Abstract
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.
Highlights
The advent of the 5G era and the concept of the ’Internet of Everything’ have profoundly accelerated the layout of the Internet of Things (IoT) [1]
This section will verify the performance of DAG-MOAIA from three aspects: the comparison of Pareto front, the optimization of Quality Of Service (QoS) constraints, and the comparison of HV
The packet loss rate, delay and transmission cost obtained by DAG-MOAIA during data transmission in the RapidIO network are lower than those obtained by other algorithms, which suggests that DAG-MOAIA
Summary
The advent of the 5G era and the concept of the ’Internet of Everything’ have profoundly accelerated the layout of the Internet of Things (IoT) [1]. In the QAC-MORA, to increase the solution space of the algorithm, the quantum bits are introduced to represent node pheromones, and quantum gates are rotated to update the pheromone In such a way, the QAC-MORA overcomes the disadvantage of traditional Ant Colony Algorithm falling local optimum and can find a better routing path for the QoS-MCRP. The MOABCA integrates two schemes: the elitism-based bee food source generation scheme for scout bees and the Pareto local search operator scheme Through these two schemes, the local search ability and global search ability of the algorithm is improved, and the diversity of the population is increased at the same time, solving the problem of falling into the local optimum of traditional Bee Colony Algorithm (BCA).
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