Population-Based Incremental Learning to Solve the FAP Problem

J. M. Chaves-González, M. A. Vega-Rodríguez, D. Domínguez-González, J. Gómez-Pulido, J. M. Sánchez-Pérez
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引用次数: 13

Abstract

Frequency assignment problem (FAP) is a very important issue in the field of telecommunications (especially in GSM-Global System for Mobile-Networks). In this work, we present the Population-Based Incremental Learning (PBIL) algorithm to solve a particular branch of the FAP problem (MS-FAP). MS-FAP (Minimum Span Frequency Assignment Problem) tries to minimize the range of frequencies which is necessary in a certain area to cover the communications which take place there. In this paper it is presented the problem and it is explained the methodology which solve it. We have performed tests with a complete set of experiments using seven well known variations of PBIL and 7 types of MS-FAP problems. At the end, the results are presented and we compare them to conclude which variation of PBIL provides the best solution to the MS-FAP problem.
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基于群体的增量学习解决FAP问题
频率分配问题(FAP)是通信领域(特别是gsm全球移动网络系统)中一个非常重要的问题。在这项工作中,我们提出了基于群体的增量学习(PBIL)算法来解决FAP问题的一个特定分支(MS-FAP)。MS-FAP(最小跨距频率分配问题)试图将某一区域所需的频率范围最小化,以覆盖在该区域发生的通信。本文提出了这一问题,并阐述了解决这一问题的方法。我们使用七种已知的PBIL变体和七种MS-FAP问题进行了一套完整的实验。最后,我们给出了结果,并对它们进行了比较,以得出哪种PBIL变异能最好地解决MS-FAP问题。
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