用于网络编码优化的改进量子进化算法

摘要

It has been proved that network coding, which allows network intermediate nodes to perform processing operations on the incoming packets instead of simply forwarding them, can approach the max-flow min-cut limit of the network graph. But such coding operations in network nodes incur additional computational overhead and consume public resources. Under condition of achieving the desired throughput in multicast scenario, this paper presents an improved quantum-inspired evolutionary algorithm (IQEA-NC) to minimize network coding resources. Compared with normal quantum-inspired evolutionary algorithm, IQEA-NC can achieve some effective improvements, such as decreasing the search space, increasing global search capacity, and jumping out of local optimum. The simulation experiment results show that IQEA-NC runs faster and more efficiently, improves the optimization performance compared with the existing algorithm.%网络编码允许网络中间节点对输入数据进行处理而非简单转发，提高了网络的吞吐量和鲁棒性，已经被证明能够达到网络最大流最小割限制。但网络节点的编码操作引发了额外的计算及资源开销。为此，该文提出了一种针对网络编码优化的改进量子进化算法IQEA-NC，以满足达到理论多播速率的情况下最小化网络的编码开销目的。IQEA-NC对传统量子进化算法进行了有效的改进，降低了算法搜索空间，增强了全局搜索能力，同时避免了陷入局部最优。仿真对比实验表明，同已有的量子进化算法及其他进化算法相比，该方法提高了优化性能，在准确性和收敛速度上都具有较大的优势。