# Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation

**Published:** September 15, 2026
**Author:** Chao Jiang, Yueling Huang, Miqing Li
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/en/articles/converge-then-diversify-decoupling-convergence-and-diversity-in-multi-ob

Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is to adequately approximate the Pareto front; that is, to obtain a high-quality solution set with 1) good…

> From the original source: **arXiv — cs.AI**
> [Read full article at arXiv — cs.AI](https://arxiv.org/abs/2609.13396)

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