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• W2048187548 abstract "Particle swarm optimization (PSO) is a population-based stochastic and derivative-free method that has been used to solve various optimization problems due to its simplicity and efficiency. While solving high-dimensional or complicated problems, PSO requires a large number of particles to explore the problem domains and consequently introduces high computational costs. In this paper, we focus on the acceleration of PSO for solving box-constrained, load-balanced optimization problems by parallelization on a graphics processing unit (GPU). We propose a GPU-accelerated PSO (GPSO) algorithm by using a thread pool model and implement GPSO on a GPU. Numerical results show that the GPU architecture fits the PSO framework well by reducing computational timing, achieving high parallel efficiency and finding better optimal solutions by using a large number of particles. For example, while solving the 100-dimensional test problems with 65,536 (16×212) particles, GPSO has achieved up to 280X and 83X speedups on a NVIDIA Tesla C1060 1.30 GHz GPU relative to an Intel Xeon-X5450 3.00 GHz central processing unit running in single- and quad-core mode, respectively. GPSO provides a promising method for tackling high-dimensional and difficult optimization problems using a low-cost and many-core GPU system." @default.
• W2048187548 created "2016-06-24" @default.
• W2048187548 creator A5005786936 @default.
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• W2048187548 date "2012-02-01" @default.
• W2048187548 modified "2023-09-23" @default.
• W2048187548 title "Accelerating parallel particle swarm optimization via GPU" @default.
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• W2048187548 doi "https://doi.org/10.1080/10556788.2010.509435" @default.
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