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Recent advances in science, engineering, and technology have created massive datasets. As a result, machine learning and data mining techniques cannot perform well on these huge datasets because they contain redundant, noisy, and irrelevant features. The purpose of feature selection is to reduce the dimensionality of datasets by selecting the most relevant attributes while simultaneously increasing classification accuracy. The application of meta-heuristic optimization techniques has become increasingly popular for feature selection in recent years due to their ability to overcome the limitations of traditional optimization methods. This paper presents a binary version of the Manta Ray Foraging Optimizer (MRFO), an alternative optimization algorithm. Besides reducing costs and reducing calculation time, we also incorporated Spearman's correlation coefficient into the proposed method, which we called Correlation Based Binary Manta Ray Foraging (CBBMRF). It eliminates highly positive correlation features at the beginning of the calculation, avoiding additional calculations and leading to faster subset selection. A comparison is made between the presented algorithms and five state-of-the-art meta-heuristics using 10 standard UCI datasets. As a result, the proposed algorithms demonstrate superior performance when solving feature selection problems.