Selection of features is an effective method for minimizing the amount of data features in order to improve machine learning classification performance. Choosing a set of attributes is a high-level procedure for selecting a collection of relevant features. To boost the classifier's performance, use a dimensional dataset. We outline a typical feature selection problem in this work in order to minimize the amount of role and responsibilities while improving accuracy. Different classification dataset from the Machine learning repository have been used to test SMA with GWO as a feature selection strategy. The feature selections for UCI repository datasets include Bat optimization, Cuckoo search optimization, Slime mould optimization, Whale optimization, Particle swarm optimization, and Grey wolf optimization.