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Reseach Article

Geometric-inspired Particle Swarm Optimization (PSO) for Classification Tasks

by Enoch Opanin Gyamfi, Zhiguang Qin, Juliana Mantebea Danso, Daniel Adu-Gyamfi, Nelson Opoku-Mensah, Noble Arden Elorm Kuadey, Daniel Konin
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 53
Year of Publication: 2022
Authors: Enoch Opanin Gyamfi, Zhiguang Qin, Juliana Mantebea Danso, Daniel Adu-Gyamfi, Nelson Opoku-Mensah, Noble Arden Elorm Kuadey, Daniel Konin

Enoch Opanin Gyamfi, Zhiguang Qin, Juliana Mantebea Danso, Daniel Adu-Gyamfi, Nelson Opoku-Mensah, Noble Arden Elorm Kuadey, Daniel Konin . Geometric-inspired Particle Swarm Optimization (PSO) for Classification Tasks. International Journal of Computer Applications. 183, 53 ( Feb 2022), 32-40. DOI=10.5120/ijca2022921954

@article{ 10.5120/ijca2022921954,
author = { Enoch Opanin Gyamfi, Zhiguang Qin, Juliana Mantebea Danso, Daniel Adu-Gyamfi, Nelson Opoku-Mensah, Noble Arden Elorm Kuadey, Daniel Konin },
title = { Geometric-inspired Particle Swarm Optimization (PSO) for Classification Tasks },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2022 },
volume = { 183 },
number = { 53 },
month = { Feb },
year = { 2022 },
issn = { 0975-8887 },
pages = { 32-40 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2022921954 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:19:51.284807+05:30
%A Enoch Opanin Gyamfi
%A Zhiguang Qin
%A Juliana Mantebea Danso
%A Daniel Adu-Gyamfi
%A Nelson Opoku-Mensah
%A Noble Arden Elorm Kuadey
%A Daniel Konin
%T Geometric-inspired Particle Swarm Optimization (PSO) for Classification Tasks
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 53
%P 32-40
%D 2022
%I Foundation of Computer Science (FCS), NY, USA

The ultimate performance of particle swarm optimization is influenced by hyper-parameters like the inertia, cognitive and social coefficient values. These hyper-parameters have a significant effect on search capability of the particle swarm optimization. When looking at previous studies that are carried out to calculate these coefficients, none of these studies has been inspired by geometric techniques to illustrate for the influence of these components on best position realization. In this, article a geometric approach to how the allocation of social, cognitive and inertia regions on a search space enables particles to move to their best positions at every iteration time. In experiment and benchmark tests, the study validates the applicability of the proposed approach to classification problem using EMNIST dataset. The modified PSO approach gives successful results in separating data into appropriate classes which confirms that the proposed method is highly competitive in guiding the directional movement of the particles towards the best positions.

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Index Terms

Computer Science
Information Sciences


Particle swarm optimization geometric classification sub-swarm