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

Norfadzlia Mohd Yusof, Azah Kamilah Muda, Satrya Fajri Pratama, Ramon Carbo-Dorca, Ajith Abraham

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number104574
JournalChemometrics and Intelligent Laboratory Systems
Volume226
DOIs
Publication statusPublished - 15 Jul 2022
Externally publishedYes

Keywords

  • Binary optimization algorithm
  • Descriptors selection
  • Drug classification
  • Time-varying transfer function

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