inverted_index.applications.\n | 181 |
| abstract_inverted_index.design\nvectors | 32 |
| abstract_inverted_index.intensification | 59 |
| abstract_inverted_index.nuclear\ndesign | 77 |
| abstract_inverted_index.this\nalgorithm | 116 |
| abstract_inverted_index.for\napplications | 97 |
| abstract_inverted_index.Gnowee's\nbehavior | 126 |
| abstract_inverted_index.rapid\nconvergence | 17 |
| abstract_inverted_index.material\nstack-ups | 87 |
| abstract_inverted_index.complex\nengineering | 180 |
| abstract_inverted_index.constrained\nnuclear | 25 |
| abstract_inverted_index.diversification\nand | 58 |
| abstract_inverted_index.results\ndemonstrate | 152 |
| abstract_inverted_index.eighteen\ncontinuous, | 146 |
| abstract_inverted_index.hybrid\nmetaheuristic | 8 |
| abstract_inverted_index.function\nevaluations. | 40 |
| abstract_inverted_index.several\nwell-established | 137 |
| abstract_inverted_index.convergence\ncharacteristics | 159 |
| abstract_inverted_index.from\nhttps://github.com/SlaybaughLab/Gnowee). | 12 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.22408557 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |