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 asmembers cross-sectional areas, the height between two layers, structure meshing in twodirections and the topology of the structure should be considered. in this study, forsimultaneous optimization of size, shape and topology of double-layer grids, a geneticalgorithm is employed and is modied based on the fuzzy inference system. first, toeciently search in design space at each stage, some solutions are generated in theneighborhood of the best sample, which enhances searching operation in the neighborhoodof the optimum solution. then, in order to achieve the possible solutions, penalties forviolation of constraints, and the number of violated constraints are considered in orderto choose the next generation. the value of objective function and the values of geneticalgorithm parameters have a great eect on the result of the algorithm. for adaptivesetting of these parameters, the fuzzy inference system is employed. the eciency of theseimprovements has been conrmed by presenting some examples of truss structures andcomparison with the other algorithms.