Garra Rufa‐inspired optimization technique

International Journal of Intelligent SystemsVolume 35, Issue 11 p. 1831-1856 RESEARCH ARTICLE Garra Rufa-inspired optimization technique Aqeel S. Jaber, Aqeel S. Jaber Department of Electrical Power Engineering Techniques, Al-Ma'moon University College, Baghdad, IraqSearch for more papers by this authorHayder A. Abdulbari, Corresponding Author Hayder A. Abdulbari abhayder@ump.edu.my Department of Chemical Engineering, College of Engineering, University Malaysia Pahang, Gambang, Pahang, Malaysia Center of Excellence for Advanced Research in Fluid Flow, PahangUniversity Malaysia Pahang, Gambang, Pahang, Malaysia Correspondence Hayder A. Abdulbari, Department of Chemical Engineering, College of Engineering, University Malaysia Pahang, Lebuhraya Tun Razak, Gambang, 26300 Pahang, Malaysia. Email: abhayder@ump.edu.mySearch for more papers by this authorNadheer A. Shalash, Nadheer A. Shalash Department of Electrical Power Engineering Techniques, Al-Ma'moon University College, Baghdad, IraqSearch for more papers by this authorAhmed N. Abdalla, Ahmed N. Abdalla Faculty of Electronic and Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, ChinaSearch for more papers by this author Aqeel S. Jaber, Aqeel S. Jaber Department of Electrical Power Engineering Techniques, Al-Ma'moon University College, Baghdad, IraqSearch for more papers by this authorHayder A. Abdulbari, Corresponding Author Hayder A. Abdulbari abhayder@ump.edu.my Department of Chemical Engineering, College of Engineering, University Malaysia Pahang, Gambang, Pahang, Malaysia Center of Excellence for Advanced Research in Fluid Flow, PahangUniversity Malaysia Pahang, Gambang, Pahang, Malaysia Correspondence Hayder A. Abdulbari, Department of Chemical Engineering, College of Engineering, University Malaysia Pahang, Lebuhraya Tun Razak, Gambang, 26300 Pahang, Malaysia. Email: abhayder@ump.edu.mySearch for more papers by this authorNadheer A. Shalash, Nadheer A. Shalash Department of Electrical Power Engineering Techniques, Al-Ma'moon University College, Baghdad, IraqSearch for more papers by this authorAhmed N. Abdalla, Ahmed N. Abdalla Faculty of Electronic and Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, ChinaSearch for more papers by this author First published: 24 August 2020 https://doi.org/10.1002/int.22274Citations: 3Read 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 Natural selection has inspired researchers to develop and apply several intelligent optimization techniques in the past few decades. Generally, in artificial intelligence optimization, the particles follow a local or global best particle until finding an acceptable solution. In well- developed optimization techniques, such as swarm optimization (PSO) and the firefly algorithm (FA), getting around the initial optimal value of the group and randomly checking the effect of the surrounding points may lead to a better solution than the initial optimal value. The present work was inspired by the fascinating movement of Garra Rufa fish between two immersed legs during a regular "fish massage session." A new optimization approach is proposed and modeled based on the movements of Garra Rufa fish, in which the particles are separated into groups, and the best optimal value leads each group for the group. Also, some of these particles are allowed to change groups depending on the fitness of the leaders of the groups. The suggested strategy is then compared with PSO and FA using multiple test optimization functions, such as the Ackley, Hartmann, Michalewicz, Shubert, Easom, Bohachevsky, and Rastrigin functions. Also, a multiobjective real issue in power system is tested using the proposed methods where the objectives were cumulative voltage deviation and power losses of three weight sets during the selection allocation of distribution generators. The results show that the proposed method provides good data and greater convergence to the optimal point compared with the classical methods for most of the functions tested. Citing Literature Supporting Information Filename Description int22274-sup-0001-Appendices.docx816.4 KB Supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. Volume35, Issue11November 2020Pages 1831-1856 RelatedInformation