Research on the Implementation and Performance Optimization of Supply Chain Finance System based on NSGA-III Parallel Algorithm

Traditional optimization algorithms struggle with diversity and convergence speed, limiting their effectiveness in complex problem-solving. The combined computation and time costs of optimization and simulation further impede the ability to address intricate engineering structures. This paper enhances the NSGA-III algorithm and establishes a parallel joint simulation optimization platform aimed at high-dimensional, multi-objective challenges in engineering. An improved NSGA-III algorithm and a joint simulation optimization method are proposed to optimize car body structures. A comparative analysis of NSGA-II and NSGA-III reveals the enhanced algorithm’s computational advantages. The joint platform facilitates parallel solver calculations, leading to nearly doubled efficiency in optimizing a car’s body side wall compared to NSGA-II. The improved NSGA-III not only provides better optimization solutions for car body design but also addresses challenges in integrating NSGA technology with supply chain finance by introducing a new service model. By refining mixed operators, optimizing variation rates, and preserving population diversity, the improved algorithm significantly enhances performance in high-dimensional optimization, resulting in increased bending and torsion stiffness, reduced maximum stress, and minimized side wall mass. Performance analysis shows superior outcomes in 69% of test problems.