Computer Network Reliability Optimization Method based on Improved Genetic Algorithm

In the context of increasing reliance on computer networks, this study proposes an improved genetic algorithm (IGA) to address limitations of traditional methods in optimizing network reliability, such as premature convergence and suboptimal solutions. This article improved the reliability optimization effect of computer network by dynamically adjusting the crossover rate and mutation rate and IGA. It analyzed the network topology to determine the optimization objectives and constraints. It constructed a population initialization strategy to generate the diversity of initial solutions, and then introduced a mechanism to dynamically adjust the crossover rate and mutation rate. During the iteration process, the algorithm can adaptively respond to complex environmental changes. By designing the fitness function and taking network reliability and packet loss rate as evaluation indicators, the performance of the improved genetic algorithm (IGA) is compared with that of the traditional GA and PSO (Particle Swarm Optimization) algorithm under different network conditions. The experimental results show that when the total network medium cost is 50, the network reliability of the IGA reaches 98.2%, and the packet loss rate is 1.2%, while the network reliability of the traditional GA is only 77.6%, and the packet loss rate is 3.5%; when the total cost is 150, the network reliability of the IGA is still maintained at 97.5%, while the performance of GA and PSO is significantly lower than the former. The IGA shows significant advantages in improving the reliability of computer networks, can effectively reduce fault repair time and packet loss rate, and provides a practical solution for network optimization.