Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Training multi-layer perceptron with enhanced brain storm optimization metaheuristics Subject The topic of the resource Algorithm hybridization; Artificial neural network; Brain storm optimization; Metaheuristics; Optimization Description An account of the resource In the domain of artificial neural networks, the learning process represents one of the most challenging tasks. Since the classification accuracy highly depends on the weights and biases, it is crucial to find its optimal or suboptimal values for the problem at hand. However, to a very large search space, it is very difficult to find the proper values of connection weights and biases. Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima. Most commonly, back-propagation is used for multi-layer-perceptron training and it can lead to vanishing gradient issue. As an alternative approach, stochastic optimization algorithms, such as nature-inspired metaheuristics are more reliable for complex optimization tax, such as finding the proper values of weights and biases for neural network training. In this work, we propose an enhanced brain storm optimization-based algorithm for training neural networks. In the simulations, ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm. The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-the-art approaches on the majority of datasets in terms of classification accuracy and convergence speed, due to the capability of balancing the intensification and diversification and avoiding the local minima. The proposed approach obtained the best accuracy on eight out of ten observed dataset, outperforming all other algorithms by 1-2% on average. When mean accuracy is observed, the proposed algorithm dominated on nine out of ten datasets. 2022 Tech Science Press. All rights reserved. Creator An entity primarily responsible for making the resource Bacanin N.; Alhazmi K.; Zivkovic M.; Venkatachalam K.; Bezdan T.; Nebhen J. Source A related resource from which the described resource is derived Computers, Materials and Continua, Vol-70, No. 2, pp. 4199-4215. Publisher An entity responsible for making the resource available Tech Science Press Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.32604/cmc.2022.020449" target="_blank" rel="noreferrer noopener">https://doi.org/10.32604/cmc.2022.020449</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115988217&amp;doi=10.32604%2Fcmc.2022.020449&amp;partnerID=40&amp;md5=9630583bfef0e12cd1ba87e8304eda7c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115988217&amp;doi=10.32604%2fcmc.2022.020449&amp;partnerID=40&amp;md5=9630583bfef0e12cd1ba87e8304eda7c</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 15462218 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bacanin N., Singidunum University, Danijelova, Belgrade, 11000, Serbia; Alhazmi K., National Center for Robotics and IoT, Communication and Information Technology Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, 12371, Saudi Arabia; Zivkovic M., Singidunum University, Danijelova, Belgrade, 11000, Serbia; Venkatachalam K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Bezdan T., Singidunum University, Danijelova, Belgrade, 11000, Serbia; Nebhen J., Prince Sattam Bin Abdulaziz University, College of Computer Engineering and Sciences, Alkharj, 11942, Saudi Arabia