Artificial Ecosystem-Based Optimization with Dwarf Mongoose Optimization for Feature Selection and Global Optimization Problems

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