Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification

Nature-inspired metaheuristic algorithms have gained great attention over the last decade due to their potential for finding optimal solutions different optimization problems. In this study, a based on dwarf mongoose algorithm (DMOA) is presented parameter estimation of an autoregressive exogenous (ARX) model. DMOA, set candidate were stochastically created and improved using only one tuning parameter. The performance DMOA ARX identification was deeply investigated in terms its convergence speed, accuracy, robustness reliability. Furthermore, comparative analyses with other recent state-of-the-art metaheuristics Aquila Optimizer, Sine Cosine Algorithm, Arithmetic Optimization Algorithm Reptile Search algorithm—using nonparametric Kruskal–Wallis test—endorsed consistent, accurate proposed identification.