The Scientific Landscape of Hyper-Heuristics: A Bibliometric Analysis Based on Scopus

Abstract

Hyper-heuristics emerged as a broader metaheuristic framework to address the limitations of traditional optimization heuristics. By abstracting the design of low-level heuristics, hyper-heuristics offer a flexible and adaptable approach to solving complex problems. This study conducts a bibliometric analysis of the hyper-heuristic-algorithms related literature indexed in the Scopus database to map its evolution, identify key research trends, and pinpoint influential authors and journals. The study encompasses document growth over time, predominant author keywords, high-impact journals, and prolific authors ranked by publication count and citation impact. A detailed examination of author keywords unveils the core research themes within the hyper-heuristic domain. The findings of this study provide valuable insights into the current literature in hyper-heuristic research and offer guidance for novice and experienced researchers.