A CUDA Implementation of the Standard Particle Swarm Optimization
A CUDA Implementation of the Standard Particle Swarm Optimization
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DOI：
10.1109/synasc.2016.043
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发表时间：
2016-09
期刊：
影响因子：
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通讯作者：
M. M. Hussain-M.;H. Hattori;N. Fujimoto
中科院分区：
文献类型：
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作者：
M. M. Hussain-M.;H. Hattori;N. Fujimoto
鸟类和鱼类的社会学习过程启发了启发式粒子群优化（PSO）搜索算法的发展。图形处理器（GPU）和统一计算设备架构（CUDA）平台的进步对减少搜索算法开发中的计算时间起着重要作用。本文提出了一种基于CUDA架构的GPU上的标准粒子群优化算法（SPSO）的实现，该算法采用合并内存访问。该算法的评估上一套著名的基准优化功能。在NVIDIA GeForce GTX 980 GPU和3.20 GHz Intel Core i5 4570 CPU上进行了实验，测试结果表明，GPU算法的运行速度最高为相应CPU算法的46倍。因此，该算法可以用来改善所需的时间来解决优化问题。
The social learning process of birds and fishesinspired the development of the heuristic Particle Swarm Optimization (PSO) search algorithm. The advancement of GraphicsProcessing Units (GPU) and the Compute Unified Device Architecture (CUDA) platform plays a significant role to reduce thecomputational time in search algorithm development. This paperpresents a good implementation for the Standard Particle SwarmOptimization (SPSO) on a GPU based on the CUDA architecture, which uses coalescing memory access. The algorithm is evaluatedon a suite of well-known benchmark optimization functions. Theexperiments are performed on an NVIDIA GeForce GTX 980GPU and a single core of 3.20 GHz Intel Core i5 4570 CPUand the test results demonstrate that the GPU algorithm runsabout maximum 46 times faster than the corresponding CPUalgorithm. Therefore, this proposed algorithm can be used toimprove required time to solve optimization problems.