Percorrer por autor "Gomez-Pulido, Juan 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.
- Deconvolution of X-ray Diffraction Profiles Using Genetic Algorithms and Differential EvolutionPublication . Santos, Sidolina P.; Gomez-Pulido, Juan A.; Sanchez-Bajo, FlorentinoSome optimization problems arise when X-ray diffraction profiles are used to determine the microcrystalline characteristics of materials, like the detection of diffraction peaks and the deconvolution process necessary to obtain the pure diffraction profile. After applying the genetic algorithms to solve satisfactorily the first problem, in this work we propose two evolutionary algorithms to solve the deconvolution problem. This optimization problem targets the objective of obtaining the profile that contains the microstructural characteristics of a material from the experimental data and instrumental effects. This is a complex problem, ill-conditioned, since not only there are many possible solutions, but also some of them lack physical sense. In order to avoid such circumstance, the regularization techniques are used, where the optimization of some of their parameters by means of intelligent computing permits to obtain the optimal solutions of the problem.
- 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.
- On the Use of Perfect Sequences and Genetic Algorithms for Estimating the Indoor Location of Wireless SensorsPublication . Ferreira, Marco; Bagarić, J.; Lanza-Gutierrez, Jose M.; Mendes, Silvio; Pereira, João; Gomez-Pulido, Juan A.Determining the indoor location is usually performed by using several sensors. Some of these sensors are fixed to a known location and either transmit or receive information that allows other sensors to estimate their own locations. The estimation of the location can use information such as the time-of-arrival of the transmitted signals, or the received signal strength, among others. Major problems of indoor location include the interferences caused by the many obstacles in such cases, causing among others the signal multipath problem and the variation of the signal strength due to the many transmission media in the path from the emitter to the receiver. In this paper, the creation and usage of perfect sequences that eliminate the signal multipath problem are presented. It also shows the influence of the positioning of the fixed sensors to the precision of the location estimation. Finally, genetic algorithms were used for searching the optimal location of these fixed sensors, therefore minimizing the location estimation error.
- Performance, power and scalability analysis of HEVC interpolation filter using FPGAsPublication . Gomez-Pulido, Juan A.; Cordeiro, Paulo J.; Assunção, PedroMotion compensation is the most time-consuming stage of the most recent video coding standard, and uses an interpolation filter to handle efficiently the video bitstream. When high resolutions, low power budgets and huge amount of video data are demanded, exploiting parallelism is a mandatory task. In this paper we propose an implementation of the interpolation filter using the reconfigurable hardware technology, in order to build parallel computing systems that offer a high performance, in terms of computing time and power consumption. The timing simulations and energy analysis performed on different devices show that the on-chip replication of the filter provides high speedups with regard to general purpose processors. The good experimental results motivates us to do a first approach to scalable parallel computing systems where parallelism is exploited from fine to coarse grain, multiplying the speedups obtained. In particular, we propose an on-chip multiprocessor system where filters act as coprocessors of embedded high-performance and low-power microprocessors, linked among them by point-to-point buses. This on-chip architecture can be applied to high performance computing systems based on the same reconfigurable hardware technology.
- Planning the Deployment of Indoor Wireless Sensor Networks Through Multiobjective Evolutionary TechniquesPublication . Lanza-Gutierrez, Jose M.; Gomez-Pulido, Juan A.; Mendes, Silvio; M. Ferreira; Pereira, J. S.This work deals with how to efficiently deploy an indoor wireless sensor network, assuming a novel approach in which we try to leverage existing infrastructure. Thus, given a set of low-cost sensors, which can be plugged into the grid or powered by batteries, a collector node, and a building plan, including walls and plugs, the purpose is to deploy the sensors optimising three conflicting objectives: average coverage, average energy cost, and average reliability. Two MultiObjective (MO) genetic algorithms are assumed to solve this issue, NSGA-II and SPEA2. These metaheuristics are applied to solve the problem using a freely available data set. The results obtained are analysed considering two MO quality metrics: hypervolume and set coverage. After applying a statistical methodology widely accepted, we conclude that SPEA2 provides the best performance on average considering such data set.