An evolutionary optimization-based approach for simulation of endurance time load functions

Abstract

A novel optimization method based on Imperialist Competitive Algorithm (ICA) for simulating endurance time (ET) excitations was proposed. The ET excitations are monotonically intensifying acceleration time histories that are used as dynamic loading. Simulation of ET excitations by using evolutionary algorithms has been challenging due to the presence of a large number of decision variables that are highly correlated due to the dynamic nature of the problem. Optimal parameter values of the ICA algorithm for simulating ETEFs were evaluated and were used to simulate ET excitations. In order to increase the capability of the ICA and provide further search in the optimization space, this algorithm was combined with simulated annealing (SA). The new excitation results were compared with the current practice for simulation of ET excitations. It was shown that the proposed ICA-SA method leads to more accurate ET excitations than the classical optimization methods.