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

TL;DR

This paper introduces MO-IOHinspector, a Python tool integrated into IOHprofiler, enabling anytime benchmarking of multi-objective algorithms with flexible analysis options, enhancing understanding of algorithm performance over time.

Contribution

It presents a novel software tool that applies unbounded archiving principles for flexible, anytime benchmarking of multi-objective algorithms within the IOHprofiler framework.

Findings

Supports changing indicators during analysis

Separates experimental design from analysis decisions

Enhances insights into algorithm performance over time

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.