2015
DOI: 10.1111/mice.12124
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Coupling Response Surface and Differential Evolution for Parameter Identification Problems

Abstract: In the present article, a new surrogate-assisted evolutionary algorithm for dynamic identification problems with unknown parameters is presented. It is based on the combination of the response surface (RS) approach (the surrogate model) with differential evolution algorithm for global search. Differential evolution (DE) is an evolutionary algorithm where N different vectors collecting the parameters of the system are chosen randomly or by adding weighted differences between vectors obtained from two population… Show more

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Cited by 58 publications
(34 citation statements)
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“…They aim at evaluating those individuals that potentially have a good prediction of the objective function value. The introduction of a second-order surrogate in the Differential Evolution (DE) algorithm is proposed in [18].…”
Section: The De-s Algorithmmentioning
confidence: 99%
“…They aim at evaluating those individuals that potentially have a good prediction of the objective function value. The introduction of a second-order surrogate in the Differential Evolution (DE) algorithm is proposed in [18].…”
Section: The De-s Algorithmmentioning
confidence: 99%
“…Recently, they received considerably increasing interest in reducing the computational effort in optimization problems, mainly when the evaluation of the objective function is highly time consuming [14]. In this work, the socalled DE-Q algorithm is adopted [9,10]; it combines the robustness of the DE algorithm with the computational efficiency due to a second-order surrogate approximation of the objective function. The optimal parameters are obtained through the minimization of an objective function defined as the relative error between numerical and experimental modal frequencies and mode shapes.…”
Section: Finite Element Model and Model Updatingmentioning
confidence: 99%
“…In these models, vertical dynamic actions depend on the pacing frequency, the walking (or running) speed, the step length, the number of people involved and the synchronous action modelling. To apply the first approach, a finite element model is developed and calibrated so that the numerical dynamic predictions agree with the experimental modal properties [9,10]. Both the updated and the non-calibrated models are used to evaluate the dynamic response of the footbridge when subjected to pedestrian loads.…”
mentioning
confidence: 99%
“…Apart from real‐time experiments, many algorithms have been developed and tested for applications such as SHM (Hampshire and Adeli, ; Park et al., ; Park et al., ; Amezquita‐Sanchez and Adeli, , ); identification of the structural parameters (Sun et al., ; Yuen and Mu, ; Vincenzi and Savoia; ; Sirca and Adeli, ; Perez‐Ramirez et al., ); and for modal updating (Oh et al., ; Sun and Betti, ; Boscato et al., ; Shabbir and Omenzetter, ). These algorithms assist assessing the health of the structure and also the parametric changes associated with the deterioration in health, making the infrastructure intelligent (Adeli and Jiang, ; Qarib and Adeli, , ).…”
Section: Introductionmentioning
confidence: 99%