Application of state-of-the-art multiobjective metaheuristic algorithms in reliability-based design optimization: a comparative study
Multiobjective reliability-based design optimization (RBDO) is a research area, which has not been investigated in the literatures comparing with single-objective RBDO. This work conducts an exhaustive study of fifteen new and popular metaheuristic multiobjective RBDO algorithms, including non-domin...
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| Published in: | Structural and multidisciplinary optimization Vol. 66; no. 8; p. 191 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.08.2023
Springer Nature B.V |
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| ISSN: | 1615-147X, 1615-1488 |
| Online Access: | Get full text |
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| Abstract | Multiobjective reliability-based design optimization (RBDO) is a research area, which has not been investigated in the literatures comparing with single-objective RBDO. This work conducts an exhaustive study of fifteen new and popular metaheuristic multiobjective RBDO algorithms, including non-dominated sorting genetic algorithm II, differential evolution for multiobjective optimization, multiobjective evolutionary algorithm based on decomposition, multiobjective particle swarm optimization, multiobjective flower pollination algorithm, multiobjective bat algorithm, multiobjective gray wolf optimizer, multiobjective multiverse optimization, multiobjective water cycle optimizer, success history-based adaptive multiobjective differential evolution, success history-based adaptive multiobjective differential evolution with whale optimization, multiobjective salp swarm algorithm, real-code population-based incremental learning and differential evolution, unrestricted population size evolutionary multiobjective optimization algorithm, and multiobjective jellyfish search optimizer. In addition, the adaptive chaos control method is employed for the above-mentioned algorithms to estimate the probabilistic constraints effectively. This comparative analysis reveals the critical technologies and enormous challenges in the RBDO field. It also offers new insight into simultaneously dealing with the multiple conflicting design objectives and probabilistic constraints. Also, this study presents the advantage and future development trends or incurs the increased challenge of researchers to put forward an effective multiobjective RBDO algorithm that assists the complex engineering system design. |
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| AbstractList | Multiobjective reliability-based design optimization (RBDO) is a research area, which has not been investigated in the literatures comparing with single-objective RBDO. This work conducts an exhaustive study of fifteen new and popular metaheuristic multiobjective RBDO algorithms, including non-dominated sorting genetic algorithm II, differential evolution for multiobjective optimization, multiobjective evolutionary algorithm based on decomposition, multiobjective particle swarm optimization, multiobjective flower pollination algorithm, multiobjective bat algorithm, multiobjective gray wolf optimizer, multiobjective multiverse optimization, multiobjective water cycle optimizer, success history-based adaptive multiobjective differential evolution, success history-based adaptive multiobjective differential evolution with whale optimization, multiobjective salp swarm algorithm, real-code population-based incremental learning and differential evolution, unrestricted population size evolutionary multiobjective optimization algorithm, and multiobjective jellyfish search optimizer. In addition, the adaptive chaos control method is employed for the above-mentioned algorithms to estimate the probabilistic constraints effectively. This comparative analysis reveals the critical technologies and enormous challenges in the RBDO field. It also offers new insight into simultaneously dealing with the multiple conflicting design objectives and probabilistic constraints. Also, this study presents the advantage and future development trends or incurs the increased challenge of researchers to put forward an effective multiobjective RBDO algorithm that assists the complex engineering system design. |
| ArticleNumber | 191 |
| Author | Yildiz, Ali Riza Yıldız, Betül Sultan Zhong, Changting Meng, Zeng Li, Gang Mirjalili, Seyedali |
| Author_xml | – sequence: 1 givenname: Zeng orcidid: 0000-0002-5648-5866 surname: Meng fullname: Meng, Zeng email: mengz@hfut.edu.cn organization: School of Civil Engineering, Hefei University of Technology – sequence: 2 givenname: Betül Sultan surname: Yıldız fullname: Yıldız, Betül Sultan organization: Department of Mechanical Engineering, Uludağ University – sequence: 3 givenname: Gang surname: Li fullname: Li, Gang organization: State Key Laboratory of Structural Analyses for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology – sequence: 4 givenname: Changting surname: Zhong fullname: Zhong, Changting organization: School of Civil Engineering and Architecture, Hainan University – sequence: 5 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali organization: Torrens University – sequence: 6 givenname: Ali Riza surname: Yildiz fullname: Yildiz, Ali Riza organization: Department of Mechanical Engineering, Uludağ University |
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| ParticipantIDs | proquest_journals_2848581889 crossref_citationtrail_10_1007_s00158_023_03639_0 crossref_primary_10_1007_s00158_023_03639_0 springer_journals_10_1007_s00158_023_03639_0 |
| PublicationCentury | 2000 |
| PublicationDate | 20230800 2023-08-00 20230801 |
| PublicationDateYYYYMMDD | 2023-08-01 |
| PublicationDate_xml | – month: 8 year: 2023 text: 20230800 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Heidelberg |
| PublicationTitle | Structural and multidisciplinary optimization |
| PublicationTitleAbbrev | Struct Multidisc Optim |
| PublicationYear | 2023 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
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| SubjectTerms | Adaptive control Comparative studies Computational Mathematics and Numerical Analysis Control methods Design optimization Engineering Engineering Design Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic methods Machine learning Multiple objective analysis Particle swarm optimization Reliability Research Paper Sorting algorithms Systems design Theoretical and Applied Mechanics |
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