Neural Network Approximation Method for Structural Optimization Using Genetic Algorithms
Date Issued
2007
Date
2007
Author(s)
Fan, Hsu
Abstract
This paper studies the neural network approximation for structural optimization using genetic algorithms. Firstly, the fitness values of the initial population are calculated using the original fitness function. Then a neural network is built with these fitness values as an approximate model for the following evolutions. An evolution control method, combined with both generation-based evolution control and individual-based evolution control, is developed and utilized in the optimization process. In the controlled evolution, exact fitness values of whole population are evaluated only at a few finite numbers of generations. For other generations, approximate fitness values are evaluated to save the computational time. For generations using approximate fitness values, individual-based evolution control is applied to prevent the overestimated individual from being regarded as the best individual in the generation. Next, an integrated program is developed by combing genetic algorithms, finite element analysis program, computer aided design program, neural network, and evolution control. Several functional and simple structural optimization examples are examined with this integrated program. Finally, optimum designs of two complex structural problems are carried out in the developed program. From the results, it shows that the approximate method and evolution control method developed in this thesis are able to reduce the time-consuming exact fitness evaluations with the same quality of convergent solution.
Subjects
Genetic algorithms
Structural optimization
Neural network approximation method
Finite element analysis
Evolution control
Type
thesis
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