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 a high dimensional real world application exhibit that OPT-GAN outperforms other traditional and neural net-based BBO algorithms.
翻译:黑匣子优化(BBO)算法(BBO)关注的是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,如高山主义。然而,复杂的现实世界问题,特别是当希望全球最佳时,由于其多样性,可能造成意想不到的障碍,可能远非先验假设。在这项研究中,我们提议一种基因化的对称式网基宽光谱全球优化(OPT-GAN)(OPT-GAN)(OPT-GAN)(OPT-GAN))(OPT-GAN)(OPT-GAN)(OPT-GAN)(OPT-GAN)(OPT-GAN)(OPT-GAN)(OPT-GAN)(OPT-GAN)(OPT-GAN) (Offeral) (ObAN) (Oban-Neet-Net-BO(BBO) (BBBBBB),它有可能比其他传统和神经网基的算法更适应常规和神经基算法。