Application of Path Optimization Algorithm and Simulation Analysis of Densifying Control Network

Abstract

Densifying control network is a primary task of the geodesic squad. In the actual operation, the geodetic task is required to be completed within the shortest time in the shortest distance. By optimizing the geodesic path, the speed of densifying control network can be increased to improve the work efficiency. In this paper, aiming at the path planning for densifying control network, the path optimization is analyzed with the model of traveling salesman problem. The genetic algorithm and the ant colony algorithm are used to simulate the path optimization problem. The two algorithms are compared and analyzed. The results show that through optimization, the total distance can be reduced to 39% of the random path, and thus this approach can be time-saving and of great practical value.