Design and Application of Improved Genetic Algorithm for Optimizing the Location of Computer Network Nodes

Abstract

The rapid development of computer technology has made network stability and node positioning accuracy important challenges in optimizing computer network design. This study proposes an optimization method based on the Improved Genetic Algorithm (IGA) to improve the positioning accuracy and stability of network nodes. Firstly, by combining the characteristics of the centroid algorithm and the Approximate Point in Triangulation Test (APIT) algorithm, preliminary optimization of node positions is carried out. Subsequently, an IGA is utilized for further optimization, dynamically adjusting the crossover probability and mutation probability to balance global and local search capabilities and avoid the algorithm falling into local optima. The experimental results showed that IGA achieved significant performance improvement in node localization. Compared with the centroid algorithm, the maximum error of IGA has been reduced by 19% and the overall average error has been reduced by 8.8%. Compared with APIT, IGA has reduced the maximum error by 7% and the overall average error by 3.8%. Regarding fitness values, IGA exhibited faster convergence speed, achieving optimal results with only 75 iterations, surpassing traditional genetic algorithms and APIT algorithms. The node coverage rate reached 98.6%, far higher than the 85.3% of the centroid algorithm and 90.5% of the APIT algorithm. These results demonstrate that IGA has higher accuracy, stability, and computational efficiency in complex network environments, providing an efficient and reliable solution for optimizing the design of computer network nodes.