International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Hybrid Type-2 Fuzzy NSGA-II Framework for Robust Optimization of Nonlinear Dynamical Systems Under Deep Uncertainty

Author(s) Manoj Kumar Singh Tomar, Dr. Uma Shankar
Country India
Abstract In the presence of deep uncertainty and multi-objective conflicts, traditional optimization approaches often fail to provide robust solutions for complex nonlinear dynamical systems. This study proposes a novel hybrid framework integrating Interval Type-2 Fuzzy Set Theory with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to achieve robust multi-objective optimization under epistemic uncertainty. The nonlinear system is modeled using Type-2 fuzzy logic to represent imprecise system parameters and constraints, while the modified NSGA-II performs fuzzy-dominant evolutionary search to extract Pareto-optimal solutions. The framework is validated on benchmark nonlinear systems with multiple conflicting objectives such as energy efficiency, stability, and tracking performance. Extensive simulations reveal that the proposed approach significantly enhances solution diversity, convergence, and robustness compared to classical NSGA-II. A sensitivity analysis using α-cuts and Monte Carlo simulations confirms improved tolerance to parameter perturbations. The results suggest that the hybrid fuzzy-NSGA-II approach is a reliable and scalable method for real-world optimization in control systems, robotics, and smart infrastructure domains.
Keywords Type-2 Fuzzy Logic, NSGA-II, Robust Optimization, Nonlinear Dynamical Systems, Multi-objective Evolutionary Algorithms, Deep Uncertainty, Pareto Front, Sensitivity Analysis, Fuzzy Dominance, α-Cut Simulation
Field Engineering
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-15
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.60949
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E-ISSN 2582-2160
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