Bibliometric Analysis on Multiobjective Optimization and Metaheuristic Algorithm
bibliographic coupling; bibliometric analysis; co-citation analysis; metaheuristic; multi-objective optimization; optimization problems; VOSviewer
For difficult optimization issues, metaheuristic algorithms are effective methods for obtaining workable solutions quickly. In the past few years, continuous efforts have been put forward by researchers to develop new effective and robust metaheuristic algorithms for solving engineering optimization problems. The research aims to find the advancements made in multi-objective optimization and metaheuristic algorithms. Metadata of 4149 articles were extracted from Scopus from the year 2000 onward and bibliometric analysis was done with the help of the VOSviewer software. It was found that Mirjalili. S. has the highest number of citations (4011). IEEE Access has published the maximum number of documents (128), the University of Tehrans School of Industrial Engineering has contributed the most in this field of research (43 documents), and China has the most contribution among all the countries with 977 documents. In recent years, the terms optimization algorithms, exploration and exploitation, learning systems, decision-making, uncertainty analysis, sustainable development, supply chains, neural networks, forecasting, machine learning, and cloud computing are being mostly used by researchers. 2026 by Apple Academic Press, Inc.
Intelligent Machining: Using Computational Intelligence to Optimize Manufacturing Processes;pp.27-50
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1201/9781774919590-3 https://www.scopus.com/pages/publications/105023793487?origin=resultslist
Saha S.K., School of Business and Management, Christ University, India; Diyaley S., Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India