Multi Objective Regionalized Bayesian Optimization via Entropy Search

Abstract

Line search optimization methods fail with multiple objective functions whose gradients are unavailable. The center of a crowded, trusted region is typically chosen as the point on the Pareto front with the highest hypervolume contribution. The proposed approach uses an entropy selection procedure to search the entire Pareto front, avoiding the computation of the Pareto front samples via cheap multi-objective optimization. By reducing uncertainty in each region, the algorithm directs its search towards areas with the highest potential for Pareto improvement. We tested the proposed method on the DTLZ test suite and other real-world applications, such as the welded beam design problem and the trajectory planning rover problem. The proposed approach yields results at par with state-of-the-art methods for exploring the Pareto front