VLSI平面规划的协同进化多目标粒子群算法

Zhen Chen, Jinzhu Chen, Wenzhong Guo, Guolong Chen
{"title":"VLSI平面规划的协同进化多目标粒子群算法","authors":"Zhen Chen, Jinzhu Chen, Wenzhong Guo, Guolong Chen","doi":"10.1109/ICNC.2012.6234515","DOIUrl":null,"url":null,"abstract":"Floorplanning is a key step in the physical design of Very Large Scale Integrated (VLSI) circuits. It is a multi-objective combinatorial optimization and has been proved to be a NP-hard problem. To solve this problem, a coevolutionary multi-objective particle swarm optimization (CMOPSO) algorithm is proposed in this paper. The algorithm imports the concept of coevolutionary algorithm and elitist strategy into basic PSO algorithm, takes both the layout area and total interconnection wire length into consideration simultaneously. Experimental results showed the new algorithm can achieve a better performance.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A coevolutionary multi-objective PSO algorithm for VLSI floorplanning\",\"authors\":\"Zhen Chen, Jinzhu Chen, Wenzhong Guo, Guolong Chen\",\"doi\":\"10.1109/ICNC.2012.6234515\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Floorplanning is a key step in the physical design of Very Large Scale Integrated (VLSI) circuits. It is a multi-objective combinatorial optimization and has been proved to be a NP-hard problem. To solve this problem, a coevolutionary multi-objective particle swarm optimization (CMOPSO) algorithm is proposed in this paper. The algorithm imports the concept of coevolutionary algorithm and elitist strategy into basic PSO algorithm, takes both the layout area and total interconnection wire length into consideration simultaneously. Experimental results showed the new algorithm can achieve a better performance.\",\"PeriodicalId\":404981,\"journal\":{\"name\":\"2012 8th International Conference on Natural Computation\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 8th International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2012.6234515\",\"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 8th International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2012.6234515","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

平面规划是超大规模集成电路(VLSI)物理设计的关键步骤。它是一个多目标组合优化问题,已被证明是一个np困难问题。为了解决这一问题,本文提出了一种协同进化多目标粒子群优化算法。该算法将协同进化算法和精英策略的概念引入到基本粒子群算法中,同时考虑布线面积和总布线长度。实验结果表明,新算法能取得较好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A coevolutionary multi-objective PSO algorithm for VLSI floorplanning
Floorplanning is a key step in the physical design of Very Large Scale Integrated (VLSI) circuits. It is a multi-objective combinatorial optimization and has been proved to be a NP-hard problem. To solve this problem, a coevolutionary multi-objective particle swarm optimization (CMOPSO) algorithm is proposed in this paper. The algorithm imports the concept of coevolutionary algorithm and elitist strategy into basic PSO algorithm, takes both the layout area and total interconnection wire length into consideration simultaneously. Experimental results showed the new algorithm can achieve a better performance.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信