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This paper introduces Gnowee, a modular, Python-based, open-source hybridmetaheuristic optimization algorithm (Available fromhttps://github.com/SlaybaughLab/Gnowee). Gnowee is designed for rapidconvergence to nearly globally optimum solutions for complex, constrainednuclear engineering problems with mixed-integer and combinatorial designvectors and high-cost, noisy, discontinuous, black box objective functionevaluations. Gnowee's hybrid metaheuristic framework is a new combination of aset of diverse, robust heuristics that appropriately balance diversificationand intensification strategies across a wide range of optimization problems.  This novel algorithm was specifically developed to optimize complex nucleardesign problems; the motivating research problem was the design of materialstack-ups to modify neutron energy spectra to specific targeted spectra forapplications in nuclear medicine, technical nuclear forensics, nuclear physics,etc. However, there are a wider range of potential applications for thisalgorithm both within the nuclear community and beyond. To demonstrate Gnowee'sbehavior for a variety of problem types, comparisons between Gnowee and severalwell-established metaheuristic algorithms are made for a set of eighteencontinuous, mixed-integer, and combinatorial benchmarks. These resultsdemonstrate Gnoweee to have superior flexibility and convergencecharacteristics over a wide range of design spaces. We anticipate this widerange of applicability will make this algorithm desirable for many complexengineering applications.

Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design