Swarm Intelligence Optimization Algorithm And The Application In Routing Optimization Strategy

With the different Along with uninterrupted development of network, the demands fornetwork services are becoming increasingly realistic and urgent. QoS routing network whichemerged as the times require has also become a turning point in the network development. Untilnow, QoS Network does not have a fixed mathematical model, but its original intention is toimprove the network quality of service to meet the current demand for a variety of complexnetwork services which plays a very important role. It is an optimized of network variety metricsfor QoS routing itself, therefore, QoS routing optimization work focus on a variety optimizationof network metrics constrained, which is to meet the necessary network constraints.As a novel optimization algorithm, swarm intelligence algorithm has been widely applied inmany fields of science, such as computer science, communication science and so on. This papernot only gives the concept of swarm intelligent optimization algorithms, but also describes avariety of typical optimization algorithm. Especially Ant colony algorithm, as a relatively goodoptimization algorithm, has been widely applied in the field of QoS routing algorithm, of whichthis paper carried out a detailed description and its slow convergence shortcoming and easy tofall into local minima, an improved adaptive ant colony algorithm was provided. The algorithmusing self-adaptive mutation rules to improve the ant colony search, improved search efficiency,and made it easier to find the global optimum.Combined with the three modes of QoS routing, including unicast routing, multicast routingand anycast routing, the improved algorithm was applied to these three algorithms. Simulationcomparison proceeds to the application on the concrete results through the simulation experiment.In the experiment, two metrics constrained which was the delay and bandwidth were selected tosimulate. And as for algorithm pheromone update, we added the chaotic ant colony algorithmand the maximum and minimum update algorithm to update information elements on the path.The experimental results showed that the improved ant colony algorithm was superior to theordinary algorithm, and by a small number of iterations; the algorithm was able to jump out oflocal optima, and ants in the search was easier to find the global optimum.Finally, summarizing this article, affirming AMACA algorithm applied in QoS routing, atthe same time, it described the some shortcomings ant colony algorithm existed and developmentof QoS routing algorithm, these issues should be the focus of study in future work.