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

arXiv (Cornell University)

Abstract

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.

Authors 8

1. Diederick Vermetten Aachen

   Leiden University

   Affiliation as printed

   LIACS , Leiden University , Leiden , The Netherlands

2. Jeroen Rook

   University of Twente

   Affiliation as printed

   University of Twente , Enschede , The Netherlands

3. Oliver Ludger Preuß
4. Jacob de Nobel Aachen

   Leiden University

   Affiliation as printed

   LIACS , Leiden University , Leiden , The Netherlands

5. Carola Doerr

   Centre National de la Recherche Scientifique · Sorbonne Université · LIP6

   Affiliation as printed

   LIP6 , CNRS , Sorbonne Université , Paris , France

6. Manuel López‐Ibáñez
7. Heike Trautmann

   Paderborn University · University of Twente

   Affiliation as printed

   Paderborn University , Paderborn , Germany

   University of Twente , Enschede , The Netherlands

8. Thomas Bäck