Particle Swarm Optimization (PSO) is a widely employed heuristic-based method that effectively addresses a variety of optimization problems owing to its simplicity and robustness. Despite its advantages, PSO's extensive computational demands can hinder its practical applications. As parallel computing and Graphics Processing Units (GPUs) have advanced, researchers have explored these technologies to overcome the computational efficiency limitations of PSO. This paper presents a High-Efficiency PSO (HEPSO) algorithm, which optimizes the PSO process within a GPU-based architecture. The HEPSO algorithm enhances GPU computational performance by: 1) transferring the data initialization process from CPUs to GPUs, minimizing the I/O overhead resulting from repetitive data migration during computation. 2) implementing a self-adaptive thread management strategy to improve algorithm execution efficiency. To evaluate the efficacy of HEPSO, we conducted experiments using four benchmark optimization functions. Our findings indicate that the time speedup ratio of HEPSO compared to GPU-PSO exceeds sixfold. Furthermore, when assessing the time required for function convergence, HEPSO outperforms GPU-PSO, necessitating only 1/3 of the time in most instances.

Particle Swarm Optimization (PSO) is one of the most commonly heuristics-based methods that has been used to solve various optimization problems due to its simplicity and robustness. However, when comes to practical applications, it requires a huge computational cost. With the development of parallel computing and Graphics Processing Unit (GPU) calculating, many researchers have tried taking these techniques to break down the obstacle of computational efficiency. It is a challenging problem for the long-term application of PSO. In this paper, we propose a HEPSO algorithm that focuses on the procedure optimization of PSO in GPU-based architecture. It optimizes the GPU computation process from two following aspects: 1) Migrate the data initialization procedure from CPUs to GPUs to reduce the huge IO loss caused by repeating migration while the computing process. 2) Employ a self-adaptive thread management strategy to improve the algorithm execution efficiency. Moreover, we use four benchmark optimization functions to test the efficiency of our HEPSO. The experiment results show that the time speedup ratio between HEPSO and GPU-PSO can exceed 6 times. Meanwhile, when we evaluate the performance of HEPSO with the time consumption for functions converge, HEPSO only needs 1/3 time of GPU-PSO in most cases.