  Differential evolution (DE) generally requires parameter control methods
(PCMs) for the scale factor and crossover rate. Although a better understanding
of PCMs provides a useful clue to designing an efficient DE, their
effectiveness is poorly understood in mixed-integer black-box optimization. In
this context, this paper benchmarks PCMs in DE on the mixed-integer black-box
optimization benchmarking function (bbob-mixint) suite in a component-wise
manner. First, we demonstrate that the best PCM significantly depends on the
combination of the mutation strategy and repair method. Although the PCM of
SHADE is state-of-the-art for numerical black-box optimization, our results
show its poor performance for mixed-integer black-box optimization. In
contrast, our results show that some simple PCMs (e.g., the PCM of CoDE)
perform the best in most cases. Then, we demonstrate that a DE with a suitable
PCM performs significantly better than CMA-ES with integer handling for larger
budgets of function evaluations. Finally, we show how the adaptation in the PCM
of SHADE fails.
