GTO的评估:基于约束基准函数的性能评估,以及三杆桁架设计问题的优化

Erdal Eker
{"title":"GTO的评估:基于约束基准函数的性能评估,以及三杆桁架设计问题的优化","authors":"Erdal Eker","doi":"10.24012/dumf.1211918","DOIUrl":null,"url":null,"abstract":"The aim of this paper is to show that the artificial gorilla troops optimization (GTO) algorithm, as an optimizer, can cope with test functions such as CEC2019, and also to best optimize the three bar truss design problem as a constrained optimization problem. As a method, two statistical measures such as the best values provided by the algorithms and the standard deviation showing the distance between the values were studied. At the same time, the convergence rate of the algorithms compared by the convergence curves were examined. For this purpose, it has been competed against two other swarm-based algorithms, sine-cosine algorithm (SCA) and golden eagle optimization (GEO). The optimization of the three bar truss design problem, which is another side of the study, has been made. The GTO algorithm reached the best values in the optimization of the parameters of the problem. In addition to the convergence curve, statistical results have examined, and the advantages of GTO are revealed through box-plot figures that evaluate the relationship between median and quartiles and the distribution among all results.","PeriodicalId":158576,"journal":{"name":"DÜMF Mühendislik Dergisi","volume":"86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessment of GTO: Performance evaluation via constrained benchmark function, and Optimized of Three Bar Truss Design Problem\",\"authors\":\"Erdal Eker\",\"doi\":\"10.24012/dumf.1211918\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this paper is to show that the artificial gorilla troops optimization (GTO) algorithm, as an optimizer, can cope with test functions such as CEC2019, and also to best optimize the three bar truss design problem as a constrained optimization problem. As a method, two statistical measures such as the best values provided by the algorithms and the standard deviation showing the distance between the values were studied. At the same time, the convergence rate of the algorithms compared by the convergence curves were examined. For this purpose, it has been competed against two other swarm-based algorithms, sine-cosine algorithm (SCA) and golden eagle optimization (GEO). The optimization of the three bar truss design problem, which is another side of the study, has been made. The GTO algorithm reached the best values in the optimization of the parameters of the problem. In addition to the convergence curve, statistical results have examined, and the advantages of GTO are revealed through box-plot figures that evaluate the relationship between median and quartiles and the distribution among all results.\",\"PeriodicalId\":158576,\"journal\":{\"name\":\"DÜMF Mühendislik Dergisi\",\"volume\":\"86 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"DÜMF Mühendislik Dergisi\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24012/dumf.1211918\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"DÜMF Mühendislik Dergisi","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24012/dumf.1211918","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文的目的是为了证明人工大猩猩部队优化(GTO)算法作为一个优化器,可以应对CEC2019等测试函数,并将三杆桁架设计问题作为约束优化问题进行最佳优化。作为一种方法,研究了算法提供的最优值和表示值之间距离的标准差这两个统计度量。同时,通过收敛曲线比较了各算法的收敛速度。为此,它与另外两种基于群的算法,正弦余弦算法(SCA)和金鹰优化(GEO)进行了竞争。对三杆桁架的优化设计问题进行了研究,这是本文研究的另一个方面。GTO算法在优化问题参数时达到了最优值。除了收敛曲线外,还对统计结果进行了检验,并通过箱线图来评估中位数与四分位数之间的关系以及所有结果之间的分布,从而揭示了GTO的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of GTO: Performance evaluation via constrained benchmark function, and Optimized of Three Bar Truss Design Problem
The aim of this paper is to show that the artificial gorilla troops optimization (GTO) algorithm, as an optimizer, can cope with test functions such as CEC2019, and also to best optimize the three bar truss design problem as a constrained optimization problem. As a method, two statistical measures such as the best values provided by the algorithms and the standard deviation showing the distance between the values were studied. At the same time, the convergence rate of the algorithms compared by the convergence curves were examined. For this purpose, it has been competed against two other swarm-based algorithms, sine-cosine algorithm (SCA) and golden eagle optimization (GEO). The optimization of the three bar truss design problem, which is another side of the study, has been made. The GTO algorithm reached the best values in the optimization of the parameters of the problem. In addition to the convergence curve, statistical results have examined, and the advantages of GTO are revealed through box-plot figures that evaluate the relationship between median and quartiles and the distribution among all results.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信