用模糊遗传算法优化半刚性钢构，兼顾轻量化与性能
Bilinear Fuzzy Genetic Algorithm and Its Application on the Optimum Design of Steel Structures with Semi-rigid Connections
- 融合模糊逻辑与遗传算法，处理结构非线性不确定性
- 相比传统方法，更快找到满足约束的优质设计方案
- 适合复杂钢结构设计，尤其半刚性连接场景
本文提出一种改进的双线性模糊遗传算法（BFGA），用于半刚性连接钢结构的设计优化。半刚性连接介于完全刚接与完全铰接之间，其非线性行为使设计极具挑战。BFGA结合模糊逻辑与遗传算法优势，有效应对结构设计中的复杂性与不确定性。相较于标准遗传算法，BFGA在合理时间内生成高质量解。通过实例验证，该方法在考虑结构重量与性能指标下，能有效求得满足所有设计要求与约束的最优方案，为具有非线性行为的复杂结构优化提供可靠解决方案。
原文摘要 · Abstract (English)
An improved bilinear fuzzy genetic algorithm (BFGA) is introduced in this chapter for the design optimization of steel structures with semi-rigid connections. Semi-rigid connections provide a compromise between the stiffness of fully rigid connections and the flexibility of fully pinned connections. However, designing such structures is challenging due to the nonlinear behavior of semi-rigid connections. The BFGA is a robust optimization method that combines the strengths of fuzzy logic and genetic algorithm to handle the complexity and uncertainties of structural design problems. The BFGA, compared to standard GA, demonstrated to generate high-quality solutions in a reasonable time. The application of the BFGA is demonstrated through the optimization of steel structures with semirigid connections, considering the weight and performance criteria. The results show that the proposed BFGA is capable of finding optimal designs that satisfy all the design requirements and constraints. The proposed approach provides a promising solution for the optimization of complex structures with nonlinear behavior.
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