Title: Evolutionary multi-objective optimisation: a bibliometric study

Authors: Mohamed Amine Abdeljaouad; Zied Bahroun; Slim Bechikh

Addresses: University of Tunis El Manar, F.S.T., LIP2-LR99ES18, 2092 Tunis, Tunisia ' Department of Industrial Engineering, College of Engineering, American University of Sharjah, P.O. Box 26666, Sharjah, UAE ' SMART Lab, University of Tunis, ISG, Tunis, Tunisia

Abstract: Evolutionary algorithms are among the most popular methods for solving optimisation problems. The use of metaheuristics emulating the natural evolution of living organisms dates back to the 1970s. In this study, a particular focus is put to use these evolutionary techniques to solve multi-objective optimisation problems. Based on a bibliometric and network analysis, the paper provides a review of 5,549 scientific publications (journal articles and book chapters) on the field before June 2019. The top contributing authors, journals, organisations, and countries are identified, as well as the most used key-words related to the topic. Then, the most influential papers are compared on the basis of citations and PageRank. The established research clusters and emergent research directions are also discussed.

Keywords: bibliometric; evolutionary; metaheuristic; multi-objective; network analysis.

DOI: 10.1504/IJMOR.2021.119965

International Journal of Mathematics in Operational Research, 2021 Vol.20 No.3, pp.328 - 354

Received: 07 Jun 2020
Accepted: 03 Aug 2020
Published online: 04 Jan 2022 *

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