Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach
This addresses the challenge of efficient multi-objective optimization in resource-constrained settings like power system design, but it is incremental as it builds on existing multi-fidelity and entropy search concepts.
The paper tackles the problem of optimizing multiple objectives with multi-fidelity function evaluations to approximate the Pareto set while minimizing resource consumption, and the proposed MF-OSEMO method significantly outperforms state-of-the-art single-fidelity algorithms in experiments on synthetic and real-world benchmarks.
We study the novel problem of blackbox optimization of multiple objectives via multi-fidelity function evaluations that vary in the amount of resources consumed and their accuracy. The overall goal is to approximate the true Pareto set of solutions by minimizing the resources consumed for function evaluations. For example, in power system design optimization, we need to find designs that trade-off cost, size, efficiency, and thermal tolerance using multi-fidelity simulators for design evaluations. In this paper, we propose a novel approach referred as Multi-Fidelity Output Space Entropy Search for Multi-objective Optimization (MF-OSEMO) to solve this problem. The key idea is to select the sequence of candidate input and fidelity-vector pairs that maximize the information gained about the true Pareto front per unit resource cost. Our experiments on several synthetic and real-world benchmark problems show that MF-OSEMO, with both approximations, significantly improves over the state-of-the-art single-fidelity algorithms for multi-objective optimization.