Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Introduction to Grey system theory
The Journal of Grey System
Damage detection using impulse response
Nonlinear Analysis: Theory, Methods & Applications
Proceedings of the 3rd International Conference on Genetic Algorithms
Advances in Engineering Software
When a genetic algorithm outperforms hill-climbing
Theoretical Computer Science
Computers and Industrial Engineering - Special issue: Selected papers from the 31st international conference on computers & industrial engineering
A hybrid real-parameter genetic algorithm for function optimization
Advanced Engineering Informatics
Grey relational grade in local support vector regression for financial time series prediction
Expert Systems with Applications: An International Journal
Improvement of e-government service process via a grey relation agent mechanism
Expert Systems with Applications: An International Journal
Applying fuzzy grey modification model on inflow forecasting
Engineering Applications of Artificial Intelligence
Mathematical and Computer Modelling: An International Journal
Assessment for soil improvement benefit of land rehabilitation in dump areas
Mathematical and Computer Modelling: An International Journal
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For damage detection, a hybrid technique consisting of two strategies is proposed. First, grey relation analysis is used to exclude the impossible damage locations such that the number of design variables can be reduced. By using two simple rules proposed in this work, all actual damage locations are included as possible damage locations and the number of design variables is effectively reduced. Secondly, a real-parameter genetic algorithm is combined with simulated annealing and adaptive mechanisms for finding the actual damages. The proposed hybrid algorithm uses only vertical nodal displacements of static responses. In addition to the ideal error-free case in which there is no difference between the experimental data and the theoretical data, the case with 5% error, in which the experimental data is simulated by adding 5% error maximum to the corresponding theoretical data, is considered for measuring error and modeling error. From the results, it is demonstrated that the proposed technique is efficient in damage identification. For the error-free case, the predicted results agree with the true solutions exactly. For the case with 5% error, the prediction obtained is also reasonable.