RND (Radio Network Design) is a Telecommunication problem consisting in covering a certain geographical area by using the smallest number of radio antennas achieving the biggest cover rate. This is an important problem, for example, in mobile/cellular technology. RND can be solved by bio-inspired algorithms. In this work we use different metaheuristics to tackle this problem. PBIL (Population-Based Incremental Learning), based on genetic algorithms and competitive learning (typical in neural networks), is a population evolution model based on probabilistic models. DE (Differential Evolution) is a very simple population-based stochastic function minimizer used in a wide range of optimization problems, including multi-objective optimization. SA (Simulated Annealing) is a classic trajectory descent optimization technique. CHC is a particular class of evolutionary algorithm which does not use mutation and relies instead on incest prevention and disruptive crossover. Due to the complexity of such a large analysis including so many techniques, we have used not only sequential algorithms, but grid computing with BOINC in order to execute thousands of experiments in only several days using around 100 computers. In this paper we present the most interesting results from our work, indicating the pros and cons of the studied solvers.
['Miguel A. Vega-Rodríguez', 'Juan A. Gómez-Pulido', 'Enrique Alba', 'David Vega-Pérez', 'Silvio Priem-Mendes', 'Guillermo Molina']
Evaluation of Different Metaheuristics Solving the RND Problem