MECSBO: Multi-strategy enhanced circulatory system based optimisation algorithm for global optimisation and reliability-based design optimisation problems
{"title":"MECSBO: Multi-strategy enhanced circulatory system based optimisation algorithm for global optimisation and reliability-based design optimisation problems","authors":"Shiyuan Yang, Chenhao Guo, Debiao Meng, Yipeng Guo, Yongqiang Guo, Lidong Pan, Shun-Peng Zhu","doi":"10.1049/cim2.12097","DOIUrl":null,"url":null,"abstract":"<p>The Circulatory System Based Optimisation (CSBO) stands as a nascent metaheuristic optimisation algorithm known for its proficiency in tackling global optimisation problems. The authors introduce the Multi-strategy Enhanced CSBO (MECSBO), an algorithm designed for global optimisation and Reliability-based Design Optimisation (RBDO). MECSBO integrates adaptive inertia weight, golden sine operator and chaos strategy to augment the convergence capacity and efficiency of the original CSBO. Furthermore, MECSBO-based RBDO algorithm is presented to address RBDO problem. The comparative analysis utilising standard real-world benchmark functions has been carried out to validate the effectiveness of the proposed MECSBO. Several RBDO problems, including three typical numerical examples and three engineering cases, are used to show abilities of the proposed MECSBO-based RBDO algorithm. The results demonstrated that MECSBO is outperformed comparing to the state-of-the-art algorithms in terms of accuracy, efficiency, and robustness in RBDO problems.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"6 2","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12097","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Collaborative Intelligent Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cim2.12097","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
The Circulatory System Based Optimisation (CSBO) stands as a nascent metaheuristic optimisation algorithm known for its proficiency in tackling global optimisation problems. The authors introduce the Multi-strategy Enhanced CSBO (MECSBO), an algorithm designed for global optimisation and Reliability-based Design Optimisation (RBDO). MECSBO integrates adaptive inertia weight, golden sine operator and chaos strategy to augment the convergence capacity and efficiency of the original CSBO. Furthermore, MECSBO-based RBDO algorithm is presented to address RBDO problem. The comparative analysis utilising standard real-world benchmark functions has been carried out to validate the effectiveness of the proposed MECSBO. Several RBDO problems, including three typical numerical examples and three engineering cases, are used to show abilities of the proposed MECSBO-based RBDO algorithm. The results demonstrated that MECSBO is outperformed comparing to the state-of-the-art algorithms in terms of accuracy, efficiency, and robustness in RBDO problems.
期刊介绍:
IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly.
The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).