Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networks.
Iyad Abu Doush, Mohammed A Awadallah, Mohammed Azmi Al-Betar, Osama Ahmad Alomari, Sharif Naser Makhadmeh, Ammar Kamal Abasi, Zaid Abdi Alkareem Alyasseri
Author Information
Iyad Abu Doush: College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait. ORCID
Mohammed A Awadallah: Department of Computer Science, Al-Aqsa University, Gaza, Palestine.
Mohammed Azmi Al-Betar: Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates.
Osama Ahmad Alomari: MLALP Research Group, University of Sharjah, Sharjah, UAE.
Sharif Naser Makhadmeh: Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates.
Ammar Kamal Abasi: Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates.
Zaid Abdi Alkareem Alyasseri: Information Technology Research and Development Center (ITRDC), University of Kufa, Najaf, Iraq.
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.
