An Adaptive Localized Decision Variable Analysis Approach to Large-Scale Multiobjective and Many-Objective Optimization

Ma, Lianbo; Huang, Min; Yang, Shengxiang; Wang, Rui

doi:10.1109/tcyb.2020.3041212

Cited by 201 publications

(82 citation statements)

References 52 publications

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“…With well-designed feature representation, ECG classification can be realized by models based on Bayes, K-means, Decision tree, and Linear Discriminate classifiers [9][10][11], along with commonly used optimization techniques [12,13]. Features like cycle and higher order of QRS wave were extracted in reference [14].…”

Section: Ecg Classification Methodmentioning

confidence: 99%

Personalized Federated Learning for ECG Classification Based on Feature Alignment

Tang

Luo

Qian

et al. 2021

Security and Communication Networks

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Electrocardiogram (ECG) data classification is a hot research area for its application in medical information processing. However, insufficient data, privacy preserve, and local deployment are still challenging difficulties. To address these problems, a novel personalized federated learning method for ECG classification is proposed in this paper. First, a global model is trained with federated learning framework on multiple local data clients. Then, we use the global model and private data to train the local model. To reduce the feature inconsistency between global and private local data and for better fitting the private local data, a novel ”feature alignment” module is devised to guarantee the uniformity, which contains two parts, global alignment and local alignment, respectively. For global alignment, the graph metric of batch data is used to constrain the dissimilarity between features generated by the global model and local model. For local alignment, triplet loss is adopted to increase discriminative ability for local private data. Comprehensive experiments on our collected dataset are evaluated. The results show that the proposed method can be better adapted to local data and exhibit superior ability of generalization.

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Section: Ecg Classification Methodmentioning

confidence: 99%

Personalized Federated Learning for ECG Classification Based on Feature Alignment

Tang

Luo

Qian

et al. 2021

Security and Communication Networks

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“…FedProx made a slight modification to FedAvg to ensure convergence in both theory and practice. Many advanced optimization methods are also available for federated system optimization [25][26][27][28].…”

Section: Related Workmentioning

confidence: 99%

Utility Optimization of Federated Learning with Differential Privacy

Zhao

Mao

Chenxi

et al. 2021

Discrete Dynamics in Nature and Society

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Secure and trusted cross-platform knowledge sharing is significant for modern intelligent data analysis. To address the trade-off problems between privacy and utility in complex federated learning, a novel differentially private federated learning framework is proposed. First, the impact of data heterogeneity of participants on global model accuracy is analyzed quantitatively based on 1-Wasserstein distance. Then, we design a multilevel and multiparticipant dynamic allocation method of privacy budget to reduce the injected noise, and the utility can be improved efficiently. Finally, they are integrated, and a novel adaptive differentially private federated learning algorithm (A-DPFL) is designed. Comprehensive experiments on redefined non-I.I.D MNIST and CIFAR-10 datasets are conducted, and the results demonstrate the superiority of model accuracy, convergence, and robustness.

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“…IGD is a comprehensive indicator to measure the convergence and distribution of the solution sets. For more detail of metric IGD, refer to [57,58].…”

Section: ) Benchmark Test Problems and Performance Indicatorsmentioning

confidence: 99%

A Novel Maximin-Based Multi-Objective Evolutionary Algorithm Using One-by-One Update Scheme for Multi-Robot Scheduling Optimization

Yang

Zhang

et al. 2021

IEEE Access

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With the continuous development of E-commerce, warehouse logistics is also facing emerging challenges, including more batches of orders and shorter order processing cycles. When more orders need to be processed simultaneously, some existing task scheduling methods may not be able to give a suitable plan, which delays order processing and reduces the efficiency of the warehouse. Therefore, the intelligent warehouse system that uses autonomous robots for automated storage and intelligent order scheduling is becoming mainstream. Based on this concept, we propose a multi-robot cooperative scheduling system in the intelligent warehouse. The aim of the multi-robot cooperative scheduling system of the intelligent storage is to drive many robots in an intelligent warehouse to perform the distributed tasks in an optimal (e.g., time-saving and energy-conserved) way. In this paper, we propose a multi-robot cooperative task scheduling model in the intelligent warehouse. For this model, we design a maximin-based multi-objective algorithm, which uses a one-by-one update scheme to select individuals. In this algorithm, two indicators are devised to discriminate the equivalent individuals with the same maximin fitness value in the environmental selection process. The results on benchmark test suite show that our algorithm is indeed a useful optimizer. Then it is applied to settle the multi-robot scheduling problem in the intelligence warehouse. Simulation experiment results demonstrate the efficiency of the proposed algorithm on the realworld scheduling problem. INDEX TERMS Many-objective optimization; Multi-objective optimization; Maximin fitness function;One-by-one update scheme; Multi-robot scheduling optimization I. INTRODUCTIONRecently, with the widespread application of autonomous robots, these cheap, small and smart robots have been widely employed in the intelligent storage management of the logistic industry [1]. In principle, the basic task of the warehouse system in the intelligent storage management is to transport goods, store goods and distribute goods efficiently [2]. Accordingly, the multi-robot coordination mechanism, if being really efficient and robust, it will make the entire intelligent warehouse management system more

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An Adaptive Localized Decision Variable Analysis Approach to Large-Scale Multiobjective and Many-Objective Optimization

Cited by 201 publications

References 52 publications

Personalized Federated Learning for ECG Classification Based on Feature Alignment

Personalized Federated Learning for ECG Classification Based on Feature Alignment

Utility Optimization of Federated Learning with Differential Privacy

A Novel Maximin-Based Multi-Objective Evolutionary Algorithm Using One-by-One Update Scheme for Multi-Robot Scheduling Optimization

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