Effects of genetic algorithm parameters on multiobjective optimization algorithm applied to system identification problem

M. Z. Zakaria, H. Jamaluddin, R. Ahmad, A. Muhaimin
{"title":"Effects of genetic algorithm parameters on multiobjective optimization algorithm applied to system identification problem","authors":"M. Z. Zakaria, H. Jamaluddin, R. Ahmad, A. Muhaimin","doi":"10.1109/ICMSAO.2011.5775624","DOIUrl":null,"url":null,"abstract":"The growing interest in multiobjective optimization algorithms and system identification resulted in a huge research area. System identification is about developing a mathematical model for representing the system observed. This paper describes the effects of genetic algorithm parameters used in multiobjective optimization algorithm (MOO) that is applied to system identification problem. Two simulated linear systems with known model structure were considered for representing the system identification problem. The performance metrics used in this study are convergence and diversity metric. These metrics show the performance of MOO when GA parameters are varied. The simulation results show the effects of GA parameter on MOO performance. A right combination of GA parameters used in MOO is shown in this study.","PeriodicalId":6383,"journal":{"name":"2011 Fourth International Conference on Modeling, Simulation and Applied Optimization","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Fourth International Conference on Modeling, Simulation and Applied Optimization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMSAO.2011.5775624","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

The growing interest in multiobjective optimization algorithms and system identification resulted in a huge research area. System identification is about developing a mathematical model for representing the system observed. This paper describes the effects of genetic algorithm parameters used in multiobjective optimization algorithm (MOO) that is applied to system identification problem. Two simulated linear systems with known model structure were considered for representing the system identification problem. The performance metrics used in this study are convergence and diversity metric. These metrics show the performance of MOO when GA parameters are varied. The simulation results show the effects of GA parameter on MOO performance. A right combination of GA parameters used in MOO is shown in this study.
遗传算法参数对多目标优化算法应用于系统辨识问题的影响
对多目标优化算法和系统辨识的兴趣日益浓厚,形成了一个巨大的研究领域。系统识别是关于开发一个数学模型来表示所观察到的系统。本文讨论了遗传算法参数在多目标优化算法(MOO)中对系统辨识问题的影响。考虑两个模型结构已知的仿真线性系统来表示系统辨识问题。本研究使用的绩效指标为收敛性指标和多样性指标。这些指标显示了当GA参数变化时MOO的性能。仿真结果显示了遗传算法参数对MOO性能的影响。本研究给出了用于MOO的GA参数的正确组合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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