Black-box optimization (BBO) algorithms are concerned with finding the best solutions for problems with missing analytical details. Most classical methods for such problems are based on strong and fixed a priori assumptions, such as Gaussianity. However, the complex real-world problems, especially when the global optimum is desired, could be very far from the a priori assumptions because of their diversities, causing unexpected obstacles. In this study, we propose a generative adversarial net-based broad-spectrum global optimizer (OPT-GAN) which estimates the distribution of optimum gradually, with strategies to balance exploration-exploitation trade-off. It has potential to better adapt to the regularity and structure of diversified landscapes than other methods with fixed prior, e.g., Gaussian assumption or separability. Experiments on diverse BBO benchmarks and high dimensional real world applications exhibit that OPT-GAN outperforms other traditional and neural net-based BBO algorithms.

翻译：黑箱优化算法旨在为缺失解析细节的问题寻找最优解。此类问题的大多数经典方法基于强且固定的先验假设（如高斯性）。然而，复杂的现实世界问题（尤其当需要全局最优解时）因其多样性可能远偏离先验假设，从而引发意料之外的障碍。本研究提出一种基于生成对抗网络的广谱全局优化器（OPT-GAN），该优化器通过逐步估计最优解分布，并融合探索-利用权衡策略。相较于具有固定先验（如高斯假设或可分性假设）的其他方法，该方法能更好地适应多样化地貌的规律性与结构。在多种黑箱优化基准测试及高维实际应用中的实验表明，OPT-GAN的性能优于传统及基于神经网络的BBO算法。