{"title":"基于代理的复杂工程问题优化","authors":"M. Kotti, M. Fakhfakh, E. Tlelo-Cuautle","doi":"10.1109/IRASET52964.2022.9737788","DOIUrl":null,"url":null,"abstract":"This paper proposes a surrogate modeling-based optimization approach for solving complex engineering optimization problems. The main challenge is to use the surrogate model for evaluating computationally expensive and constrained problems. Kriging and radial basis function models are considered for modeling both performances and constrains. Penalty technique is considered for dealing with constraints. To show the efficiency of the proposed algorithms, obtained results are compared with the conventional equation-based particle swarm optimization (PSO) algorithm results. Accuracy and robustness of the approaches are also demonstrated. Experimental results indicate that the proposed approaches are promising for solving complex constrained optimization problems.","PeriodicalId":377115,"journal":{"name":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Surrogate-Based Optimization for Complex Engineering problems\",\"authors\":\"M. Kotti, M. Fakhfakh, E. Tlelo-Cuautle\",\"doi\":\"10.1109/IRASET52964.2022.9737788\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a surrogate modeling-based optimization approach for solving complex engineering optimization problems. The main challenge is to use the surrogate model for evaluating computationally expensive and constrained problems. Kriging and radial basis function models are considered for modeling both performances and constrains. Penalty technique is considered for dealing with constraints. To show the efficiency of the proposed algorithms, obtained results are compared with the conventional equation-based particle swarm optimization (PSO) algorithm results. Accuracy and robustness of the approaches are also demonstrated. Experimental results indicate that the proposed approaches are promising for solving complex constrained optimization problems.\",\"PeriodicalId\":377115,\"journal\":{\"name\":\"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IRASET52964.2022.9737788\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRASET52964.2022.9737788","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Surrogate-Based Optimization for Complex Engineering problems
This paper proposes a surrogate modeling-based optimization approach for solving complex engineering optimization problems. The main challenge is to use the surrogate model for evaluating computationally expensive and constrained problems. Kriging and radial basis function models are considered for modeling both performances and constrains. Penalty technique is considered for dealing with constraints. To show the efficiency of the proposed algorithms, obtained results are compared with the conventional equation-based particle swarm optimization (PSO) algorithm results. Accuracy and robustness of the approaches are also demonstrated. Experimental results indicate that the proposed approaches are promising for solving complex constrained optimization problems.