Enhanced Marine Predators Algorithm for Solving Global Optimization and Feature Selection Problems

Feature selection (FS) is applied to reduce data dimensions while retaining much information. Many optimization methods have been enhance the efficiency of FS algorithms. These approaches processing time and improve accuracy learning models. In this paper, a developed method called MPAO based on marine predators algorithm (MPA) “narrowed exploration” strategy Aquila optimizer (AO) proposed handle FS, global optimization, engineering problems. This modification enhances exploration behavior MPA update explore search space. Therefore, narrowed AO increases searchability MPA, thereby improving its ability obtain optimal or near-optimal results, which effectively helps original overcome local optima issues in problem domain. The performance evaluated solving problems using some evaluation criteria, including maximum value (Max), minimum (Min), standard deviation (Std) fitness function. Furthermore, results are compared meta-heuristic over four Experimental confirm