Evaluation of Different Metaheuristics Solving the RND Problem

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 o...

• Type: Conference
• Year: 2007
• Where: EVOW
• Authors: Miguel A. Vega-Rodríguez, Juan Antonio Gómez Pulido, Enrique Alba, David Vega-Pérez, Silvio Priem-Mendes, Guillermo Molina
• Keywords: Artificial Intelligence, Certain Geographical Area, EVOW 2007, Population-Based Incremental Learning, Population-based Stochastic Function