A Graphics Processing Unit-Based Parallel Simplified Swarm Optimization Algorithm for Enhanced Performance and Precision

Authors

  • Wenbo Zhu
  • Shang-Ke Huang
  • Wei-Chang Yeh National Tsing Hua University, Taiwan
  • Zhenyao Liu
  • Chia-Ling Huang

DOI:

https://doi.org/10.6977/IJoSI.202510_9(5).0003

Abstract

Graphics processing units (GPUs) have emerged as powerful platforms for parallel computing, enabling personal computers to solve complex optimization tasks effectively. Although swarm intelligence algorithms (SIAs) naturally lend themselves to parallelization, a GPU-based implementation of the Simplified Swarm Optimization (SSO) algorithm has not been reported in the literature. This paper introduces a CUDA Simplified Swarm Optimization (CUDA-SSO) algorithm on the CUDA platform, with a time-complexity analysis of O(Ngen ´ Nsol ´ Nvar), where tt is the number of iterations, Nsol is the population size (i.e., number of fitness function evaluations), and Nvar represents the required pairwise comparisons. By eliminating resource preemption of personal best (pBests) and global best (gBest) updates, CUDA-SSO significantly reduces the overall complexity and avoids concurrency conflicts. Numerical experiments demonstrate that the proposed approach achieves an order-of-magnitude improvement in run time with superior solution precision relative to CPU-based SSO, making it a compelling methodology for large-scale, data-parallel optimization tasks.

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Published

2025-10-16

Issue

Vol. 9 No. 5 (2025): International Journal of Systematic Innovation

Section

Regular full papers

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