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A novel grouping harmony search algorithm for the multiple-type access node location problem

机译:一种新型的多类型接入节点定位问题的分组和声搜索算法

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In this paper we present a novel grouping harmony search algorithm for the Access Node Location Problem (ANLP) with different types of concentrators. The ANLP is a NP-hard problem where a set of distributed terminals, with distinct rate demands, must be assigned to a variable number of concentrators subject to capacity constraints. We consider the possibility of choosing between different concentrator models is given in order to provide service demand at different cost. The ANLP is relevant in communication networks design, and has been considered before within the design of MPLS networks, for example. The approach we propose to tackle the ANLP problem consists of a hybrid Grouping Harmony Search (GHS) algorithm with a local search method and a technique for repairing unfeasible solutions. Moreover, the presented scheme also includes the adaptation of the GHS to a differential scheme, where each proposed harmony is obtained from the same harmony in the previous iteration. This differential scheme is perfectly adapted to the specifications of the ANLP problem, as it utilizes the grouping concept based on the proximity between nodes, instead of being only based on the grouping concept. This allows for a higher efficiency on the searching process of the algorithm. Extensive Monte Carlo simulations in synthetic instances show that this proposal provides faster convergence rate, less computational complexity and better statistical performance than alternative algorithms for the ANLP, such as grouping genetic algorithms, specially when the size of the scenario increases. We also include practical results for the application of GHS to a real wireless network deployment problem in Bizkaia, northern Spain.
机译:在本文中,我们针对具有不同类型集中器的访问节点位置问题(ANLP),提出了一种新颖的分组和声搜索算法。 ANLP是一个NP难题,必须将一组具有不同速率需求的分布式终端分配给受容量限制的可变数量的集中器。我们考虑了在不同集中器模型之间进行选择的可能性,以便以不同的成本提供服务需求。 ANLP与通信网络设计相关,例如,在MPLS网络设计之前就已经考虑过。我们提出的解决ANLP问题的方法包括混合编组和声搜索(GHS)算法和本地搜索方法以及修复不可行解决方案的技术。此外,所提出的方案还包括使GHS适应于差分方案,其中每个提议的和声均从先前迭代中的相同和声获得。这种差分方案非常适合ANLP问题的规范,因为它利用了基于节点之间邻近度的分组概念,而不是仅基于分组概念。这使得算法的搜索过程具有更高的效率。综合实例中的广泛蒙特卡洛仿真表明,与ANLP的替代算法(例如,分组遗传算法)相比,该方案提供了更快的收敛速度,更低的计算复杂度和更好的统计性能,尤其是当场景大小增加时。我们还提供了将GHS应用于西班牙北部Bizkaia的实际无线网络部署问题的实际结果。

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