CUDA-based hierarchical multi-block particle swarm optimization algorithm

T. Lan, Maoyun Guo, J. Qu, Yi Chai, Zhenglei Liu, Xunjie Zhang
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引用次数: 2

Abstract

In order to improve the traditional Particle Swarm Optimization (PSO) algorithm's speed and optimization ability, this paper proposes a new algorithm based on CUDA (Compute Unified Device Architecture) technology which employs the two level PSO, the bottom level PSO and the top level PSO. And in the bottom level, the particles are divided into N groups, each of which will run the PSO and send the best particle to the top level individually to achieve better convergency. And the algorithm applys the CUDA threads to run the above PSO at different levels parallel to accelerate the algorithm speed. The simulation results show that the performance of the algorithm the paper provided is better than that of the traditional PSO.
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基于cuda的分层多块粒子群优化算法
为了提高传统粒子群优化算法的速度和优化能力,本文提出了一种基于CUDA(计算统一设备架构)技术的粒子群优化算法,该算法采用底层粒子群优化算法和顶层粒子群优化算法两层结构。在底层,粒子被分成N组,每组运行粒子群,并将最佳粒子单独发送到顶层,以达到更好的收敛性。该算法利用CUDA线程在不同级别并行运行上述PSO,以加快算法速度。仿真结果表明,本文算法的性能优于传统粒子群算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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