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 to these methods. 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 conducted on BBO benchmarking problems and several other benchmarks with diversified landscapes exhibit that OPT-GAN outperforms other traditional and neural net-based BBO algorithms.
翻译:黑盒优化(BBO)算法涉及寻找解决缺少分析细节问题的最佳办法。这类问题的大多数典型方法都是基于强烈和固定的先验假设,如高斯尼。然而,复杂的现实世界问题,特别是当希望全球最佳时,可能与其先定的假设相去甚远,因为它们具有多样性,给这些方法造成意想不到的障碍。在这项研究中,我们建议采用一种基因化的对称性对称式网基宽光谱全球优化(OPT-GAN)来估计最佳分布,并采用平衡勘探-开发交易的战略。它有可能更好地适应多样化地貌的规律和结构,而不是以前定的,例如高斯假设或分离。在BBOB基准问题上进行的实验以及其他一些有多样化地貌基准的实验显示,OFGAN-GAN比其他传统和神经网基BO算法要优于其他传统和神经网基BO。