A NEW METHOD FOR DESIGNING THE OPTIMUM GEODETIC NETWORKS USING GENETIC ALGORITHMS

Classically, a geodetic network can be optimized using the trial and error method or analytical methods such as linear programming, quadratic programming, or some optimization problems can be solved by generalized or iterative generalized inverses. Optimization problems may also be solved by intelligent optimization techniques such as genetic algorithms, simulated annealing and particle swarm optimization algorithm. This work describes the uses of genetic algorithms to find out the optimum location of stations of geodetic networks First Order Design as well as the optimum weight of observations Second Order Design simultaneously using different objective functions and constraint handling methods. 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 genetic algorithms and shows its efficiency using a simulated example.