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Parameter identification problems have emerged due to the increasing demanding of precision in the numerical results obtained by finite element method (FEM) software. High result precision can only be obtained with confident input data and robust numerical techniques. The determination of parameters should always be performed confronting numerical and experimental results leading to the minimum difference between them. However, the success of this task is dependent of the specification of the cost/objective function, defined as the difference between the experimental and the numerical results. Recently, various objective functions have been formulated to assess the errors between the experimental and computed data (Lin et al., 2002 [36]; Cao and Lin, 2008 [14]; among others). The objective functions should be able to efficiently lead the optimisation process. An ideal objective function should have the …