{"title":"Intelligent Strategies to Combine Move Heuristics in Selection Hyper-heuristics for Real-World Fibre Network Design Optimisation","authors":"Anil Arpaci, Jun Chen, J. Drake, Tim Glover","doi":"10.1109/SSCI50451.2021.9659994","DOIUrl":null,"url":null,"abstract":"Increasing competition in today's telecommunication industry drives the need for more cost effective services. In order to reduce the cost of designing a fibre network with low capital expenditure, automation and optimisation of network design has become crucial. British Telecom's network design software, BT NetDesign, has been developed for the purpose of network design and optimisation using a rich set of network/graph-based heuristics and the simulated annealing (SA) search method. Although NetDesign provides several different ways of navigating the search space via different move heuristics, the existing search method (SA) does not consistently reach the near-global optimum as the size of network increases. To deal with larger networks, this study utilises an intelligent approach based on the well-known Luby sequence to combine move heuristics, using two separate learning schemes: frequency based and bigram statistics. These two strategies are rigorously evaluated on network instances of different sizes. Experimental results on real-world case studies indicate that a bigram scheme with a longer warm-up period to learn heuristic combinations can reach high quality solutions for large networks.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSCI50451.2021.9659994","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Increasing competition in today's telecommunication industry drives the need for more cost effective services. In order to reduce the cost of designing a fibre network with low capital expenditure, automation and optimisation of network design has become crucial. British Telecom's network design software, BT NetDesign, has been developed for the purpose of network design and optimisation using a rich set of network/graph-based heuristics and the simulated annealing (SA) search method. Although NetDesign provides several different ways of navigating the search space via different move heuristics, the existing search method (SA) does not consistently reach the near-global optimum as the size of network increases. To deal with larger networks, this study utilises an intelligent approach based on the well-known Luby sequence to combine move heuristics, using two separate learning schemes: frequency based and bigram statistics. These two strategies are rigorously evaluated on network instances of different sizes. Experimental results on real-world case studies indicate that a bigram scheme with a longer warm-up period to learn heuristic combinations can reach high quality solutions for large networks.