The Exploration of Hybrid Metaheuristics-Based Approaches: A Bibliometric Analysis

Abstract

The rapid evolution of computational intelligence has driven significant interest in hybrid metaheuristics, which combine multiple optimization algorithms to solve complicated problems more efficiently. This study performs a comprehensive bibliometric analysis of hybrid metaheuristics-based approaches aimed at exploring the landscape in scientific literature. Employing an analysis of publications, this study maps the historical development, key terms and the most dominant works in this field. The analysis is conducted using data extracted from the bibliometric database, which is Web of Science (WoS), covering publications from 2020 to 2024 with a keyword associated with hybrid metaheuristics. Utilizing the R Studio with Bibliometrix package and Shiny UI, the bibliometric study uncovered the thematic structure of the research area using co-occurrence network, revealed the intercorrelation concepts, represented the central themes and the evolution of research focus over 5 years through word clouds, Sankey diagram and others. The study findings indicate a growing and broader trend towards the integration of metaheuristics and optimization algorithms, echoing the interdisciplinary nature of this domain. The findings also highlighted the diversity of applications in this field such as artificial intelligence and cloud computing, underscoring the relevancy of hybrid metaheuristics. The identification of leading authors and institutions contributed to a better insight of their contributions in this field. The overall knowledge of this bibliometric study is expected to guide the researchers in their future study to identify the emerging trends and understand the impact of their work.