DYNAMIC FINITE-ELEMENT MODEL UPDATING USING SIMULATED ANNEALING AND GENETIC ALGORITHMS

Dynamic finite element (FE) model updating may be considered as an opt imisation process. Over the past few years, two powerful new optimisat ion algorithms have been developed independently of each other; namely , the genetic algorithm (GA) and simulated annealing (SA). These algor ithms are both probabilistic search algorithms capable of finding the global minimum amongst many local minima. This paper compares various implementations of the two algorithms for model updating purposes. A n ew variant of simulated annealing is suggested and is found to be the most effective of all the optimisation algorithms considered. This ver sion of simulated annealing is then tested using several objective fun ctions for simulated model updating in the frequency domain. In the se cond part of this paper, both SA and GAs are applied to a practical FE model updating problem using measured data. The new variation of the SA algorithm, termed the blended SA algorithm, performed better than t he traditional GA algorithm. However, the results obtained show a sign ificant dependence on the choice of updating parameters. It was conclu ded that model updating using these optimisation algorithms is a promi sing and viable approach, but the appropriate choice of updating param eters is of paramount importance. (C) 1998 Academic Press Limited.