Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations


Many science, engineering, and design optimization problems require balancing the trade-offs between several conflicting objectives. The objectives are often black-box functions whose evaluations are time-consuming and costly. Multi-objective Bayesian optimization can be used to automate the process of discovering the set of optimal solutions, called Pareto-optimal, while minimizing the number of performed evaluations. To further reduce the evaluation time in the optimization process, testing of several samples in parallel can be deployed. We propose a novel multi-objective Bayesian optimization algorithm that iteratively selects the best batch of samples to be evaluated in parallel. Our algorithm approximates and analyzes a piecewise-continuous Pareto set representation. This representation allows us to introduce a batch selection strategy that optimizes for both hypervolume improvement and diversity of selected samples in order to efficiently advance promising regions of the Pareto front. Experiments on both synthetic test functions and real-world benchmark problems show that our algorithm predominantly outperforms relevant state-of-the-art methods.

NeurIPS 2020


A SOTA algorithm for multi-objective optimization which improves the Pareto front using minimal number of evaluations and can work with batch evaluation settings. Our algorithm combines a powerful solver running on differentiable surrogate problem model fitted using history data and a diversity-aware selection strategy of candidate solutions to evaluate on real problem.