A CUDA Implementation of the Standard Particle Swarm Optimization

被引：0

作者：

Hussain, Md. Maruf ^{[1
]}

Hattori, Hiroshi ^{[2
]}

Fujimoto, Noriyuki ^{[2
]}

机构：

[1] Osaka Prefecture Univ, Grad Sch Sci, Sakai, Osaka, Japan

[2] Osaka Prefecture Univ, Grad Sch Engn, Sakai, Osaka, Japan

来源：

PROCEEDINGS OF 2016 18TH INTERNATIONAL SYMPOSIUM ON SYMBOLIC AND NUMERIC ALGORITHMS FOR SCIENTIFIC COMPUTING (SYNASC) | 2016年

关键词：

Particle Swarm Optimization (PSO); GPGPU; coalescing memory access; cuRAND; atomic function;

D O I：

10.1109/SYNASC.2016.37

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The social learning process of birds and fishes inspired the development of the heuristic Particle Swarm Optimization (PSO) search algorithm. The advancement of Graphics Processing Units (GPU) and the Compute Unified Device Architecture (CUDA) platform plays a significant role to reduce the computational time in search algorithm development. This paper presents a good implementation for the Standard Particle Swarm Optimization (SPSO) on a GPU based on the CUDA architecture, which uses coalescing memory access. The algorithm is evaluated on a suite of well-known benchmark optimization functions. The experiments are performed on an NVIDIA GeForce GTX 980 GPU and a single core of 3.20 GHz Intel Core i5 4570 CPU and the test results demonstrate that the GPU algorithm runs about maximum 46 times faster than the corresponding CPU algorithm. Therefore, this proposed algorithm can be used to improve required time to solve optimization problems. Index terms-Particle Swarm Optimization (PSO),

引用

页码：219 / 226

页数：8

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