The scaling factor (F) in the mutation operation and the crossover rate (CR) in the crossover operation are considerably critical in assisting differential evolution (DE) to attain good optimization performance. As a result, DE is very sensitive to these two parameters. To address this predicament, many adaptive parameter control methods have been proposed for these two parameters. However, there are no comprehensive comparisons among these adaptive parameter methods. To make up for this defect, this paper mainly investigates the effectiveness of six widely utilized adaptive strategies, namely the ones in JADE, IDE, jDE, SinDE, FDSADE, and RDE. For fairness, this paper selects the binomial crossover and the mutation “DE/current-to-pbest/1” to accompany the six adaptive parameter strategies. Experimental results on the commonly adopted CEC2014 benchmark suite have demonstrated that the adaptive parameter control methods in IDE and JADE help DE achieve the best overall performance. With these investigations, it is envisaged that this paper provides a fundamental guideline for new learners and those looking for an appropriate adaptive parameter technique for their newly created DE algorithms.