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.
@article{arxiv.2412.07444,
title = {MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler},
author = {Diederick Vermetten and Jeroen Rook and Oliver L. Preuß and Jacob de Nobel and Carola Doerr and Manuel López-Ibañez and Heike Trautmann and Thomas Bäck},
journal= {arXiv preprint arXiv:2412.07444},
year = {2024}
}