Multi-Strategy Enhanced NSGA-III Algorithm and Its Application in the Variable-Thickness Design of Morphing Leading Edges

To address the strongly coupled and highly nonlinear optimization problems arising from the increasing system complexity, optimization objectives, and variable dimensions in practical engineering applications, this paper proposes a multi-strategy enhanced NSGA-III algorithm (MSNSGA-III) by introducing K-means clustering, an adaptive hybrid operator, and an assistant evolutionary population strategy on the basis of the NSGA-III algorithm. This algorithm overcomes the performance limitations of the original algorithm in large-scale search with multiple variables. By employing the DTLZ test functions with different variable dimensions and conducting comparisons with six other representative algorithms, the proposed algorithm is proven to have strong competitiveness in terms of diversity and convergence speed. To reflect the superiority of the algorithm in practical applications, this paper establishes a variable-thickness optimization model for the morphing leading edge. By adopting the spline curve-based optimization variable control strategy and the MSNSGA-III algorithm, the optimal thickness distribution of the leading edge skin is obtained. The results show that, compared with the leading edge with a fixed skin thickness of 1.5 mm, the optimized variable thickness skin leading edge achieves 43.6% improvement in shape maintaining accuracy, 40.9% improvement in deformation accuracy, and 17.5% reduction in driving force.