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- W2076390126 abstract "Several Ant Colony Optimization (ACO) algorithms have been proposed to solve multiobjective problems. MOEA/D-ACO is a multiobjective algorithm based on ACO and the decomposition approach and presents best results when compared with MOEA/D and BiCriterion on discrete optimization problems. This paper proposes a new parallel implementation of MOEA/D-ACO for execution on the Graphics Processing Unit (GPU) using NVIDIA CUDA in order to improve the efficiency, reaching high quality results in a reasonable execution time. Based on recent researches, both the solution construction and pheromone update phases are implemented using a data parallel approach. We use the multiobjective Travelling Salesman Problem (MTSP) as application. We report speedups of up to 11x from the sequential implementation. Moreover, the results point out that: the number of objectives and the number of sub problems affect directly the speedup." @default.
- W2076390126 created "2016-06-24" @default.
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- W2076390126 date "2014-10-01" @default.
- W2076390126 modified "2023-09-25" @default.
- W2076390126 title "A GPU Implementation of MOEA/D-ACO for the Multiobjective Traveling Salesman Problem" @default.
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- W2076390126 doi "https://doi.org/10.1109/bracis.2014.65" @default.
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