School of Computer, Electronics and Information, Guangxi University, No. 100 University East Road, Nanning 530004, China.
Sensors (Basel). 2022 Jan 25;22(3):914. doi: 10.3390/s22030914.
The RapidIO standard is a packet-switching interconnection technology similar to the Internet Protocol (IP) conceptually. It realizes the high-speed transmission of RapidIO packets at the transport layer, but this greatly increases the probability of network blocking. Therefore, it is of great significance to optimize the RapidIO routing strategy. For this problem, this paper proposes a Double-Antibody Group Multi-Objective Artificial Immune Algorithm (DAG-MOAIA), which improves the local search and global search ability of the population by adaptive crossover and adaptive mutation of the double-antibody groups, and uses co-competition of multi-antibody groups to increase the diversity of population. Through DAG-MOAIA, an optimal transmission path from the source node to multiple destination nodes can be selected to solve the Quality Of Service (QoS) problem during data transmission and ensure the QoS of the RapidIO network. Simulation results show that DAG-MOAIA could obtain high-quality solutions to select better routing transmission paths, and exhibit better comprehensive performance in all simulated test networks, which plays a certain role in solving the problem of the RapidIO routing strategy.
RapidIO 标准是一种类似于概念上的 Internet 协议 (IP) 的数据包交换互连技术。它在传输层实现 RapidIO 数据包的高速传输,但这大大增加了网络阻塞的概率。因此,优化 RapidIO 路由策略具有重要意义。针对这一问题,本文提出了一种双抗体群多目标人工免疫算法(DAG-MOAIA),通过双抗体群的自适应交叉和自适应变异来提高种群的局部搜索和全局搜索能力,并利用多抗体群的共同竞争来增加种群的多样性。通过 DAG-MOAIA,可以选择从源节点到多个目标节点的最佳传输路径,解决数据传输过程中的服务质量 (QoS) 问题,并确保 RapidIO 网络的 QoS。仿真结果表明,DAG-MOAIA 可以获得高质量的解决方案,选择更好的路由传输路径,并在所有模拟测试网络中表现出更好的综合性能,这对解决 RapidIO 路由策略问题起到了一定的作用。