{"title":"具有节点连接约束的网络设计","authors":"H. Cancela, F. Robledo, G. Rubino","doi":"10.1145/1035662.1035664","DOIUrl":null,"url":null,"abstract":"The Generalized Steiner Problem with Node-Connectivity constraints (GSPNC) consists of determining of a minimum cost subnetwork of a given network where some pairs of nodes are required to satisfy node-connectivity requirements. The GSPNC has applications to the design of low-cost communications networks which can survive failures in the servers as well as in the connection lines. The GSPNC is known to be NP-Complete. In this paper, we introduce an algorithm based on GRASP (Greedy Randomized Adaptive Search Procedure), an effective combinatorial optimization metaheuristic. Experimental results are obtained over a set of problem instances with different characteristics and connectivity requirements, obtaining in all these cases optimal or near-optimal results.","PeriodicalId":415618,"journal":{"name":"International Latin American Networking Conference","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Network design with node connectivity constraints\",\"authors\":\"H. Cancela, F. Robledo, G. Rubino\",\"doi\":\"10.1145/1035662.1035664\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Generalized Steiner Problem with Node-Connectivity constraints (GSPNC) consists of determining of a minimum cost subnetwork of a given network where some pairs of nodes are required to satisfy node-connectivity requirements. The GSPNC has applications to the design of low-cost communications networks which can survive failures in the servers as well as in the connection lines. The GSPNC is known to be NP-Complete. In this paper, we introduce an algorithm based on GRASP (Greedy Randomized Adaptive Search Procedure), an effective combinatorial optimization metaheuristic. Experimental results are obtained over a set of problem instances with different characteristics and connectivity requirements, obtaining in all these cases optimal or near-optimal results.\",\"PeriodicalId\":415618,\"journal\":{\"name\":\"International Latin American Networking Conference\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Latin American Networking Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1035662.1035664\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Latin American Networking Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1035662.1035664","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
摘要
具有节点连通性约束的广义斯坦纳问题(GSPNC)是给定网络的最小代价子网络的确定问题，其中需要若干对节点来满足节点连通性要求。GSPNC在低成本通信网络的设计中具有应用价值，该网络可以在服务器和连接线故障中存活下来。已知GSPNC是np完全的。本文介绍了一种有效的组合优化元启发式算法——贪心随机自适应搜索程序(GRASP, random Adaptive Search Procedure)。在一组具有不同特征和连通性要求的问题实例上获得实验结果，在所有这些情况下都获得了最优或接近最优的结果。
The Generalized Steiner Problem with Node-Connectivity constraints (GSPNC) consists of determining of a minimum cost subnetwork of a given network where some pairs of nodes are required to satisfy node-connectivity requirements. The GSPNC has applications to the design of low-cost communications networks which can survive failures in the servers as well as in the connection lines. The GSPNC is known to be NP-Complete. In this paper, we introduce an algorithm based on GRASP (Greedy Randomized Adaptive Search Procedure), an effective combinatorial optimization metaheuristic. Experimental results are obtained over a set of problem instances with different characteristics and connectivity requirements, obtaining in all these cases optimal or near-optimal results.
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