Implementation of Parallel Simplified Swarm Optimization in CUDA
Integration and Collaboration Laboratory, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan
arXiv:2110.01470 [cs.NE], (1 Oct 2021)
@misc{yeh2021implementation,
title={Implementation of Parallel Simplified Swarm Optimization in CUDA},
author={Wei-Chang Yeh and Zhenyao Liu and Shi-Yi Tan and Shang-Ke Huang},
year={2021},
eprint={2110.01470},
archivePrefix={arXiv},
primaryClass={cs.NE}
}
As the acquisition cost of the graphics processing unit (GPU) has decreased, personal computers (PC) can handle optimization problems nowadays. In optimization computing, intelligent swarm algorithms (SIAs) method is suitable for parallelization. However, a GPU-based Simplified Swarm Optimization Algorithm has never been proposed. Accordingly, this paper proposed Parallel Simplified Swarm Optimization (PSSO) based on the CUDA platform considering computational ability and versatility. In PSSO, the theoretical value of time complexity of fitness function is O (tNm). There are t iterations and N fitness functions, each of which required pair comparisons m times. pBests and gBest have the resource preemption when updating in previous studies. As the experiment results showed, the time complexity has successfully reduced by an order of magnitude of N, and the problem of resource preemption was avoided entirely.
October 10, 2021 by hgpu
