Pranav Mehta, S. M. Sait, B. Yildiz, Mehmet Umut Erdaş, Mehmet Kopar, Ali Rıza Yıldız
{"title":"A new enhanced mountain gazelle optimizer and artificial neural network for global optimization of mechanical design problems","authors":"Pranav Mehta, S. M. Sait, B. Yildiz, Mehmet Umut Erdaş, Mehmet Kopar, Ali Rıza Yıldız","doi":"10.1515/mt-2023-0332","DOIUrl":null,"url":null,"abstract":"\n Nature-inspired metaheuristic optimization algorithms have many applications and are more often studied than conventional optimization techniques. This article uses the mountain gazelle optimizer, a recently created algorithm, and artificial neural network to optimize mechanical components in relation to vehicle component optimization. The family formation, territory-building, and food-finding strategies of mountain gazelles serve as the major inspirations for the algorithm. In order to optimize various engineering challenges, the base algorithm (MGO) is hybridized with the Nelder–Mead algorithm (HMGO-NM) in the current work. This considered algorithm was applied to solve four different categories, namely automobile, manufacturing, construction, and mechanical engineering optimization tasks. Moreover, the obtained results are compared in terms of statistics with well-known algorithms. The results and findings show the dominance of the studied algorithm over the rest of the optimizers. This being said the HMGO algorithm can be applied to a common range of applications in various industrial and real-world problems.","PeriodicalId":18231,"journal":{"name":"Materials Testing","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Testing","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1515/mt-2023-0332","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0
Abstract
Nature-inspired metaheuristic optimization algorithms have many applications and are more often studied than conventional optimization techniques. This article uses the mountain gazelle optimizer, a recently created algorithm, and artificial neural network to optimize mechanical components in relation to vehicle component optimization. The family formation, territory-building, and food-finding strategies of mountain gazelles serve as the major inspirations for the algorithm. In order to optimize various engineering challenges, the base algorithm (MGO) is hybridized with the Nelder–Mead algorithm (HMGO-NM) in the current work. This considered algorithm was applied to solve four different categories, namely automobile, manufacturing, construction, and mechanical engineering optimization tasks. Moreover, the obtained results are compared in terms of statistics with well-known algorithms. The results and findings show the dominance of the studied algorithm over the rest of the optimizers. This being said the HMGO algorithm can be applied to a common range of applications in various industrial and real-world problems.
期刊介绍:
Materials Testing is a SCI-listed English language journal dealing with all aspects of material and component testing with a special focus on transfer between laboratory research into industrial application. The journal provides first-hand information on non-destructive, destructive, optical, physical and chemical test procedures. It contains exclusive articles which are peer-reviewed applying respectively high international quality criterions.