Bilinear Fuzzy Genetic Algorithm and Its Application on the Optimum Design of Steel Structures with Semi-rigid Connections

TL;DR

This paper introduces an improved bilinear fuzzy genetic algorithm (BFGA) for optimizing steel structures with semi-rigid connections, effectively handling nonlinear behavior and uncertainties to find high-quality, optimal designs.

Contribution

The paper presents a novel BFGA method that combines fuzzy logic and genetic algorithms for structural optimization, addressing nonlinearities and uncertainties in semi-rigid connection design.

Findings

BFGA outperforms standard GA in solution quality and speed

BFGA successfully optimizes steel structures with semi-rigid connections

Optimized designs meet all structural and performance constraints

Abstract

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.