An efficient improved African vultures optimization algorithm with dimension learning hunting for traveling salesman and large‐scale optimization applications

International Journal of Intelligent SystemsVolume 37, Issue 12 p. 12367-12421 RESEARCH ARTICLE An efficient improved African vultures optimization algorithm with dimension learning hunting for traveling salesman and large-scale optimization applications Narinder Singh, Corresponding Author Narinder Singh [email protected] [email protected] orcid.org/0000-0003-3805-0188 Department of Mathematics, Punjabi University, Patiala, Punjab, India Correspondence Narinder Singh, Department of Mathematics, Punjabi University, Patiala 147002, Punjab, India. Email: [email protected] and [email protected]Search for more papers by this authorEssam H. Houssein, Essam H. Houssein Faculty of Computers and Information, Minia University, Minia, EgyptSearch for more papers by this authorSeyedali Mirjalili, Seyedali Mirjalili Centre for Artificial Intelligence Research and Optimization, Torrens University, Australia Yonsei Frontier Lab, Yonsei University, Seoul, South KoreaSearch for more papers by this authorYankai Cao, Yankai Cao Faculty of Applied Science, UBC Chemical and Biological Engineering, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorGaneshsree Selvachandran, Ganeshsree Selvachandran Department of Actuarial Science and Applied Statistics, Faculty of Business and Management, UCSI University, Jalan Menara Gading, Cheras, Kuala Lumpur, MalaysiaSearch for more papers by this author Narinder Singh, Corresponding Author Narinder Singh [email protected] [email protected] orcid.org/0000-0003-3805-0188 Department of Mathematics, Punjabi University, Patiala, Punjab, India Correspondence Narinder Singh, Department of Mathematics, Punjabi University, Patiala 147002, Punjab, India. Email: [email protected] and [email protected]Search for more papers by this authorEssam H. Houssein, Essam H. Houssein Faculty of Computers and Information, Minia University, Minia, EgyptSearch for more papers by this authorSeyedali Mirjalili, Seyedali Mirjalili Centre for Artificial Intelligence Research and Optimization, Torrens University, Australia Yonsei Frontier Lab, Yonsei University, Seoul, South KoreaSearch for more papers by this authorYankai Cao, Yankai Cao Faculty of Applied Science, UBC Chemical and Biological Engineering, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorGaneshsree Selvachandran, Ganeshsree Selvachandran Department of Actuarial Science and Applied Statistics, Faculty of Business and Management, UCSI University, Jalan Menara Gading, Cheras, Kuala Lumpur, MalaysiaSearch for more papers by this author First published: 30 November 2022 https://doi.org/10.1002/int.23091 [Correction added on 15 December 2022, after first online publication affiliation for third author has been changed to 3 and 4 and figures 3,16,17 and 18 have been replaced.] Read 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 Abstract Exploring the finest shortest-path traveling salesman optimization application is a typical NP-hard problem. Similarly the solution of the large-scale optimization applications is also a big challenging issue in front of scientists. First, African Vultures Optimization Algorithm (AVOA) was developed to resolve continuous applications where it performed fine. In the last few months, many enhanced strategies of AVOA have been offered in recent literature works and it has been extensively utilized to resolve large-scale engineering optimization applications. This study offers a newly modified dimension learning hunting (DLH)-based AVOA called DLHAV algorithm to resolve highly complex continuous and discrete applications. It helps improve the imbalance amid the hunting (or exploitation) and search (or exploration), the lack of crowd diversity, slow convergence speed, trapping in local optima, and early convergence of the AVOA variant. The proposed strategy benefits from a newly driven approach called the DLH search approach congenital from the separate exploitation behavior of vultures in the search domain. DLH exploration strategy utilizes a distinct method to make the best neighborhood for all vultures in which the nearest member information can be supplied amid vultures. DLH helps in improving the balance amid global and local and sustains diversity. To scrutinize the performance of DLHAV, the solutions of the DLHAV method are verified on 29-CEC'17 and 10-CEC'20 with familiar comparative methods and some other classical optimization approaches over many familiar traveling salesman problem/large-scale instances. With the intention of attaining unbiased and rigorous comparison, descriptive statistics such as standard deviation and mean have been applied, and the statistical Friedman test is also conducted. The experimental solution carried out in this study has revealed that the proposed algorithm outperforms significantly over the other alternative optimizers. CONFLICT OF INTEREST The authors declare is no conflict of interest. Open Research DATA AVAILABILITY STATEMENT No. Research data are not shared. Volume37, Issue12December 2022Pages 12367-12421 RelatedInformation