A multiobjective genetic algorithm for radio network optimization

Author

Meunier, H. ; Talbi, El-Ghazali ; Reininger, Philippe

Author_Institution

Lille I Univ., Villeneuve d´´Ascq, France

Volume

1

fYear

2000

fDate

2000

Firstpage

317

Abstract

Engineering of mobile telecommunication networks endures two major problems: the design of the network and the frequency assignment. We address the first problem in this paper, which has been formulated as a multiobjective constrained combinatorial optimisation problem. We propose a genetic algorithm (GA) that aims to approximate the Pareto frontier of the problem. Advanced techniques have been used, such as Pareto ranking, sharing and elitism. The GA has been implemented in parallel on a network of workstations to speed up the search. To evaluate the performance of the GA, we have introduced two new quantitative indicators: the entropy and the contribution. Encouraging results are obtained on real-life problems

Keywords

Pareto distribution; combinatorial mathematics; constraint theory; entropy; genetic algorithms; operations research; parallel algorithms; radio networks; software performance evaluation; telecommunication computing; workstation clusters; Pareto frontier approximation; Pareto ranking; contribution; elitism; entropy; frequency assignment; mobile telecommunication networks; multiobjective constrained combinatorial optimisation problem; multiobjective genetic algorithm; network design; parallel algorithm; performance evaluation; quantitative indicators; radio network optimization; search speed; sharing; workstation network; Base stations; Constraint optimization; Design engineering; Entropy; Frequency; Genetic algorithms; Performance evaluation; Radio network; Testing; Workstations;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2000. Proceedings of the 2000 Congress on

Conference_Location

La Jolla, CA

Print_ISBN

0-7803-6375-2

Type

conf

DOI

10.1109/CEC.2000.870312

Filename

870312