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An improved particle swarm optimization algorithm for data classification

Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. The main problem PSO faces is premature converg...

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Main Authors: Waqas Haider Bangyal, Kashif Nisar, Tariq Rahim Soomro, Ag Asri Ag Ibrahim, Ghulam Ali Mallah, Nafees Ul Hassan, Najeeb Ur Rehman
Format: Article
Language:English
Published: MDPI AG, Basel, Switzerland 2023
Subjects:
QA75.5-76.95 Electronic computers. Computer science
TK7885-7895 Computer engineering. Computer hardware
Online Access:https://eprints.ums.edu.my/id/eprint/42555/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42555/
https://doi.org/10.3390/app13010283
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spelling my.ums.eprints.425552025-01-07T03:47:42Z https://eprints.ums.edu.my/id/eprint/42555/ An improved particle swarm optimization algorithm for data classification Waqas Haider Bangyal Kashif Nisar Tariq Rahim Soomro Ag Asri Ag Ibrahim Ghulam Ali Mallah Nafees Ul Hassan Najeeb Ur Rehman QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. The main problem PSO faces is premature convergence due to lack of diversity, and it is usually stuck in local minima when dealing with complex real-world problems. In meta-heuristic algorithms, population initialisation is an important factor affecting population diversity and convergence speed. In this study, we propose an improved PSO algorithm variant that enhances convergence speed and population diversity by applying pseudo-random sequences and opposite rank inertia weights instead of using random distributions for initialisation. This paper also presents a novel initialisation population method using a quasi-random sequence (Faure) to create the initialisation of the swarm, and through the opposition-based method, an opposite swarm is generated. We proposed an opposition rank-based inertia weight approach to adjust the inertia weights of particles to increase the performance of the standard PSO. The proposed algorithm (ORIW-PSO-F) has been tested to optimise the weight of the feed-forward neural network for fifteen data sets taken from UCI. The proposed techniques’ experiment result depicts much better performance than other existing techniques. MDPI AG, Basel, Switzerland 2023 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42555/1/FULL%20TEXT.pdf Waqas Haider Bangyal and Kashif Nisar and Tariq Rahim Soomro and Ag Asri Ag Ibrahim and Ghulam Ali Mallah and Nafees Ul Hassan and Najeeb Ur Rehman (2023) An improved particle swarm optimization algorithm for data classification. Applied Sciences, 13. pp. 1-18. https://doi.org/10.3390/app13010283
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
topic QA75.5-76.95 Electronic computers. Computer science
TK7885-7895 Computer engineering. Computer hardware
spellingShingle QA75.5-76.95 Electronic computers. Computer science
TK7885-7895 Computer engineering. Computer hardware
Waqas Haider Bangyal
Kashif Nisar
Tariq Rahim Soomro
Ag Asri Ag Ibrahim
Ghulam Ali Mallah
Nafees Ul Hassan
Najeeb Ur Rehman
An improved particle swarm optimization algorithm for data classification
description Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. The main problem PSO faces is premature convergence due to lack of diversity, and it is usually stuck in local minima when dealing with complex real-world problems. In meta-heuristic algorithms, population initialisation is an important factor affecting population diversity and convergence speed. In this study, we propose an improved PSO algorithm variant that enhances convergence speed and population diversity by applying pseudo-random sequences and opposite rank inertia weights instead of using random distributions for initialisation. This paper also presents a novel initialisation population method using a quasi-random sequence (Faure) to create the initialisation of the swarm, and through the opposition-based method, an opposite swarm is generated. We proposed an opposition rank-based inertia weight approach to adjust the inertia weights of particles to increase the performance of the standard PSO. The proposed algorithm (ORIW-PSO-F) has been tested to optimise the weight of the feed-forward neural network for fifteen data sets taken from UCI. The proposed techniques’ experiment result depicts much better performance than other existing techniques.
format Article
author Waqas Haider Bangyal
Kashif Nisar
Tariq Rahim Soomro
Ag Asri Ag Ibrahim
Ghulam Ali Mallah
Nafees Ul Hassan
Najeeb Ur Rehman
author_facet Waqas Haider Bangyal
Kashif Nisar
Tariq Rahim Soomro
Ag Asri Ag Ibrahim
Ghulam Ali Mallah
Nafees Ul Hassan
Najeeb Ur Rehman
author_sort Waqas Haider Bangyal
title An improved particle swarm optimization algorithm for data classification
title_short An improved particle swarm optimization algorithm for data classification
title_full An improved particle swarm optimization algorithm for data classification
title_fullStr An improved particle swarm optimization algorithm for data classification
title_full_unstemmed An improved particle swarm optimization algorithm for data classification
title_sort improved particle swarm optimization algorithm for data classification
publisher MDPI AG, Basel, Switzerland
publishDate 2023
url https://eprints.ums.edu.my/id/eprint/42555/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42555/
https://doi.org/10.3390/app13010283
_version_ 1821003231120064512
score 13.226497

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