An improved quantum-inspired evolutionary algorithm for coding resource optimization based network coding multicast scheme

This paper investigates how to minimize the required coding resources in network-coding-based multicast scenarios. An evolutionary algorithm (MEQEA) is proposed to address the above problem. Based on quantum-inspired evolutionary algorithm (QEA), MEQEA introduces multi-granularity evolution mechanism which allows different chromosomes, at each generation, to have different rotation angle step values for update. In virtue of this mechanism, MEQEA significantly improves its capability of exploration and exploitation, since its optimization performance is no longer overly dependant upon the single rotation angle step scheme shared by all chromosomes. MEQEA also presents an adaptive quantum mutation operation which is able to prevent local search efficiently. Simulations are carried out over a number of network topologies. The results show that MEQEA outperforms other heuristic algorithms and is characterized by high success ratio, fast convergence, and excellent global-search capability.