The Application of an Improved Particle Swarm Optimization for Multi-Constrained QoS Routing

With the development of network, users´services put forward diverse demands on the network QoS (Quality of Service), the QoS routing is the optimization problem under the satisfaction of multiple QoS constraints. This paper firstly sets up a multi-constrained QoS routing model and constructs the fitness value function by transforming the QoS constraints with a penalty function. Secondly, we merge and discrete the iterative formula of PSO (Particles Swarm Optimization) to tailor it to non-continuous search space routing problem. Finally, the natural selection and mutation ideas of Genetic Algorithm are applied to the PSO to improve the PSO algorithm, which makes the particles more diversity. The simulation results show that the proposed algorithm can not only successfully solve the multi-constrained QoS routing problem and increase entire network performance, it also achieves a better effect in the success rate of the search.