Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

Abstract

In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

“…Others include a simplified slime mould algorithm [ 42 ], i.e., a modified version of the slime mould heuristic, with an introduction of enhanced adaptive oscillation for better exploration capability during the early search phase, with application to wireless sensor network optimization problems; a code pathfinder algorithm [ 43 ], i.e., a discrete complex code pathfinder heuristic for an efficient solution to the optimization problem of wind farm layout through an improved exploration capability; and a firefighting strategy based marine predators approach [ 44 ], i.e., an improved variant of marine predator heuristic through an introduction of opposition-based learning for more uniform initial population and adaptive weight factor for creating balance between exploration/exploitation capabilities to effectively handle the forest fire rescue issues. More of these intelligent computer algorithms include a chaotic grey wolf optimizer [ 45 ], i.e., a modified grey wolf optimizer by incorporating the concepts of chaotic maps and adaptive convergence factor for robust and accurate parameter estimation of control autoregressive systems; a subtraction average based optimizer [ 46 ], i.e., an optimization approach inspired by the subtraction average of searchers agents for the position updates of the particles in the search space; and an enhanced dragonfly heuristic [ 47 ], i.e., enhanced version of dragonfly algorithm with an improved mechanism of global search and a local search for the four color map problem. There are also two others: a non-dominated sorting genetic algorithm [ 48 ], i.e., a modified variant of genetic algorithm with special congestion approach and adaptive crossover scheme to effectively solve multi objective and multi modal optimization problems, and, lastly, a green anaconda optimizer [ 49 ], i.e., an optimization heuristic that mimics the natural behavior of the green anacondas to solve various benchmark optimization challenges.…”

Mother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimization

“…Researchers have developed numerous metaheuristic algorithms to solve optimization problems more effectively. These methods have found applications in various fields such as dynamic scheduling 10 , construction of multi-classifier systems 11 , 12 , clustering approach 13 – 15 , IoT-based complex problems 16 , 17 , parameter estimation 18 – 20 , modeling of nonlinear processes 21 , 22 , energy carriers and electrical engineering 23 – 27 , wave solutions 28 – 31 , and higher-order nonlinear dynamical equation 32 .…”

Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems

“…Mostly, metaheuristic algorithms are used to solve real-world engineering problems. For instance, non-linear MPA is used for Hammerstein autoregressive exogenous systems 80 , chaotic GWO is used in identification of control autoregressive model 81 , MPA with key term separation technique used for nonlinear Hammerstein system identification 82 , Dwarf mongoose algorithm for identification of autoregressive exogenous model 83 , Aquila optimizer for control autoregressive systems identification 84 , etc. Though the algorithms are used for real-world applications, still most of the algorithms struggle to handle constraint optimization problems.…”