An Angle-Based Bi-Objective Optimization Algorithm for Redundancy Allocation in Presence of Interval Uncertainty

Xu, Yaoxian; Pi, Dechang; Yang, Shengxiang; Chen, Yang; Qin, Shuo; Zio, Enrico

doi:10.1109/tase.2022.3148459

Cited by 11 publications

(3 citation statements)

References 66 publications

(92 reference statements)

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“…Redundancy is the use of multiple components to perform the same function, while failover is the ability of a network to switch to a backup component automatically. That approach guarantees that if one component malfunctions, another component can seamlessly assume control and sustain the intended functionality [ 45 ]. Moreover, load balancing and monitoring help prevent any component from becoming overloaded or causing a failure by distributing traffic across multiple components and monitoring the network health tracking process [ 46 ].…”

Section: 6g Visions and Requirementsmentioning

confidence: 99%

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

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The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

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Section: 6g Visions and Requirementsmentioning

confidence: 99%

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Furthermore, since the values of L and d determine the dimensions of w and b, the MOM in ( 10) is an uncertainty optimization problem. Hence, most common multiobjective optimization algorithms, such as strength pareto evolutionary algorithm, nondominated sorting genetic algorithm-II, multiobjective evolutionary algorithm, and multiple objective particle swarm optimization [39], [40], [41], are also not suitable for…”

Section: Multiobjective Optimization For Model Trainingmentioning

confidence: 99%

A Deep Learning Feature Fusion Based Health Index Construction Method for Prognostics Using Multiobjective Optimization

et al. 2023

Self Cite

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Degradation modeling and prognostics serve as the basis for system health management. Recently, various sensors provide plentiful monitoring data that can reflect the system status. A multitude of feature fusion techniques based on multisensor data have been proposed to generate a composite health index (HI) for prognostics, which can represent the underlying degradation mechanism. Most existing methods have used linear fusion models and neglected the practical requirements for HI construction, which are insufficient to reveal the nonlinear relations among features and difficult to obtain accurate HIs for complicated systems. This study proposes a novel feature fusion-based HI construction method with deep learning and multiobjective optimization. Multiple degradation features are fused by a deep neutral network (DNN). Several desired properties that the HIs should have for prognostics are adopted to formulate the objective functions of DNN training. To balance the spatial complexity and performance of the fusion model, a multiobjective optimization model is generated for training the DNN. Then, a generalized nonlinear Wiener process model is used to predict the remaining useful life with the resulted HIs. Finally, two cases are analyzed to illustrate the effectiveness and robustness of the proposed method.

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“…In a different approach, the authors of ref. [29] present a novel angle-based interval crowding distance. Lastly, ref.…”

Section: Introductionmentioning

confidence: 99%

Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow

Zheng,

Tao,

et al. 2023

Applied Sciences

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The conventional optimal power flow (OPF) is confronted with challenges in tackling more than three objectives and the stochastic characteristics due to the uncertainty and intermittence of the RESs. However, there are few methods available that simultaneously address high-dimensional objective optimization and uncertainty handling. This paper proposes a set-based group search optimizer (SetGSO) to tackle the stochastic many-objective optimal power flow (MaOPF) of power systems penetrated with renewable energy sources. The proposed SetGSO depicts the original stochastic variables by set-based individuals under the evolutionary strategy of the basic GSO, without using repeated sampling or probabilistic information. Consequently, two metrics, hyper-volume and average imprecision, are introduced to transform the stochastic MaOPF into a deterministic bi-objective OPF, guaranteeing a much superior Pareto-optimal front. Finally, our method was evaluated on three modified bus systems containing renewable energy sources, and compared with the basic GSO using Monte Carlo sampling (GSO-MC) and a set-based genetic algorithm (SetGA) in solving the stochastic MaOPF. The numerical results demonstrate a saving of 90% of the computation time in the proposed SetGSO method compared to sampling-based approaches and it achieves improvements in both the hyper-volume and average imprecision indicators, with a maximum enhancement of approximately 30% and 7% compared to SetGA.

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An Angle-Based Bi-Objective Optimization Algorithm for Redundancy Allocation in Presence of Interval Uncertainty

Cited by 11 publications

References 66 publications

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

A Deep Learning Feature Fusion Based Health Index Construction Method for Prognostics Using Multiobjective Optimization

Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow

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