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@article{198466,
        author = {Gauravkumar Bharatbhai Mehta and Prof.Dr. Vineet Kumar Goel and Prof.Dr. Dinesh Kumar},
        title = {Comparative Analysis of Direct Search Domain and NSGA-II Algorithms for Multi-Objective Conceptual Design of Firefighting Aircraft},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {10359-10384},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198466},
        abstract = {The design of special-purpose aircraft in its early development requires robust and flexible optimization approaches with the capability to balance divergent performance goals and intricate multidisciplinary interactions. In the present work, we introduce a multi-objective evolutionary framework for conceptual aircraft design based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Contrary to the gradient-based and Direct Search Domain (DSD) methods, which can converge prematurely to locally optimal solutions, NSGA-II can search the design space in a broader manner and build a diverse Pareto front methodically without the need for gradient information or predefined search domains. This aspect is especially advantageous in conceptual aircraft design, as the combination of aerodynamics, structure, and operations is commonly nonlinear and highly coupled. The method combines all analysis models for weight estimation, aerodynamic performance, and take-off evaluation with wing geometry as a dominating design variable. The optimization problem is proposed to optimize payload capability, take-off distance, and aerodynamic efficiency in line with structural and other operational feasibility constraints. Instead of looking for a single “best” option, the framework establishes a set of valid choices, and the designer can compare trade-offs between competing goals, which are subject to missions. The optimization procedure effectively discovers the layout of the design space and demonstrates how changes to payload capacity are generally associated with changes to take-off distance, whereas aerodynamic efficiency may conflict with structural considerations. Notably, the Pareto front produced by NSGA-II represents a range of feasible solutions that can be used for various operational scenarios so that decisions are more flexible than the ones based on DSD. These results indicate that NSGA-II is a scalable and adaptable optimization strategy for conceptual aircraft design, such as when full system coupling and sophisticated trade-offs are to be assessed. Future work will further enhance the proposed optimization method by combining high-fidelity CFD and FEM simulations; hence, further hybrid optimization strategies will be developed for NSGA-II with gradient-based refinement to enhance convergence and engineering validation.},
        keywords = {Multi-Objective Optimization; Conceptual Aircraft Design; Non-Dominated Sorting Genetic Algorithm II (NSGA-II); Direct Search Domain (DSD); Firefighting Aircraft; Pareto-Front Analysis; Aero-Structural Coupling; Multi-Disciplinary Design Optimization (MDO); Evolutionary Computation; Design-Space Exploration},
        month = {April},
        }