Author(s)

Hongbo Xu, Shasha Wang, Zhijian Qu^*

School of Computer Science and Technology, Shandong University of Technology, Zibo, China.

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, Quantum Genetic Algorithm, Multicast Networks, Optimization

Xu, H. , Wang, S. and Qu, Z. (2019) A Cooperative Evolution of Multiple Operators Based Adaptive Quantum Genetic Algorithm for Network Coding Resources Optimization. Journal of Computer and Communications, 7, 147-161. doi: 10.4236/jcc.2019.77014.