Excogitating marine predators algorithm based on random opposition‐based learning for feature selection

Summary Obtaining precise information from a high‐dimensional dataset is one of the most difficult tasks as datasets contain more features and fewer samples. The high‐dimensionality of the dataset reduces predictive capability and increases the computational complexity of the analytical model. The widespread employment of meta‐heuristic methods to handle the challenge of high‐dimensional datasets has been exceptional in recent years. The marine predators algorithm (MPA) is a recently developed meta‐heuristic algorithm based on the “survival‐of‐the‐fittest” notion. This research critique overcomes the drawbacks of the existing MPA and proposes a feature selection model using random opposition‐based learning (ROBL). The searching for the optimum solution in a single direction of the traditional MPA reduces its performance. The incorporation of ROBL in the MPA enhances its ability to reconnoiter bigger search space. The proposed algorithm generates a new population based on the initial and random opposite population. The performance of ROBL‐MPA is inspected on six high‐dimensional microarray datasets. The results of the proposed ROBL‐MPA are compared to traditional MPA and opposition based MPA (OBL‐MPA). The proposed ROBL‐MPA outperforms traditional MPA based on several benchmark performance analysis tests.