An adaptive large neighbourhood search for designing transparent optical core network

D. Mehta, B. O’Sullivan, Cemalettin Ozturk, L. Quesada
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引用次数: 1

Abstract

In this paper, we focus on designing a core optical network. Given a set of Metro-Core (MC) nodes, the problem is to select a set of links such that the maximum distance between any pair of nodes is bounded. The primary objective is to minimise the total length of the links followed by a secondary objective which is to minimise the total length of the paths between the pairs of MC nodes. We present a mixed integer programming (MIP) model for this problem. As the size of the instances of this problem increases, the MIP model does not scale in terms of memory and time. We therefore present an adaptive large neighbourhood search (LNS) algorithm where the idea is to find an initial solution and repeatedly improve it by solving relatively small subproblems. We propose two different ways of selecting subproblems. The experiments are performed using 3 real-world network topologies: Ireland, UK and Italy. Our results demonstrate that the developed approach is suitable for solving large networks and it can compute very high quality solutions which are very close to optimal values.
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透明光核心网设计中的自适应大邻域搜索
在本文中,我们重点设计了一个核心光网络。给定一组Metro-Core (MC)节点,问题是选择一组链路,使任意一对节点之间的最大距离有界。主要目标是最小化链路的总长度,其次是最小化MC节点对之间路径的总长度。针对这一问题,提出了一个混合整数规划(MIP)模型。随着该问题实例的大小增加,MIP模型在内存和时间方面无法扩展。因此,我们提出了一种自适应大邻域搜索(LNS)算法,其思想是找到一个初始解,并通过解决相对较小的子问题来反复改进它。我们提出了两种不同的选择子问题的方法。实验使用三个真实的网络拓扑进行:爱尔兰,英国和意大利。我们的结果表明,所开发的方法适用于求解大型网络,并且可以计算非常接近最优值的非常高质量的解。
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