A Cooperative Evolution of Multiple Operators Based Adaptive Quantum Genetic Algorithm for Network Coding Resources Optimization

Abstract

In order to optimize the network coding resources in a multicast network, an improved adaptive quantum genetic algorithm (AM-QEA) was proposed. Firstly, the optimization problem was translated into a graph decomposition problem. Then the graph decomposition problem was represented by the binary coding, which can be processed by quantum genetic algorithm. At last, a multiple-operators based adaptive quantum genetic algorithm was proposed to optimize the network coding resources. In the algorithm, the individual fitness evaluation operator and population mutation adjustment operator were employed to solve the shortcomings of common quantum genetic algorithm, such as high convergence rate, easy to fall into local optimal solution and low diversity of the population in later stage. The experimental results under various topologies show that the proposed algorithm has the advantages of high multicast success rate, fast convergence speed and strong global search ability in resolving the network coding resource optimization problems.Network coding is an effective technique for improving network throughput, and it also can bring many benefits for the multicast transmission. It allows intermediate network nodes to perform arbitrary mathematical calculations to combine (encode) the input packets, and then output the encoded packets to the downstream nodes [2]. The key problem network coding based multicast technology is how to quickly and efficiently determine the coding nodes of each route in the network. Therefore, the point-to-multipoint multicast rate can reach the upper limit specified by Shannon's "maximum stream-minimum cut" theorem [3]. As an emerging technology, network coding technology has received much attention. However, this problem belongs to the NP-hard problem, and there is currently no clear and efficient solution.