Abstract
In the present paper, an improved Surrogate-Assisted Evolutionary Algorithm is proposed. It combines the Differential Evolution algorithm with a quadratic surrogate approximation and a proper infill sampling strategy to choose appropriate sample points. The selection of the new candidate points is arranged to enhance both the local accuracy and the global optimum search. A comparison between performances of different evolutionary algorithms is carried out by searching the global minimum of two benchmark functions, by solving a dynamic identification problem of a three floor frame and by calibrating the non-linear stress-crack opening relation for Fibre-Reinforced Concrete specimens starting from experimental data.
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