Polynomial approximation method for structural optimization using genetic algorithms
Date Issued
2006
Date
2006
Author(s)
Peng, Jian-Gou
DOI
zh-TW
Abstract
This thesis studies the polynomial approximation method for structural optimization using genetic algorithms. Firstly, genetic algorithm is used to solve the structural optimization problem. Then a second-order polynomial function is used to build the approximate model of the fitness function. This approximate model is used in evaluating approximate fitness values, instead of the original fitness values. In the optimization process, a generation-based evolution control method is developed. Under the controlled evolution, fitnesses of population individuals are evaluated exactly only for some specific generations. In the following some generations, individual fitnesses are estimated by the approximate model. When the approximate fittnesses are evaluated, some individuals might be overestimated by the approximate model, and an individual-based evolution control is applied to reevaluate exact fitnesses of these individuals. It prevents the overestimated individual to be regarded as the best individual within the generation. Finally, an integrated program combining finite element analysis software ANSYS, polynomial approximate model, evolution control, and genetic algorithm is developed. Several functional and structural optimization examples are test by this integrated program. From the results, it shows that the approximation method proposed by this thesis is able to achieve the same quality of convergent solution with fewer numbers of function evaluations.
Subjects
多項式近似法
遺傳演算法
結構最佳化
演化控制
有限元素分析
Polynomial approximation method
Genetic algorithms
Structural optimization
Evolution control
Finite element analysis
Type
thesis
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