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Abstract
Second Order Design of Geodetic Networks Using Augmented Lagrangian Genetic Algorithm
H. Sahabi
Iran
Email: Hossein.sahabi@gmail.com
This paper suggests a new method for designing an optimum observational plan of geodetic networks (SOD problem) using genetic algorithm. Optimization using genetic algorithms needs neither linearization nor differentiation of the object function or the constraint equations. As genetic algorithm uses simple mathematical computations it is easy to implement. In constraint problems one can use Lagrangian multiplier to redefine the problems as unconstraint ones. The network is designed in a way that the variance-covariance of the estimated parameters optimally approximates the criterion matrix. The paper reviews different components of the augmented Lagrangian genetic algorithm and shows its efficiency using a numerical example.
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