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- W2761283610 abstract "This paper presents a GPU-accelerated parallel MAX-MIN Ant System (MMAS) algorithm based on an approach named grouped roulette wheel selection (G-Roulette). A data parallel model is adapted in the proposed approach with special consideration for GPU architecture. We propose a G-Roulette strategy to enhance the parallel computation of fitness-proportionate selection. The G-Roulette strategy includes two hierarchical stages to choose the optimal city. Consequently the running time is decreased by the G-Roulette strategy. We modify the MMAS with dynamical evaporation factor in the stage of pheromone updating. Experimental results show that G-Roulette enhanced MMAS algorithm is competitive with other state-of-art parallel ACO algorithms." @default.
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- W2761283610 date "2017-04-01" @default.
- W2761283610 modified "2023-09-27" @default.
- W2761283610 title "A GPU-based parallel MAX-MIN Ant System algorithm with grouped roulette wheel selection" @default.
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- W2761283610 doi "https://doi.org/10.1109/cscwd.2017.8066721" @default.
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