The Evolution of Metaheuristic Research: A Bibliometrics Analysis of Research Trends in Computer Sciences

Abstract

The purpose of this paper is to examine the development of research trends and to propose potential future paths by conducting a bibliometric analysis of metaheuristic studies. Enhancing the sector's importance and giving readers a clear understanding of its development. The research uses a bibliometric approach, collecting its information from the Scopus database. Articles published in academic journals on metaheuristic research between the years 2015 and 2023 will be analyzed for this study. Data is collected, analyzed, and interpreted so that conclusions can be drawn. Important insights into the development of metaheuristic studies emerge from the analysis. Global optimization, heuristic approaches, scheduling, genetic algorithms, evolutionary algorithms, and benchmarking are only a few of the overarching research issues that this study reveals. There is a growing interest in new fields of study, such as adaptive neuro-fuzzy inference, forecasting, feature selection, biomimetic, and the exploration-exploitation trade-off. The results shed light on where the field of metaheuristics is at the moment and where it needs to go in the future. The study's dependence on Scopus data and its refusal to include data from other sources are two of its acknowledged drawbacks. It draws attention to the need for more study to address these limitations and recommends possible directions for future studies, such as adding different data and looking at different applications. This research appends to the frame of knowledge by providing a thorough bibliometric examination of how metaheuristic studies have developed. It emphasizes the value of metaheuristic algorithms as optimization instruments and their influence on resolving complex issues. Researchers, practitioners, and policymakers can all benefit from these findings, as they shed light on current and future paths in metaheuristics research.