User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization
Joshua Hang Sai Ip, Ankush Chakrabarty, Ali Mesbah, Diego Romeres
Abstract
Incorporating user preferences into multi-objective Bayesian optimization (MOBO) allows for personalization of the op- timization procedure. Preferences are often abstracted in the form of an unknown utility function, estimated through pair- wise comparisons of potential outcomes. However, utility-driven MOBO methods can yield solutions that are dominated by nearby solutions, as non-dominance is not enforced. Additionally, classical MOBO commonly relies on estimating the entire Pareto front to identify the Pareto-optimal solutions, which can be expensive and ignore user preferences. Here, we present a new method, termed preference-utility-balanced MOBO (PUB-MOBO), that allows users to disambiguate between near-Pareto candidate solutions. PUB-MOBO combines utility-based MOBO with local multi-gradient descent to refine user-preferred solutions to be near-Pareto-optimal. To this end, we propose a novel preference-dominated utility function that concurrently preserves user-preferences and dominance amongst candidate solutions. A key advantage of PUB-MOBO is that the local search is restricted to a (small) region of the Pareto front directed by user preferences, alleviating the need to estimate the entire Pareto-front. PUB-MOBO is tested on three synthetic benchmark problems: DTLZ1, DTLZ2 and DH1, as well as on three real-world problems: Vehicle Safety, Conceptual Marine Design, and Car Side Impact. PUB-MOBO consistently outperforms state-of-the-art competitors in terms of proximity to the Pareto-front and utility regret across all the problems.
BibTeX
@article{Sai Ip_Chakrabarty_Mesbah_Romeres_2025, title={User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/34230}, DOI={10.1609/aaai.v39i19.34230}, abstractNote={Incorporating user preferences into multi-objective Bayesian optimization (MOBO) allows for personalization of the op-
timization procedure. Preferences are often abstracted in the form of an unknown utility function, estimated through pair-
wise comparisons of potential outcomes. However, utility-driven MOBO methods can yield solutions that are dominated
by nearby solutions, as non-dominance is not enforced. Additionally, classical MOBO commonly relies on estimating the entire Pareto front to identify the Pareto-optimal solutions, which can be expensive and ignore user preferences. Here, we present a new method, termed preference-utility-balanced MOBO (PUB-MOBO), that allows users to disambiguate between near-Pareto candidate solutions. PUB-MOBO combines utility-based MOBO with local multi-gradient descent to refine user-preferred solutions to be near-Pareto-optimal.
To this end, we propose a novel preference-dominated utility function that concurrently preserves user-preferences and dominance amongst candidate solutions. A key advantage of PUB-MOBO is that the local search is restricted to a (small) region of the Pareto front directed by user preferences, alleviating the need to estimate the entire Pareto-front. PUB-MOBO is tested on three synthetic benchmark
problems: DTLZ1, DTLZ2 and DH1, as well as on three real-world problems: Vehicle Safety, Conceptual Marine Design, and Car Side Impact. PUB-MOBO consistently outperforms state-of-the-art competitors in terms of proximity to the Pareto-front and utility regret across all the problems.}, number={19}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Sai Ip, Joshua Hang and Chakrabarty, Ankush and Mesbah, Ali and Romeres, Diego}, year={2025}, month={Apr.}, pages={20246-20254} }