The Research On Swarm Intelligence Algorithm In The Network Strategy And Its Application

In the Internet, with the increasing of multimedia communications and network video, the conflict between resource and demand of network is more and more remarkable. The balance of routing and flux in the network is most important factor in the development of network. How to restrain the congestion of network and improve the quality of network, which makes the network dynamic balance, is the focus technique research in the network.The unicast and multicast routing of network is importance capability inspection of network, which is paid more attention by people these days, especially in the multiple constrained condition of network. The quality-of-service(QoS)is adopted in the quality of network mostly. For the dynamic balance in the network, the demands of network character parameter such as delay, delay-jitter, bandwidth, packet-loss and cost are considered at the same time, which are independent each other. The routing, which are content the multi-parameter limit, is NP-complete problems in the network.In the research of the unicast and multicast routing of network simultaneously, many scholar are studied to investigate into flux of network at home and abroad presently, which are all focus on traffic engineering(TE). Recently the emphases questions of the traffic engineering focus on multiple constrained condition of routing. Considering of the multiple constrained condition, this dissertation makes the flux distributing in the network uniformity and optimizes the dynamic capability of network based on flux of network and state of resource through carrying out reasonable control.The dissertation contains the several parts content of unicast routing, multicast routing and balance of traffic network. By uses of character of intelligence algorithm, a new measure is studied by the research of unicast routing, multicast routing and control of traffic engineering as a whole. At the same time, the convergence analysis of algorithm and the safety of network are analyzed totally. The main achievements of this dissertation include:1.How to solve QoS optimizing routing problem was researched deeply by improvement technique. Firstly, A new algorithm was brought forward by multiple constraint optimization based on particle swarm amalgamation combination of ant colony algorithm, which adopts particle swarm optimization to get initialization a new solution by searching routing and avoided to be trapped into local seeking solution only by ant colony algorithm. This algorithm increased the scope of searching better routing, advanced self-adaptable capability and accurate optimizing. Secondly, a multiple constrained QoS algorithm based on chaos and ant colony optimization was proposed. By using of the properties of randomicity, regularity and ergodicity of chaos, the mixed algorithms found out the whole seeking solution quickly. Then, the mixed means improved ant colony algorithms by chaos factor and improved the searching capability. The result of searching had the advantage over the base ant colony algorithm remarkably. The experimental results show that these two new improved algorithms have high efficiency.2.Linking to the character of multicast routing in network, combination of clone and particle swarm optimization based on multiple constrained multicast routing algorithms was put forward though analyzing of multiple-constrain in the multicast routing network. The new multicast routing algorithm was studied by the change of speed and location finding multicast tree and by the Immunity Clone algorithm to search best route, which decreased the time of the local and global searching. The clone algorithm added process of clone copy,clone mutation and clone selection. In the course of clone mutation, the algorithm was high adaptability with the definite probability by changing. Then, in the course of clone selection, the algorithm avoided the degeneracy of genus regularly and enhanced the speed of convergence algorithm and the global searching capability. The simulation experimental results show that the improved algorithms have better optimization performance.3.By analyzing the relation between networks of traffic and routing deeply, the fuzzy weight value of routing controlled ant colony optimization algorithm based limited bandwidths(Fuzzy-ACO)were proposed based on the research. In the ant colony optimization algorithm based limited bandwidths, networks of traffic was controlled in the weight value by fuzzy. Since the mathematics model was founded with networks of traffic, spending of networks was decreased by large numbers of detector with grouping. By means of inspecting content of network with real time, the networks of traffic and routing were balanced dynamically. Simultaneously, the networks of traffic with the weight value were connected with pheromone, which dynamically adjusts optimal routing selected among multiple paths. The ant colony algorithm achieves globally searching ability. The simulation results show that the given algorithm was effective and high speed,in which it dramatically improved the exploring speed of convergence in network traffic by traditional networks traffic algorithm.4.For the multiple constrained condition ant-colony-optimization(ACO) algorithms, the making choice of constrained condition and designing function was very importance. The convergence of ant colony algorithm under the quality-of-service(QoS)condition was studied. By redefining the selection of pheromone, the convergence of algorithm was demonstrated by applying theory. Through the changing time of pheromone and scope of value of pheromone was analyzed in the ant colony algorithm with QoS condition, the controllability of ACO was also proved theoretically. The simulation results show that the given algorithm was practicable, by making the algorithm converge both locally and globally under a general convergence condition. This works may provide a foundation for further theoretical studies on the multiple-constrain QoS of ACO.5.By analyzing the network's security, the threat of the intrusion on line was detected with theory of biology clustering. The clustering analysis way by combination of particle-swarm-optimization(PSO)and ant-colony-optimization(ACO)algorithm was discussed. Firstly, the center and number of clustering are determined by using the PSO, and then the above clustering results are optimized by the K-means algorithm combining with ACO. The simulated experiments show that the combining algorithm is obviously superior to some common clustering algorithms since it has obvious advantage in optimization capacity.In the dissertation, the unicast routing, multicast routing and balance of traffic network was analyzed and discussed completely. Some effective improvement methods were proposed and the convergence of the algorithm was demonstrated in this dissertation. The Cluster Analysis in the safety of network was proposed. Those all swarm intelligence algorithms were realized the application of analysis in the network. Lastly, the work of this dissertation is summarized, and further research directions were indicated.