Abstract Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because of their strong capabilities in picking the optimal features and removing redundant and irrelevant features.Artificial Ecosystem-based Optimization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature.Dwarf Mongoose Optimization Algorithm (DMOA) is a jilungin dreaming tea recent MH algorithm showing a high exploitation capability.This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO and DMOA to develop an efficient FS algorithm with a better equilibrium between exploration and exploitation.The performance of the AEO-DMOA is investigated on seven datasets from different domains and campicon.com a collection of twenty-eight global optimization functions, eighteen CEC2017, and ten CEC2019 benchmark functions.
Comparative study and statistical analysis demonstrate that AEO-DMOA gives competitive results and is statistically significant compared to other popular MH approaches.The benchmark function results also indicate enhanced performance in high-dimensional search space.