Novel General Active Reliability Redundancy Allocation Problems and
Algorithm
- URL: http://arxiv.org/abs/2109.13659v1
- Date: Wed, 18 Aug 2021 11:54:42 GMT
- Title: Novel General Active Reliability Redundancy Allocation Problems and
Algorithm
- Authors: Wei-Chang Yeh
- Abstract summary: The reliability redundancy allocation problem (RRAP) is used to maximize system reliability.
A novel RRAP, called the general RRAP (GRRAP), is proposed to extend the series-parallel structure or bridge network to a more general network structure.
To solve the proposed novel GRRAP, a new algorithm, called the BAT-SSOA3, used the simplified swarm optimization (SSO) to update solutions.
- Score: 1.5990720051907859
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The traditional (active) reliability redundancy allocation problem (RRAP) is
used to maximize system reliability by determining the redundancy and
reliability variables in each subsystem to satisfy the volume, cost, and weight
constraints. The RRAP structure is very simple, that is, redundant components
are parallel in each subsystem, and all subsystems are either connected in
series or in a bridge network. Owing to its important and practical
applications, a novel RRAP, called the general RRAP (GRRAP), is proposed to
extend the series-parallel structure or bridge network to a more general
network structure. To solve the proposed novel GRRAP, a new algorithm, called
the BAT-SSOA3, used the simplified swarm optimization (SSO) to update
solutions, the small-sampling tri-objective orthogonal array (SS3OA) to tune
the parameters in the proposed algorithm, the binary-addition-tree algorithm
(BAT) to calculate the fitness (i.e., reliability) of each solution, and the
penalty function to force infeasible back to the feasible region. To validate
the proposed algorithm, the BAT-SSOA3 is compared with state-of-the-art
algorithms, such as, particle swarm optimization (PSO) and SSO, via designed
experiments and computational studies.
Related papers
- Distributed Noncoherent Joint Transmission Based on Multi-Agent Reinforcement Learning for Dense Small Cell MISO Systems [8.146481327854545]
We consider a dense small cell (DSC) network where multi-antenna small cell base stations (SBSs) transmit data over a shared band.
arXiv Detail & Related papers (2024-08-22T02:11:14Z) - Joint User Association, Interference Cancellation and Power Control for
Multi-IRS Assisted UAV Communications [80.35959154762381]
Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications are expected to alleviate the load of ground base stations in a cost-effective way.
Existing studies mainly focus on the deployment and resource allocation of a single IRS instead of multiple IRSs.
We propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation.
arXiv Detail & Related papers (2023-12-08T01:57:10Z) - Fairness-Driven Optimization of RIS-Augmented 5G Networks for Seamless
3D UAV Connectivity Using DRL Algorithms [8.296140341710462]
We study the problem of joint active and passive beamforming for reconfigurable intelligent surface (RIS)-assisted massive multiple-input multiple-output systems.
We propose two novel algorithms to address this problem.
arXiv Detail & Related papers (2023-11-14T06:43:36Z) - Fidelity-Guarantee Entanglement Routing in Quantum Networks [64.49733801962198]
Entanglement routing establishes remote entanglement connection between two arbitrary nodes.
We propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (SD) pairs in quantum networks.
arXiv Detail & Related papers (2021-11-15T14:07:22Z) - A Novel Simplified Swarm Optimization for Generalized Reliability
Redundancy Allocation Problem [1.2043574473965315]
This study proposes a novel RRAP called General RRAP (GRRAP) to be applied to network systems.
Since GRRAP is an NP-hard problem, a new algorithm called Binary-addition simplified swarm optimization (BSSO) is also proposed in this study.
arXiv Detail & Related papers (2021-10-01T00:12:11Z) - Learning to Beamform in Heterogeneous Massive MIMO Networks [48.62625893368218]
It is well-known problem of finding the optimal beamformers in massive multiple-input multiple-output (MIMO) networks.
We propose a novel deep learning based paper algorithm to address this problem.
arXiv Detail & Related papers (2020-11-08T12:48:06Z) - Resource Allocation via Model-Free Deep Learning in Free Space Optical
Communications [119.81868223344173]
The paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications.
Under this framework, we propose two algorithms that solve FSO resource allocation problems.
arXiv Detail & Related papers (2020-07-27T17:38:51Z) - Simplified Swarm Optimization for Bi-Objection Active Reliability
Redundancy Allocation Problems [1.5990720051907859]
The reliability redundancy allocation problem (RRAP) is a well-known problem in system design, development, and management.
In this study, a bi-objective RRAP is formulated by changing the cost constraint as a new goal.
To solve the proposed problem, a new simplified swarm optimization (SSO) with a penalty function, a real one-type solution structure, a number-based self-adaptive new update mechanism, a constrained non-dominated solution selection, and a new pBest replacement policy is developed.
arXiv Detail & Related papers (2020-06-17T13:15:44Z) - Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding
Design for Multiuser MIMO Systems [59.804810122136345]
We propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed.
An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed.
We show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.
arXiv Detail & Related papers (2020-06-15T02:57:57Z) - RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment
and Passive Beamforming Design [116.88396201197533]
A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS)
The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency.
A novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset.
A decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS.
arXiv Detail & Related papers (2020-01-28T14:37:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.