A parallel and hybrid multi-objective evolutionary algorithm applied to the design of cellular networks

Cellular network design is a major issue in mobile telecommunication systems. In this paper , a model of the problem in its full practical complexity, based on multiobjective constrained combinatorial optimization, has been investigated. We adopted the Pareto approach at resolution in order to compute a set of diversified non-dominated networks, thus removing the need for the designer to rank or weight objectives. We design an asynchronous steady-state evolutionary algorithm for its resolution. Specific coding scheme and genetic and neighborhood operators have been designed for the tackled problem. On the other side, we make use of many generic features related to advanced intensification and diversification search techniques, hybridization of metaheuristics and grid computing for the distribution of the computations. They aim at improving the quality of networks and robustness, at speeding-up the search, hence efficiently solving large instances of the problem. Using realistic benchmarks, the computed networks and speed-ups on parallel/distributed architectures show the efficiency and the scalability of hierarchical models of hybridization and parallelization used in conjunction