{"title":"复杂空间系统耦合参数化设计的集成优化方法","authors":"Ming Wen, Cheng-shan Han","doi":"10.1109/ICSSEM.2012.6340732","DOIUrl":null,"url":null,"abstract":"This paper investigates the problem of optimization method for coupling parametrization design of complex space system. The objective is to develop an integrated optimization framework to design complex space system. An improved collaborative optimization (ICO) strategy for complex design using hybrid optimization algorithm is proposed, which associates genetic algorithm (GA) with tabu search (TS). A multi-objective function with penalty terms for the system-level optimization and a relax factor for the subsystem optimization are adopted, which can avoid unsolvability or converging difficulty. The results demonstrate the applicability and effectiveness of the proposed design methodology.","PeriodicalId":115037,"journal":{"name":"2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrated optimization method for coupling parametrization design of complex space system\",\"authors\":\"Ming Wen, Cheng-shan Han\",\"doi\":\"10.1109/ICSSEM.2012.6340732\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper investigates the problem of optimization method for coupling parametrization design of complex space system. The objective is to develop an integrated optimization framework to design complex space system. An improved collaborative optimization (ICO) strategy for complex design using hybrid optimization algorithm is proposed, which associates genetic algorithm (GA) with tabu search (TS). A multi-objective function with penalty terms for the system-level optimization and a relax factor for the subsystem optimization are adopted, which can avoid unsolvability or converging difficulty. The results demonstrate the applicability and effectiveness of the proposed design methodology.\",\"PeriodicalId\":115037,\"journal\":{\"name\":\"2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSSEM.2012.6340732\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSSEM.2012.6340732","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Integrated optimization method for coupling parametrization design of complex space system
This paper investigates the problem of optimization method for coupling parametrization design of complex space system. The objective is to develop an integrated optimization framework to design complex space system. An improved collaborative optimization (ICO) strategy for complex design using hybrid optimization algorithm is proposed, which associates genetic algorithm (GA) with tabu search (TS). A multi-objective function with penalty terms for the system-level optimization and a relax factor for the subsystem optimization are adopted, which can avoid unsolvability or converging difficulty. The results demonstrate the applicability and effectiveness of the proposed design methodology.