Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networks

Authors

Iyad Abu Doush, American University of Kuwait
Mohammed A. Awadallah, Al-Aqsa University
Mohammed Azmi Al-Betar, Ajman UniversityFollow
Osama Ahmad Alomari, University of Sharjah
Sharif Naser Makhadmeh, Ajman University
Ammar Kamal Abasi, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Zaid Abdi Alkareem Alyasseri, University of Kufa, Information Technology Research and Development CentreFollow

Document Type

Article

Publication Title

Neural Computing and Applications

Abstract

The success of the supervised learning process for feedforward neural networks, especially multilayer perceptron neural network (MLP), depends on the suitable configuration of its controlling parameters (i.e., weights and biases). Normally, the gradient descent method is used to find the optimal values of weights and biases. The gradient descent method suffers from the local optimal trap and slow convergence. Therefore, stochastic approximation methods such as metaheuristics are invited. Coronavirus herd immunity optimizer (CHIO) is a recent metaheuristic human-based algorithm stemmed from the herd immunity mechanism as a way to treat the spread of the coronavirus pandemic. In this paper, an external archive strategy is proposed and applied to direct the population closer to more promising search regions. The external archive is implemented during the algorithm evolution, and it saves the best solutions to be used later. This enhanced version of CHIO is called ACHIO. The algorithm is utilized in the training process of MLP to find its optimal controlling parameters thus empowering their classification accuracy. The proposed approach is evaluated using 15 classification datasets with classes ranging between 2 to 10. The performance of ACHIO is compared against six well-known swarm intelligence algorithms and the original CHIO in terms of classification accuracy. Interestingly, ACHIO is able to produce accurate results that excel other comparative methods in ten out of the fifteen classification datasets and very competitive results for others.

DOI

10.1007/s00521-023-08577-y

Publication Date

4-19-2023

Keywords

Archive technique, CHIO, Coronavirus herd immunity optimizer, Feedforward neural networks, MLP, Optimization

Comments

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OA version (pathway b) Accepted version

12 months embargo

License: Publisher's Bespoke License Published source must be acknowledged with citation Must link to publisher version with DOI Post-prints are subject to Springer Nature re-use terms

Recommended Citation

Abu Doush, I., et al., "Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networks", Neural Computing & Applications, Apr 2023, doi:10.1007/s00521-023-08577-y