Improved swarm intelligence algorithms with time-varying modified Sigmoid transfer function for Amphetamine-type stimulants drug classification

Muda, Azah Kamilah and Mohd Yusof, Norfadzlia and Pratama, Satrya Fajri and Carbo-Dorca, Ramon and Abraham, Ajith (2022) Improved swarm intelligence algorithms with time-varying modified Sigmoid transfer function for Amphetamine-type stimulants drug classification. Chemometrics and Intelligent Laboratory Systems, 226. pp. 1-10. ISSN 0169-7439

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IMPROVED SWARM INTELLIGENCE ALGORITHMS WITH TIME-VARYING MODIFIED SIGMOID TRANSFER FUNCTION FOR AMPHETAMINE-TYPE STIMULANTS DRUG CLASSIFI CATION-COMPRESSED.PDF
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Official URL: https://reader.elsevier.com/reader/sd/pii/S0169743...

Abstract

Swarm-intelligence (SI) algorithms have received great attention in addressing various binary optimization problems such as feature selection. In this article, a new time-varying modified Sigmoid transfer function with two time-varying updating schemes is proposed as the binarization method for particle swarm optimization (PSO), grey wolf optimization algorithm (GWO), whale optimization algorithm (WOA), harris hawk optimization (HHO), and manta-ray foraging optimization (MRFO). The new binary algorithms, BPSO, BGWOA, BWOA, BHHO, and BMRFO algorithms are utilized for solving the descriptors selection problem in supervised Amphetamine-type Stimulants (ATS) drug classification task. The goal of this study is to improve the speed of convergence and classification accuracy. To evaluate the performance of the proposed methods, experiments were carried out on a specific chemical dataset containing molecular descriptors of ATS and non-ATS drugs. The results obtained showed that the proposed methods’ performances on the chemical dataset are promising in near to optimal convergence, fast computation, increased classification accuracy, and enormous reduction in descriptor size.

Item Type:	Article
Uncontrolled Keywords:	Descriptors selection, Time-varying transfer function, Binary optimization algorithm, Drug classification
Divisions:	Faculty of Information and Communication Technology
Depositing User:	mr eiisaa ahyead
Date Deposited:	23 Feb 2023 16:42
Last Modified:	23 Feb 2023 16:42
URI:	http://eprints.utem.edu.my/id/eprint/26343
Statistic Details:	View Download Statistic

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