h2o, different stopping metric leads to different optimal for hyperparameters
First of all, you are using different metrics to determine how well you are doing, that means it's not weird that different metrics find different hyperparameter settings that work better. Second of all, some hyperparameters might not matter for the problem you are solving, which means all the signal you are getting from those hyperparameters is noise. Third of all, most of the machine learning algorithms are stochastic, meaning there is randomness involved in training them and sometimes in evaluating them, this means even starting the same grid or random search could lead to different hyperparameters. That said the probability of that is only high if the real performances are close to each other.