Facial expression recognition (FER) in computer vision is a very daunting computational task because of high-intra class variations. Researchers have developed methods for extracting features from images that apply conventional and deep learning approaches. When conventional approaches are used in FER system, the size of extracted features is very large, which may adversely affect the performance of the classifier. Therefore, in the process of FER, feature selection is an essential phase. We propose an improvement on competitive binary gray wolf optimizer in discrete search space named as Improved MOCBGWO, and it is utilized to perform feature selection within wrapper-based setup. Next, we applied the SVM and K nearest neighbors classifiers to selected features for investigating the performance of the proposed system through two publicly available standard datasets: CK+ and JAFFE. We compared the performance of the proposed improvement with binary gray wolf optimizer, binary moth flame optimizer, binary particle swarm optimizer, and competitive binary GWO for evaluating its efficacy. The experimental results reveal that our proposed improvement boosts the recognition accuracy along with reduction in the size of feature vector in a superior way as compared to other methods.