用正交配置和多目标优化随机分形搜索求解分数最优控制问题

J. V. C. F. Lima, F. S. Lobato, V. Steffen Jr
{"title":"用正交配置和多目标优化随机分形搜索求解分数最优控制问题","authors":"J. V. C. F. Lima,&nbsp;F. S. Lobato,&nbsp;V. Steffen Jr","doi":"10.1007/s43674-021-00003-x","DOIUrl":null,"url":null,"abstract":"<div><p>In this contribution the solution of Fractional Optimal Control Problems (FOCP) by using the Orthogonal Collocation Method (OCM) and the Multi-objective Optimization Stochastic Fractal Search (MOSFS) algorithm is investigated. For this purpose, three classical case studies on engineering are considered. Initially, the concentration profiles of laccase enzyme production process are analyzed to evaluate the influence of fractional order. Then, two classical FOCP (Catalyst Mixing and Batch Reactor) are solved by using the association between OCM and MOSFS approachesthrough the formulation and solution of a multi-objective optimization problem. The results indicate that the variation of the fractional order impliesdifferent values for the original objective function. In addition, physicallyincoherent profiles can be obtained by considering the fluctuation of the fractional order. Finally, the proposed MOSFS is considered as apromising methodology to solve multi-objective optimization problems.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"1 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s43674-021-00003-x","citationCount":"1","resultStr":"{\"title\":\"Solution of Fractional Optimal Control Problems by using orthogonal collocation and Multi-objective Optimization Stochastic Fractal Search\",\"authors\":\"J. V. C. F. Lima,&nbsp;F. S. Lobato,&nbsp;V. Steffen Jr\",\"doi\":\"10.1007/s43674-021-00003-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this contribution the solution of Fractional Optimal Control Problems (FOCP) by using the Orthogonal Collocation Method (OCM) and the Multi-objective Optimization Stochastic Fractal Search (MOSFS) algorithm is investigated. For this purpose, three classical case studies on engineering are considered. Initially, the concentration profiles of laccase enzyme production process are analyzed to evaluate the influence of fractional order. Then, two classical FOCP (Catalyst Mixing and Batch Reactor) are solved by using the association between OCM and MOSFS approachesthrough the formulation and solution of a multi-objective optimization problem. The results indicate that the variation of the fractional order impliesdifferent values for the original objective function. In addition, physicallyincoherent profiles can be obtained by considering the fluctuation of the fractional order. Finally, the proposed MOSFS is considered as apromising methodology to solve multi-objective optimization problems.</p></div>\",\"PeriodicalId\":72089,\"journal\":{\"name\":\"Advances in computational intelligence\",\"volume\":\"1 4\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1007/s43674-021-00003-x\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in computational intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s43674-021-00003-x\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in computational intelligence","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s43674-021-00003-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文研究了用正交配置法和多目标优化随机分形搜索算法求解分数最优控制问题。为此,考虑了三个经典的工程案例研究。首先,分析漆酶生产过程中的浓度分布,以评估分数阶数的影响。然后,利用OCM和MOSFS方法之间的关联,通过多目标优化问题的公式化和求解,求解了两个经典的FOCP(催化剂混合和间歇反应器)。结果表明,分数阶的变化对原始目标函数意味着不同的值。此外,可以通过考虑分数阶的波动来获得物理非相干轮廓。最后,所提出的MOFS被认为是解决多目标优化问题的一种有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Solution of Fractional Optimal Control Problems by using orthogonal collocation and Multi-objective Optimization Stochastic Fractal Search

Solution of Fractional Optimal Control Problems by using orthogonal collocation and Multi-objective Optimization Stochastic Fractal Search

In this contribution the solution of Fractional Optimal Control Problems (FOCP) by using the Orthogonal Collocation Method (OCM) and the Multi-objective Optimization Stochastic Fractal Search (MOSFS) algorithm is investigated. For this purpose, three classical case studies on engineering are considered. Initially, the concentration profiles of laccase enzyme production process are analyzed to evaluate the influence of fractional order. Then, two classical FOCP (Catalyst Mixing and Batch Reactor) are solved by using the association between OCM and MOSFS approachesthrough the formulation and solution of a multi-objective optimization problem. The results indicate that the variation of the fractional order impliesdifferent values for the original objective function. In addition, physicallyincoherent profiles can be obtained by considering the fluctuation of the fractional order. Finally, the proposed MOSFS is considered as apromising methodology to solve multi-objective optimization problems.

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