Survivable and delay-guaranteed backbone wireless mesh network design
Journal of Parallel and Distributed Computing
Local search study of honeycomb clustering problem for cellular planning
International Journal of Mobile Network Design and Innovation
Optimal placement of antennae using metaheuristics
NMA'06 Proceedings of the 6th international conference on Numerical methods and applications
Self-organization and evolution combined to address the vehicle routing problem
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Optimisation of CDMA-based mobile telephone networks: algorithmic studies on real-world networks
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A hybrid nature-inspired optimizer for wireless mesh networks design
Computer Communications
The effects of location personalization on individuals' intention to use mobile services
Decision Support Systems
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We focus on the dimensioning process of cellular networks that addresses the evaluation of equipment global costs to cover a city. To deal with frequency assignment, that constitutes the most critical resource in mobile systems, the network is usually modeled as a pattern of regular hexagonal cells. Each cell represents the area covered by the signal of a transmitter or base station (BS). Our work emphasizes on the design of irregular hexagonal cells in an adaptive way. Hexagons transform themselves and adapt their shapes according to a traffic density map and to geometrical constraints. This process, called adaptive meshing (AM), may be seen as a solution to minimize the required number of BS to cover a region and to propose a basis for transmitter positioning. The solution we present to the mesh generation problem for mobile network dimensioning is based on the use of an evolutionary algorithm. This algorithm, called hybrid island evolutionary strategy (HIES), performs distributed computation. It allows the user to tackle problem instances with large traffic density map requiring several hundreds of cells. HIES combines local search fast computation on individuals, incorporated into a global island-like strategy. Experiments are done on one real case representing the mobile traffic load of the second French city of Lyon and on several other traffic maps from urban fictive data sets.