Examinando por Autor "Poveda Chaves, Roberto Manuel"
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Ítem Algoritmo genético paralelo implementado en GPU para la solución del problema de asignación cuadrática multiobjetivoPupiales Arévalo, Andrés; Poveda Chaves, Roberto Manuel; Poveda Chaves, Roberto Manuel [0000-0002-6694-7673]This work presents the research results for the design and parallel implementation of the NSGA-II algorithm, combined with an adaptation of the Greedy 2-opt local search heuristic for solving Multiobjective Quadratic Assignment Problems (mQAP). , for its acronym in English). The algorithm was implemented in CUDA C++ using an RTX 2060 Graphics Processing Unit (GPU), to exploit the hardware parallelism offered by this device. The test instances addressed for the evaluation of the algorithm and its implementation correspond to representative cases from the literature.Ítem Implementación de un Algoritmo BIO-Inspirado para la Optimización de Funciones Multi-Objetivo Basado en Quimiotaxis de Bacterias Sobre GPGPU’SAlmanza León, Jesús Ignacio; Poveda Chaves, Roberto ManuelThis document describes the implementation of a algorithm based on bacterial chemotaxis on GPU for multi-objective optimization functions. This algorithm was validated with three (3) benchmark problems (ZDT-1, SCH, POL) and implementing three (3) different performance measures to compare their results with the BCMOA and NSGA-II algorithms.Ítem Quadratic assignment problem (qap) on gpu through a master-slave pga(Universidad Distrital Francisco José de Caldas) Castellanos Millán, Julián Octavio; Amarillo Calvo, Víctor Hugo; Poveda Chaves, Roberto ManuelÍtem Soluciones cercanas al optimo para el problema del ladrón viajero a través de un algoritmo genético paralelo implementado en unidades de procesamiento grafico (gpus)Wisk, Sebastian; Poveda Chaves, Roberto Manuel; Poveda Chaves, Roberto Manuel [0000-0002-6694-7673]The Traveling Thief Problem (TTP) is a new and important combinatorial optimization problem that combines two outstanding problems of the NP-Hard class; which are the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). The TTP has been attempted to be solved using different algorithms and heuristics; The research proposed in this paper will seek an implementation on GPUs of a parallel genetic algorithm to find solutions close to the optimal through the exhaustive use of multiprocessing hardware and the appropriate use of memory spaces. The problems tested correspond to prominent problems in the literature (Benchmark Problems) and the results of the execution will be compared against other results documented so far to determine the validity of our algorithm.