Hamming-distance-based adaptive quantum-inspired evolutionary algorithm for network coding resources optimization

An adaptive quantum-inspired evolutionary algorithm based on Hamming distance (HD-QEA) was presented to optimize the network coding resources in multicast networks. In the HD-QEA, the diversity among individuals was taken into consideration, and a suitable rotation angle step (RAS) was assigned to each individual according to the Hamming distance. Performance comparisons were conducted among the HD-QEA, a basic quantum-inspired evolutionary algorithm (QEA) and an individual's fitness based adaptive QEA. A solid demonstration was provided that the proposed HD-QEA is better than the other two algorithms in terms of the convergence speed and the global optimization capability when they are employed to optimize the network coding resources in multicast networks.