Intelligent Strategies to Combine Move Heuristics in Selection Hyper-heuristics for Real-World Fibre Network Design Optimisation

Anil Arpaci, Jun Chen, J. Drake, Tim Glover
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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.
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结合移动启发式选择的智能策略-现实世界光纤网络设计优化的超启发式
当今电信行业日益激烈的竞争促使人们需要更具成本效益的服务。为了降低光纤网络的设计成本,降低资本支出,网络设计的自动化和优化变得至关重要。英国电信的网络设计软件BT NetDesign是为网络设计和优化而开发的,使用了一套丰富的基于网络/图的启发式和模拟退火(SA)搜索方法。尽管NetDesign通过不同的移动启发式提供了几种不同的导航搜索空间的方法,但随着网络规模的增加,现有的搜索方法(SA)并不能始终达到近全局最优。为了处理更大的网络,本研究利用了一种基于著名的Luby序列的智能方法来结合移动启发式,使用两种独立的学习方案:基于频率和双图统计。这两种策略在不同规模的网络实例上进行了严格的评估。实际案例研究的实验结果表明,具有较长预热期的双图方案可以获得大型网络的高质量解。
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