Bibliometric analysis of nature inspired optimization techniques

Abstract

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.