Layout optimization of double-layer grids using modified genetic algorithm based on fuzzy inference system

In weight optimizitaion of double-layer grids, various parameters, such as members cross-sectional areas, the height between two layers, structure meshing in two directions and the topology of the structure should be considered. In this study, for simultaneous optimization of size, shape and topology of double-layer grids, a genetic algorithm is employed and is modied based on the fuzzy inference system. First, to eciently search in design space at each stage, some solutions are generated in the neighborhood of the best sample, which enhances searching operation in the neighborhood of the optimum solution. Then, in order to achieve the possible solutions, penalties for violation of constraints, and the number of violated constraints are considered in order to choose the next generation. The value of objective function and the values of genetic algorithm parameters have a great eect on the result of the algorithm. For adaptive setting of these parameters, the fuzzy inference system is employed. The eciency of these improvements has been conrmed by presenting some examples of truss structures and comparison with the other algorithms.