OPT-GAN: A Broad-Spectrum Global Optimizer for Black-box Problems by Learning Distribution

TL;DR

OPT-GAN is a novel black-box optimization method using generative adversarial networks to adaptively estimate the distribution of optima, improving performance on diverse and high-dimensional problems.

Contribution

It introduces a broad-spectrum optimizer that learns the distribution of solutions, overcoming fixed prior assumptions like Gaussianity in traditional methods.

Findings

Outperforms traditional BBO algorithms on benchmark tests.

Effective in high-dimensional real-world applications.

Balances exploration and exploitation adaptively.

Abstract

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.