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

• Main Authors: Bevins, James, Slaybaugh, Rachel
• Format: Journal Article
• Language: English
• Published: 15-04-2018
• Subjects: Computer Science - Neural and Evolutionary Computing
• DOI: 10.48550/arxiv.1804.05429

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.