Al‑Shourbaji, IbrahimKachare, PramodFadlelseed, SajidJabbari, AbdohHussien, Abdelazim G.Al‑Saqqar, FaisalAbualigah, LaithAlameen, Abdalla2025-10-162023https://dspace.nu.edu.sd/handle/nusu/132Meta-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 Opti mization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature. Dwarf Mongoose Optimization Algorithm (DMOA) is a 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 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 competi tive results and is statistically significant compared to other popular MH approaches. The benchmark function results also indicate enhanced performance in high-dimensional search space.enFeature selection · Machine learning · Metaheuristic algorithms · Artificial ecosystem-based optimization · Dwarf mongoose optimizationArticle