Using fuzzy genetic algorithm for the weight optimization of steel frames with semi-rigid connections

In this paper, combination of genetic algorithm (GA) and fuzzy logic is used for weight optimization of steel frames with rigid or semi-rigid connections. In the genetic algorithm, uniform crossover operator is employed and also, binary coding is used to achieve better convergence. Behavior of steel frames depends highly on beam to column connections. Here, beam to column connections are assumed to be semi-rigid or rigid. Linear analysis and design has been used for steel frame structures. Matlab program has been utilized for the process of optimization in combination with OpenSees software for frame analysis. Beams and columns sections are selected from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Displacement and stress constraints are imposed on the frame. Frye and Morris polynomial model is used for semi-rigid connection. Also, the proposed algorithm considers a fitness function using appropriate balancing factors which leads to a faster convergence. Three different design examples with various types of connections are presented to demonstrate the efficiency and robustness of the proposed approach. The results show that the fuzzy genetic algorithm results in lighter structures consuming less computation time compared to simple genetic algorithm.