Year: 2025
Type: article
Abstract: Particle Swarm Optimization (PSO) is a widely used heuristic algorithm valued for its simplicity and robustness in solving diverse optimization problems. However, its high computational cost often limits large-scale applications. With the rapid development of parallel computing and Graphics Processing Units (GPUs), researchers have increasingly leveraged these technologies to enhance PSO efficiency. This paper introduces a High-Efficiency PSO (HEPSO) algorithm designed for GPU-based architectures. HEPSO improves computational performance through two key strategies: (1) transferring data initialization from the CPU to the GPU to reduce I/O overhead caused by repeated data migration, and (2) incorporating a self-adaptive thread management mechanism to enhance execution efficiency. Experiments conducted on nine benchmark optimization functions demonstrate that HEPSO achieves more than a sixfold speedup compared to conventional GPU-PSO. Moreover, in terms of convergence time, HEPSO requires only about one-third of the runtime in most cases.
Source: Applied Sciences
DOI: 10.3390/app151910429
Authors: Zhikun Liu, Jia Wu, Bolei Dong, Ye Liu
Institution: Xi'an Shiyou University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Funder: Xi'an Shiyou University
OpenAlex ID: W4414548965