Nature-Inspired Informatics for Telecommunication Network Design

Nature-Inspired Informatics for Telecommunication Network Design

Sergio Nesmachnow (Universidad de la República, Uruguay), Héctor Cancela (Universidad de la República, Uruguay) and Enrique Alba (Universidad de Málaga, Spain)
DOI: 10.4018/978-1-60566-986-1.ch023
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Abstract

The speedy pace of change in telecommunications and its ubiquitous presence have drastically altered the way people interact, impacting production, government, and social life. The infrastructure for providing telecommunication services must be continuously renewed, as innovative technologies emerge and drive changes by offering to bring new services to the end users. In this context, the problem of efficiently designing the underlying networks in order to satisfy different requirements while at the same time keeping the capital and operative expenditures bounded is of ever growing importance and actuality. Network design problems have many variations, depending on the characteristics of the technologies to be employed, as well as on the simplifying hypothesis that can be applied on each particular context, and on the planning horizon. Nevertheless, in most cases they are extremely complex problems, for which exact solutions cannot be found in practice. Nature-inspired optimization techniques (belonging to the metaheuristic computational methods) are important tools in these cases, as they are able to achieve good quality solutions in reasonable computational times. The objective of this chapter is to present a systematic review of nature-inspired techniques employed to solve optimization problems related to telecommunication network design. The review is aimed at providing an insight of different approaches in the area, in particular covering four main classes of applications: minimum spanning trees, reliable networks, local access network design and backbone location, and cellular and wireless network design. A large proportion of the papers deal with single objective models, but there is also a growing number of works that study multi-objective problems, which search for solutions that perform well in a number of different criteria. While genetic algorithms and other evolutionary algorithms appear most frequently, there is also significant research on other methods, such as ant colony optimization, particle swarm optimization, and other nature-inspired techniques.

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