# Achieving Diversity in Objective Space for Sample-efficient Search of Multiobjective Optimization Problems

arXiv: https://arxiv.org/abs/2306.13780 · HTML: https://mlsanity.com/paper/2306.13780/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2006.05078/) (2020)
- [Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces](https://mlsanity.com/paper/2109.10964/) (2021)
- [An Easy-to-use Real-world Multi-objective Optimization Problem Suite](https://mlsanity.com/paper/2009.12867/) (2020)
- [Constrained Efficient Global Optimization of Expensive Black-box Functions](https://mlsanity.com/paper/2211.00162/) (2022)
- [Practical heteroskedastic Gaussian process modeling for large simulation experiments](https://mlsanity.com/paper/1611.05902/) (2016)
- [Single-stage gradient-based stellarator coil design: Optimization for near-axis quasi-symmetry](https://mlsanity.com/paper/2010.02033/) (2020)

## Built on by (most cited first, 1 of 1)

- [Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization](https://mlsanity.com/paper/2501.19342/) (2025)

## Abstract (arXiv)

Efficiently solving multi-objective optimization problems for simulation optimization of important scientific and engineering applications such as materials design is becoming an increasingly important research topic. This is due largely to the expensive costs associated with said applications, and the resulting need for sample-efficient, multiobjective optimization methods that efficiently explore the Pareto frontier to expose a promising set of design solutions. We propose moving away from using explicit optimization to identify the Pareto frontier and instead suggest searching for a diverse set of outcomes that satisfy user-specified performance criteria. This method presents decision makers with a robust pool of promising design decisions and helps them better understand the space of good solutions. To achieve this outcome, we introduce the Likelihood of Metric Satisfaction (LMS) acquisition function, analyze its behavior and properties, and demonstrate its viability on various problems.