{"title":"基于自适应海洋捕食者算法的发动机罩轻量化优化设计方法","authors":"Chenglin Zhang, Zhicheng He, Qiqi Li, Yong Chen, Shaowei Chen, X. Nie","doi":"10.1093/jcde/qwad047","DOIUrl":null,"url":null,"abstract":"The lightweight design of the hood is crucial for the structural optimization of an entire vehicle. However, traditional high-fidelity-based lightweight methods are time-consuming due to the complex structures of the hood, and the lightweight results heavily rely on engineering experiences. To this end, an improved adaptive marine predator algorithm (AMPA) is proposed to solve this problem. Compared to the original marine predator algorithm (MPA), the proposed AMPA adapts to optimization problems through three enhancements, including chaotic theory-based initialization, a mixed search strategy, and dynamic partitioning of iteration phases. Experimental comparisons of AMPA, MPA, and eight state-of-the-art algorithms are conducted on IEEE CEC2017 benchmark functions. AMPA outperforms the others in both 30- and 50-dimensional experiments. Friedman and Wilcoxon’s sign-rank tests further confirm AMPA’s superiority and statistical significance. An implicit parametric model of the hood is generated, and the critical design variables are determined through global sensitivity analysis to realize hood lightweight. The stacking method is employed to construct a surrogate meta-model of the hood to accelerate the optimization efficiency of the vehicle hood. Utilizing the meta-model and the proposed AMPA, the hood mass is reduced by 7.43% while all six static and dynamic stiffness metrics are enhanced. The effectiveness of the proposed optimization method is validated through finite element analysis.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An adaptive marine predator algorithm based optimization method for hood lightweight design\",\"authors\":\"Chenglin Zhang, Zhicheng He, Qiqi Li, Yong Chen, Shaowei Chen, X. Nie\",\"doi\":\"10.1093/jcde/qwad047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The lightweight design of the hood is crucial for the structural optimization of an entire vehicle. However, traditional high-fidelity-based lightweight methods are time-consuming due to the complex structures of the hood, and the lightweight results heavily rely on engineering experiences. To this end, an improved adaptive marine predator algorithm (AMPA) is proposed to solve this problem. Compared to the original marine predator algorithm (MPA), the proposed AMPA adapts to optimization problems through three enhancements, including chaotic theory-based initialization, a mixed search strategy, and dynamic partitioning of iteration phases. Experimental comparisons of AMPA, MPA, and eight state-of-the-art algorithms are conducted on IEEE CEC2017 benchmark functions. AMPA outperforms the others in both 30- and 50-dimensional experiments. Friedman and Wilcoxon’s sign-rank tests further confirm AMPA’s superiority and statistical significance. An implicit parametric model of the hood is generated, and the critical design variables are determined through global sensitivity analysis to realize hood lightweight. The stacking method is employed to construct a surrogate meta-model of the hood to accelerate the optimization efficiency of the vehicle hood. Utilizing the meta-model and the proposed AMPA, the hood mass is reduced by 7.43% while all six static and dynamic stiffness metrics are enhanced. The effectiveness of the proposed optimization method is validated through finite element analysis.\",\"PeriodicalId\":48611,\"journal\":{\"name\":\"Journal of Computational Design and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2023-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Design and Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1093/jcde/qwad047\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Design and Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1093/jcde/qwad047","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
An adaptive marine predator algorithm based optimization method for hood lightweight design
The lightweight design of the hood is crucial for the structural optimization of an entire vehicle. However, traditional high-fidelity-based lightweight methods are time-consuming due to the complex structures of the hood, and the lightweight results heavily rely on engineering experiences. To this end, an improved adaptive marine predator algorithm (AMPA) is proposed to solve this problem. Compared to the original marine predator algorithm (MPA), the proposed AMPA adapts to optimization problems through three enhancements, including chaotic theory-based initialization, a mixed search strategy, and dynamic partitioning of iteration phases. Experimental comparisons of AMPA, MPA, and eight state-of-the-art algorithms are conducted on IEEE CEC2017 benchmark functions. AMPA outperforms the others in both 30- and 50-dimensional experiments. Friedman and Wilcoxon’s sign-rank tests further confirm AMPA’s superiority and statistical significance. An implicit parametric model of the hood is generated, and the critical design variables are determined through global sensitivity analysis to realize hood lightweight. The stacking method is employed to construct a surrogate meta-model of the hood to accelerate the optimization efficiency of the vehicle hood. Utilizing the meta-model and the proposed AMPA, the hood mass is reduced by 7.43% while all six static and dynamic stiffness metrics are enhanced. The effectiveness of the proposed optimization method is validated through finite element analysis.
期刊介绍:
Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering:
• Theory and its progress in computational advancement for design and engineering
• Development of computational framework to support large scale design and engineering
• Interaction issues among human, designed artifacts, and systems
• Knowledge-intensive technologies for intelligent and sustainable systems
• Emerging technology and convergence of technology fields presented with convincing design examples
• Educational issues for academia, practitioners, and future generation
• Proposal on new research directions as well as survey and retrospectives on mature field.