Research Of Network Coding Optimization Based On Intelligent Algorithm For Optical Multicast

With the rapid growth of new businesses, such as distance education, IPTV,videoconferencing and so on, the optical multicast with high transmission capacity andlarge bandwidth capacity has become the main method of multicast technology. Thedevelopment of new multicast applications also increases the consumption of resourcesand makes the available wavelength resources more scarce in optical multicast. Thenetwork coding can improve bandwidth utilization and network throughput. Therefore,the combination of network coding and optical multicast can solve the problem what theoptical multicast faces.The traditional optical multicast routing algorithms with using network coding canincrease network throughput, improve network robustness and balance network load.Therefore, in chapter2, we describe the basic theories of optical multicast and networkcoding, the structure of coding node and the implementation for optical multicast,intelligent optimization algorithms and emphatically analyze the application ofintelligent optimization algorithm for optical multicast routing optimization.However, excessive coding operations will increase the resource consumption ofthe optical network. Thus, it is necessary to use as less coding operations as possiblewithout reducing received rate. Because the optimization of the network coding is a NPhard problem, the intelligent optimization algorithm which is a suitable method forsolving this kind of problem is adopted in chapter3. With the increasing of the searchspace in algorithm, the existing intelligent optimization algorithm will appear prematureand be easy to fall into a local optimum. Therefore, in chapter3, the algorithm ofminimizing coding links based on the genetic algorithm is proposed. The dynamicmutation and new individual accepting strategy are designed to improve the diversity ofpopulation, which can improve the rate of population convergence. The use of optimalindividual retention policies can effectively prevent the best individual to be destroyed.The local search algorithm is added to improve the ability of local search in algorithm,which can effectively prevent the population falling into a local optimum solution.Compared with the existing algorithms, the proposed algorithm is simulated to showthat it has higher efficiency, faster convergence speed and can find the optimalinformation transmitting scheme required fewer encoding links in the optical network. The optical multicast routing algorithm based on network coding receives the datasent from the source node by the maximum multicast rate, which wastes the reception ofsome destination nodes. For taking advantage of the reception of destination node toimprove the throughput of network, in chapter4, a new routing algorithm for opticalmulticast based on the teaching and learning optimization algorithm is proposed.All-one vector is added to the algorithm which ensures that there is at least one feasibleindividual at the beginning of the algorithm. The self-driven learning and mutationoperation of genetic algorithm are designed to increase the diversity of the individualstudies and population. The probability vector restart scheme is designed to enhancethe global search capability of the algorithm. The simulated results demonstrate that thealgorithm has a faster convergence speed and can achieve higher network throughputcompared with the existing algorithms.