Percorrer por autor "Vega-Rodriguez, Miguel A."
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- Accelerating floating-point fitness functions in evolutionary algorithms: a FPGA-CPU-GPU performance comparisonPublication . Gomez-Pulido, Juan A.; Vega-Rodriguez, Miguel A.; Sanchez-Perez, Juan M.; Priem-Mendes, Silvio; Carreira, VitorMany large combinatorial optimization problems tackled with evolutionary algorithms often require very high computational times, usually due to the fitness evaluation. This fact forces programmers to use clusters of computers, a computational solution very useful for running applications of intensive calculus but having a high acquisition price and operation cost, mainly due to the Central Processing Unit (CPU) power consumption and refrigeration devices. A low-cost and high-performance alternative comes from reconfigurable computing, a hardware technology based on Field Programmable Gate Array devices (FPGAs). The main objective of the work presented in this paper is to compare implementations on FPGAs and CPUs of different fitness functions in evolutionary algorithms in order to study the performance of the floating-point arithmetic in FPGAs and CPUs that is often present in the optimization problems tackled by these algorithms. We have taken advantage of the parallelism at chip-level of FPGAs pursuing the acceleration of the fitness functions (and consequently, of the evolutionary algorithms) and showing the parallel scalability to reach low cost, low power and high performance computational solutions based on FPGA. Finally, the recent popularity of GPUs as computational units has moved us to introduce these devices in our performance comparisons. We analyze performance in terms of computation times and economic cost.
- GRASP and grid computing to solve the location area problemPublication . Almeida da Luz, Sónia Maria; Rodriguez-Hermoso, Manuel M.; Vega-Rodriguez, Miguel A.; Gomez-Pulido, Juan A.; Sanchez-Perez, Juan M.In this paper we present a new approach based on the GRASP (Greedy Randomized Adaptive Search Procedure) metaheuristic to solve the Location Area (LA) problem over a grid computing environment. All the experiments carried out to complete this study were executed in a real grid environment provided by a virtual organization of the European project EGEE. These experiments were divided into sequential and parallel executions with the intention of analyzing the behavior of the different variants of GRASP when applied to the LA problem. We have used four distinct test networks and also decided to compare the results obtained by this new approach with those achieved through other algorithms from our previous work and also by other authors. The experimental results show that this GRASP based approach is very encouraging because, with the grid computing, the execution time is much more reduced and the results obtained are very similar to those of other techniques proposed in the literature.
- Multiobjective frequency assignment problem using the MO-VNS and MO-SVNS algorithmsPublication . Maximiano, Marisa; Vega-Rodriguez, Miguel A.; Gomez-Pulido, Juan A.; Sanchez-Perez, Juan M.In GSM networks, the Frequency Assignment is a critical task for the mobile operators. In this paper we study different multiobjective metaheuristics to address the Frequency Assignment problem (FAP). In fact, solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. This is the scenario in the FAP, where it is sought an assignment of frequencies to a number of transmitters in as efficient way as possible. The multiobjective FAP tries to minimize the number of interferences caused when a limited number of frequencies needs to be assigned to a high number of transceivers. Besides these interferences costs, the separation costs are also considered. Our approach uses a Multiobjective Variable Neighborhood Search (MO-VNS) algorithm and also its variant Multiobjective Skewed Variable Neighborhood Search (MO-SVNS). Two real-world GSM networks were used, currently being in operation, to test the presented metaheuristics.