A data scientist is performing hyperparameter optimization using an iterative optimization algorithm, with each unique set of hyperparameters trained on a separate compute node. Despite conducting eight evaluations across eight nodes, they notice no consistent improvement in model accuracy. What changes could potentially enhance the model's accuracy during this tuning process?