Bibliometric analysis of nature inspired optimization techniques

•Nature-inspired optimization has gained immense popularity over the past 6 decades.•A total of 91,507 articles published between 2016 and 2020 were analyzed.•Metrics like number of publications, citations, and h-index are used for comparison.•China, India, and the US contribute the most to nature-i...

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Bibliographic Details
Published in:Computers & industrial engineering Vol. 169; p. 108161
Main Authors: Dalavi, Amol M., Gomes, Alyssa, Javed Husain, Aaliya
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.07.2022
Subjects:
Bibliometric analysis
Bio-inspired algorithms
Citation analysis
Evolutionary algorithms
Nature inspired optimization algorithms
Optimization
DE
ABC
Nature inspired optimization algorithms
ALO
NIOT
IPO
HS
Bibliometric analysis
Optimization
SA
GSA
SI
GA
EA
GWO
TLBO
Citation analysis
CI
ICA
Evolutionary algorithms
ACO
PSO
BB-BC
SFLA
CS
Bio-inspired algorithms
FA
NSGA
MVF
BA
ISSN:0360-8352, 1879-0550
Online Access:Get full text
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Summary:•Nature-inspired optimization has gained immense popularity over the past 6 decades.•A total of 91,507 articles published between 2016 and 2020 were analyzed.•Metrics like number of publications, citations, and h-index are used for comparison.•China, India, and the US contribute the most to nature-inspired optimization.•Major applications include engineering optimization, AI, and decision sciences. Nature-inspired optimization has gained immense popularity over the past six decades and has been extensively used across various disciplines. This paper aims to statistically evaluate the impact and importance of nature-inspired optimization by presenting an analysis of works published between 2016 and 2020. The data is obtained from Scopus and focuses on metrics like the total number of publications, citations, average citations per publication, and the h-index. Graphical and statistical analysis was carried out using Excel, Python, RAWGraphs, and Tableau Public. All the data in the present work was accessed on 11th August 2021. A total of 91,507 publications were analysed. China, India, and the US are the highest contributors with 27045, 12129, and 8947 publications respectively. The Ministry of Education China has contributed the most to this field, followed by the Chinese Academy of Sciences. The National Natural Science Foundation of China has funded the highest number of works (14.72% publications). Zhang M. is the most productive author with 224 publications. Lecture Notes in Computer Science, Advances in Intelligent Systems and Computing, and IEEE Access are the most productive journals. The top disciplines contributing to research include Computer Science (55.22%), Engineering (48.06%), and Mathematics (27.30%), and the top application areas include optimization, artificial intelligence, and decision sciences. The most popular algorithms include Genetic Algorithms, Simulated Annealing, and Particle Swarm Optimization. This data could prove beneficial to scholars looking for an overview of nature-inspired algorithms to determine future research directions.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2022.108161