GPU-Accelerated High-Efficiency PSO with Initialization and Thread Self-Adaptation

Zhikun Liu

doi:10.3390/app151910429

GPU-Accelerated High-Efficiency PSO with Initialization and Thread Self-Adaptation

2025-09-25

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DOI：10.3390/app151910429

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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.

Keywords:

PSO

GPU

initialization strategy

thread self-adaption

GPU-Accelerated High-Efficiency PSO with Initialization and Thread Self-Adaptation

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