Game theory–based multi-task scheduling in cloud manufacturing using an extended biogeography-based optimization algorithm

Xiao, Jiuhong; Zhang, Wenyu; Zhang, Shuai; Zhuang, Xiaoyu

doi:10.1177/1063293x19882744

Cited by 30 publications

(17 citation statements)

References 34 publications

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“…

represents the maximum rate of mutation.

represents the probability of the habitat with the number of species

[30] .

represents the maximum probability of species.…”

Section: Methodsmentioning

confidence: 99%

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images

Sun

Tang

et al. 2021

Knowledge-Based Systems

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Aim: By October 6, 2020, Coronavirus disease 2019 (COVID-19) was diagnosed worldwide, reaching 3,355,7427 people and 1,037,862 deaths. Detection of COVID-19 and pneumonia by the chest X-ray images is of great significance to control the development of the epidemic situation. The current COVID-19 and pneumonia detection system may suffer from two shortcomings: the selection of hyperparameters in the models is not appropriate, and the generalization ability of the model is poor. Method: To solve the above problems, our team proposed an improved intelligent global optimization algorithm, which is based on the biogeography-based optimization to automatically optimize the hyperparameters value of the models according to different detection objectives. In the optimization progress, after selecting the immigration of suitable index vector and the emigration of suitable index vector, we proposed adding a comparison operation to compare the value of them. According to the different numerical relationships between them, the corresponding operations are performed to improve the migration operation of biogeography-based optimization. The improved algorithm (momentum factor biogeography-based optimization) can better perform the automatic optimization operation. In addition, our team also proposed two frameworks: biogeography convolutional neural network and momentum factor biogeography convolutional neural network. And two methods for detection COVID-19 based on the proposed frameworks. Results: Our method used three convolutional neural networks (LeNet-5, VGG-16, and ResNet-18) as the basic classification models for chest X-ray images detection of COVID-19, Normal, and Pneumonia. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 1.56%, 1.48%, and 0.73% after using biogeography-based optimization to optimize the hyperparameters of the models. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 2.87%, 6.31%, and 1.46% after using the momentum factor biogeography-based optimization to optimize the hyperparameters of the models. Conclusion: Under the same experimental conditions, the performance of the momentum factor biogeography-based optimization is superior to the biogeography-based optimization in optimizing the hyperparameters of the convolutional neural networks. Experimental results show that the momentum factor biogeography-based optimization can improve the detection performance of the state-of-the-art approaches in terms of overall accuracy. In future research, we will continue to use and improve other global optimization algorithms to enhance the application ability of deep learning in medical pathological image detection.

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“…

represents the maximum rate of mutation.

represents the probability of the habitat with the number of species

[30] .

represents the maximum probability of species.…”

Section: Methodsmentioning

confidence: 99%

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images

Sun

Tang

et al. 2021

Knowledge-Based Systems

View full text Add to dashboard Cite

show abstract

“…Being benefited from the internet technology advances, cloud-computation schemes have integrated into traditional manufacturing industries, and cloud-manufacturing applications have been implemented on various production lines (Abrishamkar et al, 2014; Sang and Xu, 2017; Wang et al, 2017). Cloud-based frameworks breakthrough geometric limitations, and distributed controller nodes offer more dexterity, greater performance, and lower cost systems simultaneously (Xiao et al, 2019). Note that high-performance servers perform most training process for neural network models, and developers select high-end computers for training and deployment based on affordable computations.…”

Section: Cloud-computation Of a Cnn Deep-learning Networkmentioning

confidence: 99%

An optimization strategy for HMI panel recognition of CNC machines using a CNN deep-learning network

Lee

Lin

2021

Concurrent Engineering

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This paper suggests an optimization strategy to train a CNN deep-learning network, which successfully recognizing working status on the HMI panels of CNC machines. To verify the developed strategy, the research experiments using a prototype that consists of a CNC milling machine and an industrial robot. In the optimization strategy, the research first defines a length-varying hyperparameter list for the deep-learning network, and the entities in the list adjust themselves to optimize the model scales. During the optimization process, this paper adopts a two-stage training scheme that gradually augments image datasets to improve HMI control-panel recognition performances, such as recognition accuracy and recognition speed to identify the CNC machine working status. Using an open-source PyTorch platform, this research establishes a cloud-based distributed architecture to build training codes for the deep-learning network, in which an applicable optimization model is deployed to recognize the CNC control-panel working status. The optimization strategy employs minimal codes to rebuild the architecture and the least efforts to reform the manufacturing system. The optimally trained model provides up to a 99.34% CNC panel-message recognition accuracy and a high-speed recognition of 100 images in 0.6 s. Moreover, the developed optimization strategy enables the prediction of necessitated dataset augmentation to training a practically implemented CNN network.

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“…Then, they designed and conducted a series of experiments to verify the effectiveness of the proposed algorithm. Xiao et al [26], aiming at the multitask scheduling problem, proposed a new cloud manufacturing multitask scheduling model based on game theory from the customer perspective. Additionally, they proposed an extended biogeography-based optimization algorithm that embeds three improvements to solve the multitask scheduling model.…”

Section: Scosmentioning

confidence: 99%

A Manufacturing SCOS Model (MSCOS) Based on the Similarity of Parameter Sequences Between Tasks and Service Composition

Gao

Guo

Yan

2021

Int J Comput Intell Syst

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Service composition and optimal selection (SCOS) is a core issue in cloud manufacturing (CMfg) when integrating distributed manufacturing services for complex manufacturing tasks. Generally, a set of recommended task parameter sequences (Tps) will be given when publishing manufacturing tasks. The similarity between the service composition parameter sequence (SCps) and Tps also reflects the rationality of the service composition. However, various evaluation models based on QoS have been proposed, ignoring the rationality between the Tps and SCps. Considering the similarity of the Tps and SCps in an evaluation model, we propose a manufacturing SCOS framework called MSCOS. The framework includes two parts: an evaluation model and an algorithm for both optimization and selection. In the evaluation model, based on the numerical proximity and geometric similarity between the Tps and SCps, improving the technique for order preference by similarity to an ideal solution (TOPSIS) with the grey correlation degree (GC), we propose the GC&TOPSIS (GTOPSIS). In the optimization and selection algorithm, an improved flower pollination algorithm (IFPA) is proposed to achieve optimization and selection based on polyline characteristics between the fitness values in the population. Experiments show that the MSCOS evaluation effect and optimal selection offer better performance than commonly used algorithms.

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Game theory–based multi-task scheduling in cloud manufacturing using an extended biogeography-based optimization algorithm

Cited by 30 publications

References 34 publications

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images

An optimization strategy for HMI panel recognition of CNC machines using a CNN deep-learning network

A Manufacturing SCOS Model (MSCOS) Based on the Similarity of Parameter Sequences Between Tasks and Service Composition

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