基于鱼群搜索优化方法的有限元模型更新

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-08-10 DOI:10.1109/BRICS-CCI-CBIC.2013.80

I. Boulkabeit, Linda Mthembu, T. Marwala, Fernando Buarque de Lima-Neto

{"title":"基于鱼群搜索优化方法的有限元模型更新","authors":"I. Boulkabeit, Linda Mthembu, T. Marwala, Fernando Buarque de Lima-Neto","doi":"10.1109/BRICS-CCI-CBIC.2013.80","DOIUrl":null,"url":null,"abstract":"A recent nature inspired optimization algorithm, Fish School Search (FSS) is applied to the finite element model (FEM) updating problem. This method is tested on a GARTEUR SM-AG19 aeroplane structure. The results of this algorithm are compared with two other metaheuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It is observed that on average, the FSS and PSO algorithms give more accurate results than the GA. A minor modification to the FSS is proposed. This modification improves the performance of FSS on the FEM updating problem which has a constrained search space.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Finite Element Model Updating Using Fish School Search Optimization Method\",\"authors\":\"I. Boulkabeit, Linda Mthembu, T. Marwala, Fernando Buarque de Lima-Neto\",\"doi\":\"10.1109/BRICS-CCI-CBIC.2013.80\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A recent nature inspired optimization algorithm, Fish School Search (FSS) is applied to the finite element model (FEM) updating problem. This method is tested on a GARTEUR SM-AG19 aeroplane structure. The results of this algorithm are compared with two other metaheuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It is observed that on average, the FSS and PSO algorithms give more accurate results than the GA. A minor modification to the FSS is proposed. This modification improves the performance of FSS on the FEM updating problem which has a constrained search space.\",\"PeriodicalId\":306195,\"journal\":{\"name\":\"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.80\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.80","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

摘要

将一种受自然启发的优化算法鱼群搜索(Fish School Search, FSS)应用于有限元模型更新问题。该方法在GARTEUR SM-AG19飞机结构上进行了试验。将该算法与遗传算法(GA)和粒子群算法(PSO)进行了比较。观察到，平均而言，FSS和PSO算法比遗传算法给出更准确的结果。提出了对金融监督制度的一个小修改。这种改进提高了FSS在有约束搜索空间的有限元更新问题上的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Finite Element Model Updating Using Fish School Search Optimization Method

A recent nature inspired optimization algorithm, Fish School Search (FSS) is applied to the finite element model (FEM) updating problem. This method is tested on a GARTEUR SM-AG19 aeroplane structure. The results of this algorithm are compared with two other metaheuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It is observed that on average, the FSS and PSO algorithms give more accurate results than the GA. A minor modification to the FSS is proposed. This modification improves the performance of FSS on the FEM updating problem which has a constrained search space.

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

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence

自引率

0.00%

发文量