用GPU加速原生变形虫优化器，速度最高提升6.7倍。
cuAPO: A CUDA-based Parallelization of Artificial Protozoa Optimizer
- 基于CUDA实现变形虫优化器的并行化，利用GPU加速计算。
- 在CEC2022基准函数上实现最高6.7倍的加速比。
- 适用于需要快速求解复杂优化问题的研究者和工程师。
元启发式算法因其能够提供近似最优解而广泛应用于复杂问题求解，但其执行时间随问题规模和解空间增大而显著增加。为获得更优结果，需进行大量迭代，导致耗时严重。为此，研究者正致力于开发先进元启发式算法的并行版本。本文提出一种基于CUDA的先进人工变形虫优化器（Artificial Protozoa Optimizer）的并行化方法，利用GPU加速。我们实现了该算法的原有串行版本与所提并行版本，并在一组CEC2022基准函数上进行了性能对比。实验结果表明，所提并行版本可实现最高达6.7倍的速度提升。此外，还通过真实世界应用——图像阈值分割，进一步验证了两种算法的有效性与效率。
原文摘要 · Abstract (English)
Metaheuristic algorithms are widely used for solving complex problems due to their ability to provide near-optimal solutions. But the execution time of these algorithms increases with the problem size and/or solution space. And, to get more promising results, we have to execute these algorithms for a large number of iterations, requiring a large amount of time and this is one of the main issues found with these algorithms. To handle the same, researchers are now-a-days working on design and development of parallel versions of state-of-the-art metaheuristic optimization algorithms. We, in this paper, present a CUDA-based parallelization of state-of-the-art Artificial Protozoa Optimizer leveraging GPU acceleration. We implement both the existing sequential version and the proposed parallel version of Artificial Protozoa Optimizer for a performance comparison. Our experimental results calculated over a set of CEC2022 benchmark functions demonstrate a significant performance gain i.e. up to 6.7 times speed up is achieved with proposed parallel version. We also use a real world application, i.e., Image Thresholding to compare both algorithms.
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