Research On Multi-Objective Optimization of Complex Systems Based on A Feedback-Driven Structure-Preserving Optimization Architecture and An Improved NSGA-II Algorithm

The complex systems typically exhibit multiple objectives, multiple constraints, and dynamic structural characteristics, which make their multi-objective optimization and structural adaptability problems highly challenging. To address these challenges, this paper proposes a Structure-Preserving Optimization Architecture (SPOA) to systematically describe the nonlinear interaction process between input stimuli, constraint boundaries, and response structures. In terms of algorithm implementation, SPOA employs an improved NSGA-II algorithm to approximate the Pareto optimal solution set in a non-convex and constrained high-dimensional decision space. Finally, simulation experiments are conducted using a tourism system as an example scenario, validating the effectiveness of the proposed model. The results show that the model demonstrates excellent performance in terms of convergence stability, parameter transferability, and robustness to structural perturbations, and exhibits broad applicability in complex systems.