A Comprehensive Review of Heuristic Approaches in Modelling, Analysis, and Identification of Nonlinear Systems

A Comprehensive Review of Heuristic Approaches in Modelling, Analysis, and Identification of Nonlinear Systems

Nonlinear systems, commonly found in scientific research and engineering applications, present significant challenges due to their intricate and complex behavior, investigating the properties of non-linear systems in different scenarios, spanning over differ¬ent types of nonlinear continuous and discrete time Multi-Input Multi-Output as well Single Input Single Output control systems with the help of modern computational heuristics. The review seeks to elucidate the distinguishing characteristics of these systems as well as the role, impact, and significance of the stochastic optimization computing paradigm based on evolutionary and swarm¬ing heuristic intelligence. In addition, this text describes how randomness significantly impacts the dynamics of such deterministic and stochastic nonlinear systems. Mathematical modeling approaches, which are rooted in the methodological foundations of or¬dinary differential equations and input-output models from an innovation studies perspective, may offer a conceptual framework to integrate these complex dynamics of nonlinear systems. This study comprehensively reviews the utilization of computational intelligence techniques, including genetic algorithms, particle swarm optimization, firefly algorithm, ant-colony optimization, sim-ulated-annealing, tabu search optimizer, differential evolution heuristics, artificial-bee colony optimization, and Cuckoo Search for parameter estimation of nonlinear systems based on Hammerstein structure.