An improved particle swarm optimization algorithm for data classification

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.

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Abstract

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.

Item Type:	Article
Keyword:	Feed-forward neural network, Quasi-random sequence, Opposition rank-based inertia weigh, Particle swarm optimisation
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering > TK7800-8360 Electronics > TK7885-7895 Computer engineering. Computer hardware
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	SITI AZIZAH BINTI IDRIS -
Date Deposited:	07 Jan 2025 11:47
Last Modified:	07 Jan 2025 11:47
URI:	https://eprints.ums.edu.my/id/eprint/42555

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