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

• Published in: Neural computing & applications Vol. 35; no. 21; pp. 15923 - 15941
• Main Authors: Abu Doush, Iyad, Awadallah, Mohammed A., Al-Betar, Mohammed Azmi, Alomari, Osama Ahmad, Makhadmeh, Sharif Naser, Abasi, Ammar Kamal, Alyasseri, Zaid Abdi Alkareem
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2023; Springer Nature B.V
• Subjects: Algorithms; Archives & records; Artificial Intelligence; Artificial neural networks; Bias; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Coronaviruses; Data Mining and Knowledge Discovery; Datasets; Evolutionary algorithms; Herd immunity; Heuristic methods; Image Processing and Computer Vision; Machine learning; Multilayer perceptrons; Neural networks; Optimization; Original; Original Article; Parameters; Probability and Statistics in Computer Science; Supervised learning; Swarm intelligence; MLP; Coronavirus herd immunity optimizer; Feedforward neural networks; CHIO; Optimization; Archive technique
• ISSN: 0941-0643; 1433-3058; 1433-3058
• DOI: 10.1007/s00521-023-08577-y

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.