Augmented Lagrangian Genetic Algorithm for Structural Optimization
Publication: Journal of Aerospace Engineering
Volume 7, Issue 1
Abstract
This paper presents a robust hybrid genetic algorithm for optimization of space structures using the augmented Lagrangian method. An attractive characteristic of genetic algorithm is that there is no line search and the problem of computation of derivatives of the objective function and constraints is avoided. This feature of genetic algorithms is maintained in the hybrid genetic algorithm presented in this paper. Compared with the penalty function‐based genetic algorithm, only a few additional simple function evaluations are needed in the new algorithm. Furthermore, the trial and error approach for the starting penalty function coefficient and the process of arbitrary adjustments are avoided. There is no need to perform extensive numerical experiments to find a suitable value for the penalty function coefficient for each type or class of optimization problem. The algorithm is general and can be applied to a broad class of optimization problems.
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Information & Authors
Information
Published In
Journal of Aerospace Engineering
Volume 7 • Issue 1 • January 1994
Pages: 104 - 118
Copyright
Copyright © 1994 American Society of Civil Engineers.
History
Received: May 28, 1992
Published online: Jan 1, 1994
Published in print: Jan 1994
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