Performance-based multi-objective optimal design of steel frame structures: Nonlinear dynamic procedure

The main problem in performance-based structures is the extremely highcomputational demand of time-history analyses. in this paper, an ecient frameworkis developed for solving the performance-based multi-objective optimal design problemconsidering the initial cost and the seismic damage cost of steel moment-frame structures.the non-dominated sorting genetic algorithm (nsga-ii) is employed as the optimizationalgorithm to search the pareto optimal solutions. for improving the time eciency ofthe solution algorithm, the generalized regression neural network (grnn) is utilizedas the meta-model for tness approximation, and a specic evolution control scheme isdeveloped. in this scheme, in order to determine which individuals should be evaluatedusing the original tness function and which by the meta-model, the fuzzy c-mean (fcm)clustering algorithm is used to choose the competent individuals rather than choosingthe individuals randomly. moreover, the computational burden of time history analysesis decreased through a particular wavelet analysis procedure. the constraints of theoptimization problem are considered in accordance with the fema codes. the resultsobtained from numerical application of the proposed framework demonstrate its capabilitiesin solving the present multi-objective optimization problem.