  Benchmarking heuristic algorithms is vital to understand under which
conditions and on what kind of problems certain algorithms perform well. In
most current research into heuristic optimization algorithms, only a very
limited number of scenarios, algorithm configurations and hyper-parameter
settings are explored, leading to incomplete and often biased insights and
results. This paper presents a novel approach we call explainable benchmarking.
Introducing the IOH-Xplainer software framework, for analyzing and
understanding the performance of various optimization algorithms and the impact
of their different components and hyper-parameters. We showcase the framework
in the context of two modular optimization frameworks. Through this framework,
we examine the impact of different algorithmic components and configurations,
offering insights into their performance across diverse scenarios. We provide a
systematic method for evaluating and interpreting the behaviour and efficiency
of iterative optimization heuristics in a more transparent and comprehensible
manner, allowing for better benchmarking and algorithm design.
