{"title":"功能梯度材料柱的多目标优化","authors":"M. Kasem, K. Maalawi","doi":"10.1109/NILES53778.2021.9600498","DOIUrl":null,"url":null,"abstract":"We developed a hybrid model for multiobjective optimization of composite structures. It is applied to find the optimal designs of slender, thin-walled, and functionally graded material (FGM) columns. The overall objective function is defined as the weighting sum of the dimensionless column mass $\\widehat{M}_{s}$ and critical buckling load $\\bar{P}_{cr}$, expressed as $f(\\bar{x})=\\alpha\\widehat{M}_{s}(\\bar{x})-(1-\\alpha)\\bar{P}_{cr}(\\bar{x})$. Three global optimization algorithms i.e., the genetic algorithm (GA), sequential quadratic programming (SQP), and hybrid GA-SQP were employed to investigate the column best design point. Several optimization models are developed and the optimal designs are obtained.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Multiobjective Optimization of Functionally Graded Material Columns\",\"authors\":\"M. Kasem, K. Maalawi\",\"doi\":\"10.1109/NILES53778.2021.9600498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We developed a hybrid model for multiobjective optimization of composite structures. It is applied to find the optimal designs of slender, thin-walled, and functionally graded material (FGM) columns. The overall objective function is defined as the weighting sum of the dimensionless column mass $\\\\widehat{M}_{s}$ and critical buckling load $\\\\bar{P}_{cr}$, expressed as $f(\\\\bar{x})=\\\\alpha\\\\widehat{M}_{s}(\\\\bar{x})-(1-\\\\alpha)\\\\bar{P}_{cr}(\\\\bar{x})$. Three global optimization algorithms i.e., the genetic algorithm (GA), sequential quadratic programming (SQP), and hybrid GA-SQP were employed to investigate the column best design point. Several optimization models are developed and the optimal designs are obtained.\",\"PeriodicalId\":249153,\"journal\":{\"name\":\"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NILES53778.2021.9600498\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NILES53778.2021.9600498","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multiobjective Optimization of Functionally Graded Material Columns
We developed a hybrid model for multiobjective optimization of composite structures. It is applied to find the optimal designs of slender, thin-walled, and functionally graded material (FGM) columns. The overall objective function is defined as the weighting sum of the dimensionless column mass $\widehat{M}_{s}$ and critical buckling load $\bar{P}_{cr}$, expressed as $f(\bar{x})=\alpha\widehat{M}_{s}(\bar{x})-(1-\alpha)\bar{P}_{cr}(\bar{x})$. Three global optimization algorithms i.e., the genetic algorithm (GA), sequential quadratic programming (SQP), and hybrid GA-SQP were employed to investigate the column best design point. Several optimization models are developed and the optimal designs are obtained.
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