A PSO Optimization Scale-Transformation Stochastic-Resonance Algorithm With Stability Mutation Operator

Tong, Ling; Li, Yong; Hu, Jinhai; Ren, Litong

doi:10.1109/access.2017.2778022

Cited by 56 publications

(32 citation statements)

References 13 publications

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“…The most likely reason is that the BPSO falls into a local optimum and can not reach a further better solution. We may introduce chaos searching [38], induce Niching Behavior [39], or use other strategies, such as resampling technique [40], or weight-digression strategy [41] to balance its global search ability and convergence speed in future work.…”

Section: Convergence Analysismentioning

confidence: 99%

Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

Zhu¹,

Jin²,

Pastor³

et al. 2019

IEEE Internet Things J.

169

121

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With the emerging vehicular applications such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in Vehicular Fog Computing (VFC). Folo is designed to support the mobility of vehicles, including vehicles that generate tasks and the others that serve as fog nodes. Considering constraints on service latency, quality loss, and fog capacity, the process of task allocation across stationary and mobile fog nodes is formulated into a joint optimization problem. This task allocation in VFC is known as a non-deterministic polynomial-time hard (NP-hard) problem. In this paper, we present the task allocation to fog nodes as a bi-objective minimization problem, where a trade-off is maintained between the service latency and quality loss. Specifically, we propose an event-triggered dynamic task allocation (DTA) framework using Linear Programming based Optimization (LBO) and Binary Particle Swarm Optimization (BPSO). To assess the effectiveness of Folo, we simulated the mobility of fog nodes at different times of a day based on real-world taxi traces and implemented two representative tasks, including video streaming and real-time object recognition. Simulation results show that the task allocation provided by Folo can be adjusted according to actual requirements of the service latency and quality, and achieves higher performance compared with naive and random fog node selection. To be more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%.

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Section: Convergence Analysismentioning

confidence: 99%

Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

Zhu¹,

Jin²,

Pastor³

et al. 2019

IEEE Internet Things J.

169

121

View full text Add to dashboard Cite

show abstract

“…The basic particle swarm optimization algorithm as one of the optimization algorithms uses each particle as a potential solution to the optimization problem [17]. It could update the location and the velocity of the particle by combining particles during each iteration.…”

Section: A Basic Particle Swarm Optimizationmentioning

confidence: 99%

Optimal Configuration of Micro Power Supply for Hybrid AC/DC Micro-grid

Zhang¹,

Zhang²

2018

Proceedings of the 2018 2nd International Conference on Electrical Engineering and Automation (ICEEA 2018)

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In the ac/dc hybrid micro-grid, the reasonable configuration of the micro power to improve power supply reliability and economy is the most important issue due to the types and combinations of distributed power sources are too many. The paper considers the power installation cost, the operation maintenance cost and the equipment replacement cost, as well as the loss of power supply probability and the excess energy ratio as the multi-objective function. The improved particle swarm optimization algorithm is used to optimize the micro power. Through matlab simulation verification, and the basic particle swarm optimization algorithm for comparative analysis. The results indicate that the improved particle swarm algorithm can effectively save the cost of the system and improve the operation efficiency of the ac/dc hybrid micro-grid. Keywords-ac/dc hybrid micro-grid; improved particle swarm optimization algorithm; optimal configuration; loss of power supply probability I.

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“…Moreover, the manual adjustment method can only be applied in offline detections. In order to solve these issues, by using some multiparameter optimization algorithms such as particle swarm optimization (PSO) [28] and genetic algorithm (GA) [22,29], some adaptive parameter-adjusting methods, which can realize SR in online conditions with high efficiency and high reliability, have been proposed and studied [30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

Lai

Wang

Zhang

et al. 2020

Shock and Vibration

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The weak-signal detection technologies based on stochastic resonance (SR) play important roles in the vibration-based health monitoring and fault diagnosis of rolling bearings, especially at their early-fault stage. Aiming at the parameter-fixed vibration signals in practical engineering, it is feasible to diagnose the potential rolling bearing faults through adaptively adjusting the SR system parameters, as well as other generalized parameters such as the amplitude-transformation coefficient and scale-transformation coefficient. However, extant adaptive adjustment methods focus on the system parameters, while the adjustments of other adjustable parameters have not been fully studied, thus limiting the detection performance of the adaptive SR method. In order to further enhance the detection performance of adaptive SR methods and extend their application in rolling bearing fault diagnosis, an adaptive multiparameter-adjusting SR (AMPASR) method for bistable systems based on particle swarm optimization (PSO) algorithm is proposed in this paper. This method can produce optimal SR output through adaptively adjusting multiparameters, thus realizing fault feature extraction and further fault diagnosis. Furthermore, the influence of algorithm parameters on the optimization results is discussed, and the optimization results of the Langevin system and the Duffing system are compared. Finally, we propose a weak-signal detection method based on the AMPASR of the Duffing system and employ three diagnosis examples involving inner ring fault, outer ring fault, and rolling element fault diagnoses to demonstrate its feasibility in rolling bearing fault diagnosis.

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A PSO Optimization Scale-Transformation Stochastic-Resonance Algorithm With Stability Mutation Operator

Cited by 56 publications

References 13 publications

Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

Optimal Configuration of Micro Power Supply for Hybrid AC/DC Micro-grid

Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

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