Genetic algorithms for telecommunication network optimization

Celli, Gianni; Costamagna, E.; Fanni, Alessandra

doi:10.1109/icsmc.1995.537939

Cited by 11 publications

(11 citation statements)

References 2 publications

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“…Results of extensive testing over networks exhibiting dierent sizes and dierent topologies of the cable conduit graph are reported, and have been compared with results obtained by means of the heuristic algorithm HAD suitable for the problem at hand [7], a GA [3] and a SA algorithm [8]. Since the best results had been obtained using a SA algorithm, we compared the behaviour of the methods using as unit the results obtained with this algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…Implementing only these features, we were able to ®nd again the best results obtained by means of other optimisation techniques [3,7,8] for sample networks. So, we focused on the optimisation of the parameters involved in the search, instead of implementing other intensi®cation or diversi®ca-tion strategies.…”

Section: Ts Algorithmmentioning

confidence: 87%

“…In particular, genetic algorithms (GAs) [3] and simulated annealing algorithms (SA) [8] have been implemented.…”

Section: European Journal Of Operational Research 106 (1998) 357±372mentioning

confidence: 99%

“…We derived the proposed algorithm after an extensive testing involving some tens of networks of dierent sizes (many information are reported in the next section). The implementation of the described algorithm has been proven to be very fast if compared with other methods used to solve this problem [3,7,8], so that it has been possible to test the performances of the algorithm with dierent values of the parameters in a reasonable amount of time. The algorithm has been run allowing 10 000 iterations for each trial.…”

Section: The Choice Of Parametersmentioning

confidence: 99%

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A Tabu Search algorithm for the optimisation of telecommunication networks

Costamagna

Fanni

Giacinto

1998

European Journal of Operational Research

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Section: Resultsmentioning

confidence: 99%

Section: Ts Algorithmmentioning

confidence: 87%

“…In particular, genetic algorithms (GAs) [3] and simulated annealing algorithms (SA) [8] have been implemented.…”

Section: European Journal Of Operational Research 106 (1998) 357±372mentioning

confidence: 99%

Section: The Choice Of Parametersmentioning

confidence: 99%

See 2 more Smart Citations

A Tabu Search algorithm for the optimisation of telecommunication networks

Costamagna

Fanni

Giacinto

1998

European Journal of Operational Research

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“…Additional examples are [7], [8], [11], [12], including several parallel and multiobjective implementations [13]- [16]. Calégari et al become known for [17] developing his RND dominating set model (still supported by the maximum independent-set search method) based on a hybrid implementation that combines a greedy algorithm with his previous GA development, and presenting it in form of a framework formally known as STORMS (software tools for the optimization of resources in mobile Systems).…”

Section: A Rnd Research Foundationsmentioning

confidence: 99%

Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design Problem

Priem-Mendes

Molina

Vega‐Rodríguez

et al. 2009

IEEE Trans. Evol. Computat.

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Abstract-The radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a noncomparable efficiency. Therefore, the aim of this paper is twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons of efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve the first aim we propose a canonical RND problem formulation driven by two main directives: technology independence and a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis.

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Intelligent Flow Control under a Game Theoretic Framework

Chen

2001

Telecommunications Optimization: Heuristic and Adaptive Techniques

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Each of us is interested in optimization, and telecommunications. Via several meetings, conferences, chats, and other opportunities, we have discovered these joint interests and decided to put together this book.It certainly wasn't easy. What made it difficult is partly the reason for this book in the first place: things are moving fast! As researchers in both industry and academia, we are under constant pressure to keep up with new technologies and opportunities. Part of our day jobs involves developing good algorithms for a steady surge of new problems arising from industry. Another part of our day jobs involves finding realistic problems which might yield to some of the algorithms we have developed, which we particularly like, but which don't work on anything else. The remainder of our day jobs tends to involve lecturing (for two of us), project management (for all of us), and things like exam-setting and marking. Finally, for two of us, childcare takes up a substantial part of our night jobs. Naturally, the book was finished when we worked out how to stuff 46 hours into a single day, and keep that up for a few successive weeks.Seriously, though, telecommunicatons is growing and changing very quickly, and all of this involves the increasing availability of new real-world problems for optimization researchers to address. Telecommunications engineers tend to know all about the problems involved, but are often not aware of developments in computer science and artificial intelligence which might go a long way towards solving those problems. We therefore argue, at the beginning of Chapter 1, that there needs to be more collaboration between the communities, so that both sides can be equipped for the challenges ahead. Indeed, this book is the result of one such collaboration.We have oriented it slightly towards the telecommunications engineer who knows little about the computational techniques, rather than the other way around. This is mainly because it is much easier to describe the techniques than it is to describe the telecommunications issues; the latter is a much larger collection. However, individual chapters go into some detail, often at an introductory level, in describing particular kinds of problem. Also, we provide a brief introduction to the essence of the problems at the beginning of each part.Let us know what you think. In particular, if you are a telecommunications engineer, and have been inspired by a chapter or two in this book to try out some techniques that you otherwise wouldn't have, please get in touch: your story may make it into the next edition!

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Genetic algorithms for telecommunication network optimization

Cited by 11 publications

References 2 publications

A Tabu Search algorithm for the optimisation of telecommunication networks

A Tabu Search algorithm for the optimisation of telecommunication networks

Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design Problem

Intelligent Flow Control under a Game Theoretic Framework

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