Fuzzy immune particle swarm optimization algorithm and its application in scheduling of MVB periodic information1

Wang, Yizhao; Wang, Lide; Xiang, Yan; Shen, Peng

doi:10.3233/ifs-152067

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…erefore, a switch migration optimization algorithm is proposed. Classical optimization algorithms include nearby selection algorithms [3], genetic algorithms [4][5][6], immune particle swarm optimization algorithms, and so on [5][6][7][8].…”

Section: Related Workmentioning

confidence: 99%

Load Balancing of SDN Controller for Migration Optimization Based on Metaheuristics

Min

2022

Mathematical Problems in Engineering

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With the existing power SDN network architecture, the improved switches migration algorithm is put forward. Through the advantages of the ant colony algorithm and genetic algorithm fusion, the migration process is optimized, and the fusion algorithm in switches uses the operation, such as restrictions in advance and pruning algorithm efficiency to eventually apply migration under multiple controllers to the switch. The efficiency and stability of switch migration under multicontroller are improved, and the performance of the RYU controller is compared with other algorithms. The results of the algorithm and performance comparison test show that the improved ant colony algorithm is reduced by 12.5% and 1.5%, which is a comparison of response delay; the accuracy of the migration operation of the algorithm can make full use of the resources of the controller and achieve the purpose of improving the migration efficiency and load balancing among multiple controllers.

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Section: Related Workmentioning

confidence: 99%

Load Balancing of SDN Controller for Migration Optimization Based on Metaheuristics

Min

2022

Mathematical Problems in Engineering

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“…The fuzzy logic control is one of the most popular control techniques, and it is often used to adjust the parameters of control systems or other algorithms adaptively. The fuzzy logic control is applied in numerous mixes with other algorithms such as the fuzzy logic for inertia weight particle swarm optimization in [40], immune particle swarm optimization in [41], neural networks in [42], model-free adaptive control in [43], and virtual reference feedback tuning in [44]. At present, we have not seen the work of using fuzzy control logic to adaptively control the CE coefficient in QPSO.…”

Section: Related Workmentioning

confidence: 99%

Scheduling Optimization of Time-Triggered Cyber-Physical Systems Based on Fuzzy-Controlled QPSO and SMT Solver

et al. 2020

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The time-triggered communication paradigm is a cost-efficient way to meet the real-time requirements of cyber-physical systems. It is a non-deterministic polynomial NP-complete problem for multi-hop networks and non-strictly periodic traffic. A two-level scheduling approach is proposed to simplify the complexity during optimization. In the first level, a fuzzy-controlled quantum-behaved particle swarm optimization (FQPSO) algorithm is proposed to optimize the scheduling performance by assigning time-triggered frame instances to the basic periods of each link. In order to prevent population from high aggregation, a random mutation mechanism is used to disturb particles at the aggregation point and enhance the diversity at later stages. Fuzzy logic is introduced and well designed to realize a dynamic adaptive adjustment of the contraction–expansion coefficient and mutation rate in FQPSO. In the second level, we use an improved Satisfiability Modulo Theories (SMT) scheduling algorithm to solve the collision-free and temporal constraints. A schedulability ranking method is proposed to accelerate the computation of the SMT-based incremental scheduler. Our approach can co-optimize the jitter and load balance of communication for an off-line schedule. The experiments show that the proposed approach can improve the performance of the scheduling table, reduce the optimization time, and reserve space for incremental messages.

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“…From the perspective of communication, MVB is a time division multiplexing (TDM) mode master-slave network [35]. Some researches have been done on the real-time features of MVB to improve the control performance of the whole networked system by Yan et al [20] and Wang et al [21]. Synchronization is another issue about the train control network.…”

Section: Mvb Framework and Health Evaluation Systemmentioning

confidence: 99%

Health Evaluation of MVB Based on SVDD and Sample Reduction

Wang

Yang

et al. 2019

IEEE Access

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Multifunction vehicle bus (MVB) is the most widely used train communication network whose performance degradation and anomaly will heavily affect the train's safe and stable operation. However, current scheduled maintenance and post-failure maintenance of MVB cannot detect the early anomaly and evaluate the health condition of the network in time. This paper provides a method to detect the anomaly and evaluate the health condition of MVB based on a one-class classification (OCC) algorithm called density-based sample reduction for support vector data description (DBSRSVDD). First, network features are extracted from physical layer waveform parameters. In order to reduce the computational complexity of SVDD, a sample reduction operation is conducted to screen out the edge samples as support vector candidates. Then, the SVDD models representing the normal patterns of a single MVB node are trained based on the support vector candidates. Performance degradation of the node is quantified by the distance between the tested sample and the trained hyper sphere. The whole network's health condition is the linear weighted sum of the nodes' scores based on their bandwidth occupancy. The experimental results show that the proposed method can detect the anomaly and degradation of MVB successfully, improve accuracy, and reduce training time compared with the existing methods.

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Fuzzy immune particle swarm optimization algorithm and its application in scheduling of MVB periodic information1

Cited by 9 publications

References 15 publications

Load Balancing of SDN Controller for Migration Optimization Based on Metaheuristics

Load Balancing of SDN Controller for Migration Optimization Based on Metaheuristics

Scheduling Optimization of Time-Triggered Cyber-Physical Systems Based on Fuzzy-Controlled QPSO and SMT Solver

Health Evaluation of MVB Based on SVDD and Sample Reduction

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