{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gnowee-a-hybrid-metaheuristic-optimization","title":"Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design","arxiv_id":"1804.05429","date":"2018-04-15","proceeding":null,"authors":["James Bevins","Rachel Slaybaugh"],"abstract":"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.","url_abs":"http://arxiv.org/abs/1804.05429v1","url_pdf":"http://arxiv.org/pdf/1804.05429v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gnowee-a-hybrid-metaheuristic-optimization","repo_url":"https://github.com/SlaybaughLab/Gnowee","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"metaheuristic-optimization","task_name":"Metaheuristic Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}