Unraveling the Efficacy of Queuing Search Against Quadratic Assignment Problem: Comparative Metaheuristic Examination

Abstract

Our research study offers an in-depth examination of the Quadratic Assignment Problem (QAP), casting light on its complexities and challenges. It presents a thorough exploration of the Queuing Search Algorithm (QSA), a robust algorithm rooted in principles of human learning, underscoring its mechanisms and efficacy. The paper further extends its scope by comparing the QSA with two novel powerful metaheuristic algorithms that have demonstrated their dominance and superiority in the field: the Modified Gorilla Troops Algorithm (MGTO) and the Gaining Sharing Knowledge Algorithm (GSK). These comparative analyses underscore the distinct capabilities and potential uses of each algorithm, specifically within the context of the Quadratic Assignment Problem (QAP). Results in this paper which used a benchmark dataset from (QAPLIB) specifically “Nug-12” show that QSA is a suitable robust approach for the Quadratic Assignment Problem.