A scoping review of literature on the application of swarm intelligence in the object classification domain

Nyaradzo Alice Tsedura; Colin Chibaya; Ernest Bhero

doi:10.20525/ijrbs.v12i5.2586

Authors

Nyaradzo Alice Tsedura School of Computer Engineering, University of KwaZulu-Natal, Durban, South Africa https://orcid.org/0009-0006-7836-2855
Colin Chibaya Doctor, Senior Lecturer, Computer Science, Data Science and Information Technology School of Natural and Applied Sciences, Sol Plaatje University, Kimberly, South Africa https://orcid.org/0000-0001-6995-605X
Ernest Bhero Doctor, Senior Lecturer, School of Engineering, University of KwaZulu-Natal, Durban, South Africa

DOI:

https://doi.org/10.20525/ijrbs.v12i5.2586

Keywords:

object classification, swarm intelligence, emergent behaviour, scoping review

Abstract

This scoping review aims to explore the various swarm technologies and how they have been used in the object classification domain with the desire to motivate the design of a generic swarm intelligence ontology based on the components of various swarm technologies. We used the PRISMA-ScR as a guide to our scoping review protocol. We conducted a search across thirteen databases and a random search as well on the internet for articles. We performed screening of all the articles by title to remove duplicates, we further on did a screening by the year of publication to ensure that all articles to be considered were published between 2012 and 2022 and we then did abstract or text synthesis. Our search query retrieved 3224 potential articles from the thirteen databases and 10 articles from a random search on the internet making a total of 3234 articles identified. Deduplication and screening were done on the identified articles and 287 articles which satisfied our inclusion criteria remained. We grouped the articles into three categories namely year of publication, swarm technology and swarm application. The year of publication showed a linear trend line which is an indication of growth in the swarm intelligence domain. Of the six categories of aims we identified we voluntarily chose to ignore articles where the aim was not specified. We noticed that 64.9% of articles were aimed at either modifying or improving. The swarm technology category indicated that 58.54% of the included articles were based on the Particle Swarm Optimization either independently or as part of a hybrid algorithm. 83.97% of the articles used classification as their swarm application. Interesting to note was the appearance of feature selection and optimization in this category. This scoping review gave an overview of how swarm technologies have been used in the object classification domain. Further research can be done by bringing and using existing algorithms in the development of generic swarm intelligence inspired ontologies.

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