基于蚁群算法的运输网络数据集自动图化

M. Ware, Nigel Richards
{"title":"基于蚁群算法的运输网络数据集自动图化","authors":"M. Ware, Nigel Richards","doi":"10.1109/CEC.2013.6557790","DOIUrl":null,"url":null,"abstract":"The work presented here investigates the usefulness of Ant Colony Optimisation to solving network schematization problems. This is a well-established problem domain and a number of solutions have appeared in the literature previously. In this paper an Ant Colony System (ACS) based algorithm is presented, together with experimental results and performance analysis. The aim is to provide an algorithm that produces better results and is more efficient (in terms of execution times) than previous solutions. Throughout the paper, ACS is tested and evaluated empirically - that is, experiments are performed and observed, these observations are recorded and subsequently analysed. In order to perform the experiments, a software implementation of the algorithm is constructed and then applied to test data sets. No attempt has been made here to perform a theoretical analysis of ACS. The results presented demonstrate that ACS can be used as an effective means of providing solutions to network schematization problems. In particular, ACS is shown to outperform a previous Simulated Annealing based solution.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"An Ant Colony System algorithm for automatically schematizing transport network data sets\",\"authors\":\"M. Ware, Nigel Richards\",\"doi\":\"10.1109/CEC.2013.6557790\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The work presented here investigates the usefulness of Ant Colony Optimisation to solving network schematization problems. This is a well-established problem domain and a number of solutions have appeared in the literature previously. In this paper an Ant Colony System (ACS) based algorithm is presented, together with experimental results and performance analysis. The aim is to provide an algorithm that produces better results and is more efficient (in terms of execution times) than previous solutions. Throughout the paper, ACS is tested and evaluated empirically - that is, experiments are performed and observed, these observations are recorded and subsequently analysed. In order to perform the experiments, a software implementation of the algorithm is constructed and then applied to test data sets. No attempt has been made here to perform a theoretical analysis of ACS. The results presented demonstrate that ACS can be used as an effective means of providing solutions to network schematization problems. In particular, ACS is shown to outperform a previous Simulated Annealing based solution.\",\"PeriodicalId\":211988,\"journal\":{\"name\":\"2013 IEEE Congress on Evolutionary Computation\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE Congress on Evolutionary Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2013.6557790\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Congress on Evolutionary Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2013.6557790","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

这里提出的工作调查了蚁群优化解决网络图式问题的有用性。这是一个成熟的问题领域,以前的文献中已经出现了许多解决方案。本文提出了一种基于蚁群系统(ACS)的算法,并给出了实验结果和性能分析。其目的是提供一种比以前的解决方案产生更好结果和更有效(就执行时间而言)的算法。在整篇论文中,对ACS进行了经验检验和评估——也就是说,进行了实验和观察,记录了这些观察结果并随后进行了分析。为了进行实验,构建了该算法的软件实现,并将其应用于测试数据集。这里没有尝试对ACS进行理论分析。结果表明,ACS可以作为解决网络原理图化问题的有效手段。特别是,ACS被证明优于以前的基于模拟退火的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Ant Colony System algorithm for automatically schematizing transport network data sets
The work presented here investigates the usefulness of Ant Colony Optimisation to solving network schematization problems. This is a well-established problem domain and a number of solutions have appeared in the literature previously. In this paper an Ant Colony System (ACS) based algorithm is presented, together with experimental results and performance analysis. The aim is to provide an algorithm that produces better results and is more efficient (in terms of execution times) than previous solutions. Throughout the paper, ACS is tested and evaluated empirically - that is, experiments are performed and observed, these observations are recorded and subsequently analysed. In order to perform the experiments, a software implementation of the algorithm is constructed and then applied to test data sets. No attempt has been made here to perform a theoretical analysis of ACS. The results presented demonstrate that ACS can be used as an effective means of providing solutions to network schematization problems. In particular, ACS is shown to outperform a previous Simulated Annealing based solution.
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:481959085
Book学术官方微信