{"title":"遗传算法求解双准则网络拓扑设计问题","authors":"Jong Ryul Kim, M. Gen","doi":"10.1109/CEC.1999.785557","DOIUrl":null,"url":null,"abstract":"Increasing attention is being paid to various problems inherent in the topological design of network systems. The topological structure of these networks can be based on service centers, terminals (users), and connection cables. Lately, these network systems have been designed with fiber optic cable, due to increasing user requirements. But considering the high cost of the fiber optic cable, it is desirable that the network architecture is composed of a spanning tree. Network topology design problems consist of finding a topology that optimizes design criteria such as connection cost, message delay, network reliability, and so on. Recently, genetic algorithms (GAs) have advanced in many research fields, such as network optimization problems, combinatorial optimization, multi-objective optimization, and so on. Also, GAs have received a great deal of attention concerning their ability as an optimization technique for many real-world problems. In this paper, a GA for solving bicriteria network topology design problems of wide-band communication networks connected with fiber optic cable is presented, considering network reliability. We also employ the Prufer number and cluster string in order to represent chromosomes. Finally, we present some experiments in order to certify the quality of the network designs obtained by using the proposed GA. From the results, the proposed method can search effectively better candidate network architecture.","PeriodicalId":292523,"journal":{"name":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"46","resultStr":"{\"title\":\"Genetic algorithm for solving bicriteria network topology design problem\",\"authors\":\"Jong Ryul Kim, M. Gen\",\"doi\":\"10.1109/CEC.1999.785557\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Increasing attention is being paid to various problems inherent in the topological design of network systems. The topological structure of these networks can be based on service centers, terminals (users), and connection cables. Lately, these network systems have been designed with fiber optic cable, due to increasing user requirements. But considering the high cost of the fiber optic cable, it is desirable that the network architecture is composed of a spanning tree. Network topology design problems consist of finding a topology that optimizes design criteria such as connection cost, message delay, network reliability, and so on. Recently, genetic algorithms (GAs) have advanced in many research fields, such as network optimization problems, combinatorial optimization, multi-objective optimization, and so on. Also, GAs have received a great deal of attention concerning their ability as an optimization technique for many real-world problems. In this paper, a GA for solving bicriteria network topology design problems of wide-band communication networks connected with fiber optic cable is presented, considering network reliability. We also employ the Prufer number and cluster string in order to represent chromosomes. Finally, we present some experiments in order to certify the quality of the network designs obtained by using the proposed GA. From the results, the proposed method can search effectively better candidate network architecture.\",\"PeriodicalId\":292523,\"journal\":{\"name\":\"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"46\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.1999.785557\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.1999.785557","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genetic algorithm for solving bicriteria network topology design problem
Increasing attention is being paid to various problems inherent in the topological design of network systems. The topological structure of these networks can be based on service centers, terminals (users), and connection cables. Lately, these network systems have been designed with fiber optic cable, due to increasing user requirements. But considering the high cost of the fiber optic cable, it is desirable that the network architecture is composed of a spanning tree. Network topology design problems consist of finding a topology that optimizes design criteria such as connection cost, message delay, network reliability, and so on. Recently, genetic algorithms (GAs) have advanced in many research fields, such as network optimization problems, combinatorial optimization, multi-objective optimization, and so on. Also, GAs have received a great deal of attention concerning their ability as an optimization technique for many real-world problems. In this paper, a GA for solving bicriteria network topology design problems of wide-band communication networks connected with fiber optic cable is presented, considering network reliability. We also employ the Prufer number and cluster string in order to represent chromosomes. Finally, we present some experiments in order to certify the quality of the network designs obtained by using the proposed GA. From the results, the proposed method can search effectively better candidate network architecture.