{"title":"使用自适应搜索向量的模拟退火","authors":"M. Miki, S. Hiwa, T. Hiroyasu","doi":"10.1109/ICCIS.2006.252256","DOIUrl":null,"url":null,"abstract":"It is reported that simulated annealing (SA), which changes one design variable at a time, is effective when applied to high-dimensional continuous optimization problems. However, if a correlation exists among the design variables, it is not efficient to search each dimension. In this paper, we propose SA with a mechanism to determine an appropriate search direction according to the landscape of the given problems. Its effectiveness is verified for high-dimensional problems with correlation among design variables","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Simulated Annealing using an Adaptive Search Vector\",\"authors\":\"M. Miki, S. Hiwa, T. Hiroyasu\",\"doi\":\"10.1109/ICCIS.2006.252256\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is reported that simulated annealing (SA), which changes one design variable at a time, is effective when applied to high-dimensional continuous optimization problems. However, if a correlation exists among the design variables, it is not efficient to search each dimension. In this paper, we propose SA with a mechanism to determine an appropriate search direction according to the landscape of the given problems. Its effectiveness is verified for high-dimensional problems with correlation among design variables\",\"PeriodicalId\":296028,\"journal\":{\"name\":\"2006 IEEE Conference on Cybernetics and Intelligent Systems\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE Conference on Cybernetics and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCIS.2006.252256\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Conference on Cybernetics and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIS.2006.252256","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Simulated Annealing using an Adaptive Search Vector
It is reported that simulated annealing (SA), which changes one design variable at a time, is effective when applied to high-dimensional continuous optimization problems. However, if a correlation exists among the design variables, it is not efficient to search each dimension. In this paper, we propose SA with a mechanism to determine an appropriate search direction according to the landscape of the given problems. Its effectiveness is verified for high-dimensional problems with correlation among design variables
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