Evolutionary multi-objective optimisation: a bibliometric study
type: Item
instance of: scholarly article
title: Evolutionary multi-objective optimisation: a bibliometric study
zbMATH Open document ID: 1486.90169
DOI: 10.1504/IJMOR.2021.119965
author: Mohamed Amine Abdeljaouad
author: Zied Bahroun
author: Slim Bechikh
published in: International Journal of Mathematics in Operational Research
publication date: 2022-02-02T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 90C29
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 62P99
zbMATH DE Number: 7468409
zbMATH Keywords: bibliometric
zbMATH Keywords: evolutionary
zbMATH Keywords: metaheuristic
zbMATH Keywords: multi-objective
zbMATH Keywords: network analysis
MaRDI profile type: Publication
full work available at URL: ijmor.2021.119965
OpenAlex ID: W4205354071
description: scientific article; zbMATH DE number 7468409