Paralleling Euclidean Particle Swarm Optimization in CUDA
Hongbing Zhu, Yongmei Guo, Jianguo Wu, Jinguang Gu, Kei Eguchi · 2011
Euclidean Particle Swarm Optimization (EPSO) is a swarm intelligence algorithm, which has been successful applied to many engineering optimization problems and shown its high search speed in these applications. However, with the increase in the dimension of optimization problems and the number of local optima, the processing speed of the EPSO has become a bottleneck of applications as each particle of them has to calculate separately fitness. In this paper the EPSO has been parallelled in Compute Unified Device Architecture (CUDA) to solve the bottleneck. Five benchmark functions had been employed to examine the performance of the parallelled EPSO (pEPSO), and the experimental results shown that the average processing of calculating fitness had been accelerated to maximum 16.27 times the original algorithm.