{"title":"Hybrid Algorithm for Solving a Design Optimization Problems","authors":"L. Gladkov, N. V. Gladkova, D. Y. Gusev","doi":"10.1109/RusAutoCon49822.2020.9208061","DOIUrl":null,"url":null,"abstract":"This article describes an integrated approach to the placement and tracing of super large integrated circuits. The approach is based on a joint decision of such integrated circuits using fuzzy genetic methods. The authors describe the problem under consideration and show a brief analysis of existing approaches to its solution. The article describes the following main points: the structure of the proposed algorithm and its main stages; developed genetic operators crossover; proposed model of the formation of the population of solutions; developed heuristics, operators and search strategies for optimal solutions. The article shows the results of computational experiments. These experiments confirm the effectiveness of the proposed method. The authors comment on the brief analysis of the results.","PeriodicalId":101834,"journal":{"name":"2020 International Russian Automation Conference (RusAutoCon)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Russian Automation Conference (RusAutoCon)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RusAutoCon49822.2020.9208061","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This article describes an integrated approach to the placement and tracing of super large integrated circuits. The approach is based on a joint decision of such integrated circuits using fuzzy genetic methods. The authors describe the problem under consideration and show a brief analysis of existing approaches to its solution. The article describes the following main points: the structure of the proposed algorithm and its main stages; developed genetic operators crossover; proposed model of the formation of the population of solutions; developed heuristics, operators and search strategies for optimal solutions. The article shows the results of computational experiments. These experiments confirm the effectiveness of the proposed method. The authors comment on the brief analysis of the results.