Abstract
Bayesian optimization, an effective method for searching the optimal solution of black-box functions, usually performs poorly for high-dimensional problems. Although many methods, such as TuRBO (Trust regions Bayesian optimization), have been proposed to solve the over-emphasis of exploration in high-dimensional global acquisition, they may converge slowly or stagnate in some cases. In this paper, we proposed a new method, called CoMABO (Covariance Matrix Adaptation for Bayesian Optimization) to enhance the convergence of TuRBO algorithm. Covariance matrix adaptation is a technique that builds Gaussian models based on the covariance matrix constructed from sampled points. CoMABO utilizes it to explore better candidate points and to optimize the complex surrogate model. Experimental results show CoMABO improves the convergence of TuRBO on various benchmark problems and real world applications.