Document Type


Date of Degree Completion

Winter 2023

Degree Name

Master of Science (MS)


Computational Science

Committee Chair

Donald Davendra

Second Committee Member

Razvan Andonie

Third Committee Member

Szilard Vajda


Metaheuristic optimization algorithms are heuristics that are capable of creating a "good enough'' solution to a computationally complex problem. Algorithms in this area of study are focused on the process of exploration and exploitation: exploration of the solution space and exploitation of the results that have been found during that exploration, with most resources going toward the former half of the process. The novel Crosshair optimizer developed in this thesis seeks to take advantage of the latter, exploiting the best possible result as much as possible by directly searching the area around that best result with a stochastic approach. This research seeks to prove that the Crosshair Optimizer is comparable, if not better in some aspects, to current established metaheuristics optimization algorithms, not only in obtaining optimal results, but usability in high performance computing, and versatility through the use of multiple randomizers and parameter tuning.