标题： 多引导火花烟花算法的GPU实现用于高效的黑盒神经网络优化 显示英文标题

标题： A GPU Implementation of Multi-Guiding Spark Fireworks Algorithm for Efficient Black-Box Neural Network Optimization

摘要： 群体智能优化算法由于其解决复杂优化问题的能力而受到广泛关注。 然而，大规模问题中的优化效率限制了相关方法的使用。 本文提出了一种GPU加速的多引导火花烟花算法（MGFWA），与传统的基于CPU的方法相比，显著提高了计算效率。 我们在多个神经网络黑盒优化问题上对GPU-MGFWA进行了基准测试，并证明了其在速度和解的质量方面的优越性能。 通过利用现代GPU的并行处理能力，所提出的GPU-MGFWA在大规模优化任务中实现了更快的收敛和更短的计算时间。 所提出的实现为加速群体智能算法提供了一种有前景的方法，使其更适合实时应用和大规模工业问题。 源代码可在 https://github.com/mxxxr/MGFWA 发布。 显示英文摘要

摘要： Swarm intelligence optimization algorithms have gained significant attention due to their ability to solve complex optimization problems. However, the efficiency of optimization in large-scale problems limits the use of related methods. This paper presents a GPU-accelerated version of the Multi-Guiding Spark Fireworks Algorithm (MGFWA), which significantly improves the computational efficiency compared to its traditional CPU-based counterpart. We benchmark the GPU-MGFWA on several neural network black-box optimization problems and demonstrate its superior performance in terms of both speed and solution quality. By leveraging the parallel processing power of modern GPUs, the proposed GPU-MGFWA results in faster convergence and reduced computation time for large-scale optimization tasks. The proposed implementation offers a promising approach to accelerate swarm intelligence algorithms, making them more suitable for real-time applications and large-scale industrial problems. Source code is released at https://github.com/mxxxr/MGFWA.