Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE Gaussian bare-bones gradient-based optimization: Towards mitigating the performance concerns Zenglin Qiao, Zenglin Qiao orcid.org/0000-0002-3793-7150 School of Emergency Management, Institute of Disaster Prevention, Langfang, ChinaSearch for more papers by this authorWeifeng Shan, Corresponding Author Weifeng Shan william.shan@gmail.com chenhuiling.jlu@gmail.com tengyt@cea-igp.ac.cn orcid.org/0000-0002-9658-9483 School of Emergency Management, Institute of Disaster Prevention, Langfang, China Institute of Geophysics, China Earthquake Administration, Beijing, China Correspondence Weifeng Shan, School of Emergency Management, Institute of Disaster Prevention, 065201 Langfang, China. Email: william.shan@gmail.com Huiling Chen, Department of Computer Science and Artificial Intelligence, Wenzhou University, 325035 Wenzhou, China. Email: chenhuiling.jlu@gmail.com Yuntian Teng, Institute of Geophysics, China Earthquake Administration, 100081 Beijing, China. Email: tengyt@cea-igp.ac.cnSearch for more papers by this authorNan Jiang, Nan Jiang orcid.org/0000-0003-1712-1872 College of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, ChinaSearch for more papers by this authorAli Asghar Heidari, Ali Asghar Heidari orcid.org/0000-0001-6938-9948 Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, ChinaSearch for more papers by this authorHuiling Chen, Corresponding Author Huiling Chen william.shan@gmail.com chenhuiling.jlu@gmail.com tengyt@cea-igp.ac.cn orcid.org/0000-0002-7714-9693 Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China Correspondence Weifeng Shan, School of Emergency Management, Institute of Disaster Prevention, 065201 Langfang, China. Email: william.shan@gmail.com Huiling Chen, Department of Computer Science and Artificial Intelligence, Wenzhou University, 325035 Wenzhou, China. Email: chenhuiling.jlu@gmail.com Yuntian Teng, Institute of Geophysics, China Earthquake Administration, 100081 Beijing, China. Email: tengyt@cea-igp.ac.cnSearch for more papers by this authorYuntian Teng, Corresponding Author Yuntian Teng william.shan@gmail.com chenhuiling.jlu@gmail.com tengyt@cea-igp.ac.cn orcid.org/0000-0001-7810-4991 Institute of Geophysics, China Earthquake Administration, Beijing, China Correspondence Weifeng Shan, School of Emergency Management, Institute of Disaster Prevention, 065201 Langfang, China. Email: william.shan@gmail.com Huiling Chen, Department of Computer Science and Artificial Intelligence, Wenzhou University, 325035 Wenzhou, China. Email: chenhuiling.jlu@gmail.com Yuntian Teng, Institute of Geophysics, China Earthquake Administration, 100081 Beijing, China. Email: tengyt@cea-igp.ac.cnSearch for more papers by this authorHamza Turabieh, Hamza Turabieh orcid.org/0000-0002-8103-563X Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaSearch for more papers by this authorMajdi Mafarja, Majdi Mafarja orcid.org/0000-0002-0387-8252 Department of Computer Science, Birzeit University, West Bank, PalestineSearch for more papers by this author Zenglin Qiao, Zenglin Qiao orcid.org/0000-0002-3793-7150 School of Emergency Management, Institute of Disaster Prevention, Langfang, ChinaSearch for more papers by this authorWeifeng Shan, Corresponding Author Weifeng Shan william.shan@gmail.com chenhuiling.jlu@gmail.com tengyt@cea-igp.ac.cn orcid.org/0000-0002-9658-9483 School of Emergency Management, Institute of Disaster Prevention, Langfang, China Institute of Geophysics, China Earthquake Administration, Beijing, China Correspondence Weifeng Shan, School of Emergency Management, Institute of Disaster Prevention, 065201 Langfang, China. Email: william.shan@gmail.com Huiling Chen, Department of Computer Science and Artificial Intelligence, Wenzhou University, 325035 Wenzhou, China. Email: chenhuiling.jlu@gmail.com Yuntian Teng, Institute of Geophysics, China Earthquake Administration, 100081 Beijing, China. Email: tengyt@cea-igp.ac.cnSearch for more papers by this authorNan Jiang, Nan Jiang orcid.org/0000-0003-1712-1872 College of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, ChinaSearch for more papers by this authorAli Asghar Heidari, Ali Asghar Heidari orcid.org/0000-0001-6938-9948 Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, ChinaSearch for more papers by this authorHuiling Chen, Corresponding Author Huiling Chen william.shan@gmail.com chenhuiling.jlu@gmail.com tengyt@cea-igp.ac.cn orcid.org/0000-0002-7714-9693 Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China Correspondence Weifeng Shan, School of Emergency Management, Institute of Disaster Prevention, 065201 Langfang, China. Email: william.shan@gmail.com Huiling Chen, Department of Computer Science and Artificial Intelligence, Wenzhou University, 325035 Wenzhou, China. Email: chenhuiling.jlu@gmail.com Yuntian Teng, Institute of Geophysics, China Earthquake Administration, 100081 Beijing, China. Email: tengyt@cea-igp.ac.cnSearch for more papers by this authorYuntian Teng, Corresponding Author Yuntian Teng william.shan@gmail.com chenhuiling.jlu@gmail.com tengyt@cea-igp.ac.cn orcid.org/0000-0001-7810-4991 Institute of Geophysics, China Earthquake Administration, Beijing, China Correspondence Weifeng Shan, School of Emergency Management, Institute of Disaster Prevention, 065201 Langfang, China. Email: william.shan@gmail.com Huiling Chen, Department of Computer Science and Artificial Intelligence, Wenzhou University, 325035 Wenzhou, China. Email: chenhuiling.jlu@gmail.com Yuntian Teng, Institute of Geophysics, China Earthquake Administration, 100081 Beijing, China. Email: tengyt@cea-igp.ac.cnSearch for more papers by this authorHamza Turabieh, Hamza Turabieh orcid.org/0000-0002-8103-563X Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaSearch for more papers by this authorMajdi Mafarja, Majdi Mafarja orcid.org/0000-0002-0387-8252 Department of Computer Science, Birzeit University, West Bank, PalestineSearch for more papers by this author First published: 01 October 2021 https://doi.org/10.1002/int.22658Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Gradient-based optimizer (GBO) is a metaphor-free mathematic-based algorithm proposed in recent years. Encouraged by the gradient-based Newton's method, this algorithm combines with population-based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare-bones mechanism, an opposition-based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO-KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO-KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO-KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation