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

TL;DR

Gnowee is a new hybrid metaheuristic algorithm designed for rapid, near-global optimization of complex, constrained, black box problems with mixed-integer and combinatorial variables, especially in nuclear engineering.

Contribution

It introduces a modular, Python-based hybrid metaheuristic framework combining diverse heuristics for improved convergence across various complex optimization problems.

Findings

Gnowee outperforms several established algorithms on benchmark problems.

It demonstrates superior convergence and flexibility across diverse problem types.

The algorithm is applicable to a wide range of engineering optimization tasks.

Abstract

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.