# MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler

arXiv: https://arxiv.org/abs/2412.07444 · HTML: https://mlsanity.com/paper/2412.07444/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2024.

## Built on by (most cited first, 2 of 2)

- [Benchmarking MOEAs for solving continuous multi-objective RL problems](https://mlsanity.com/paper/2505.13726/) (2025)
- [On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization](https://mlsanity.com/paper/2605.04954/) (2026)

## Abstract (arXiv)

Benchmarking is one of the key ways in which we can gain insight into the strengths and weaknesses of optimization algorithms. In sampling-based optimization, considering the anytime behavior of an algorithm can provide valuable insights for further developments. In the context of multi-objective optimization, this anytime perspective is not as widely adopted as in the single-objective context. In this paper, we propose a new software tool which uses principles from unbounded archiving as a logging structure. This leads to a clearer separation between experimental design and subsequent analysis decisions. We integrate this approach as a new Python module into the IOHprofiler framework and demonstrate the benefits of this approach by showcasing the ability to change indicators, aggregations, and ranking procedures during the analysis pipeline.