Efficient Optimization Algorithm Research Articles

T[formula omitted]LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstruction

The tensor low-rank prior has attracted considerable attention in dynamic MR reconstruction. Tensor low-rank methods preserve the inherent high-dimensional structure of data, allowing for improved extraction and utilization of intrinsic low-rank characteristics. However, most current methods are still confined to utilizing low-rank structures either in the image domain or predefined transformed domains. Designing an optimal transformation adaptable to dynamic MRI reconstruction through manual efforts is inherently challenging. In this paper, we propose a deep unrolling network that utilizes the convolutional neural network (CNN) to adaptively learn the transformed domain for leveraging tensor low-rank priors. Under the supervised mechanism, the learning of the tensor low-rank domain is directly guided by the reconstruction accuracy. Specifically, we generalize the traditional t-SVD to a transformed version based on arbitrary high-dimensional unitary transformations and introduce a novel unitary transformed tensor nuclear norm (UTNN). Subsequently, we present a dynamic MRI reconstruction model based on UTNN and devise an efficient iterative optimization algorithm using ADMM, which is finally unfolded into the proposed T2LR-Net. Experiments on two dynamic cardiac MRI datasets demonstrate that T2LR-Net outperforms the state-of-the-art optimization-based and unrolling network-based methods.

An efficient algorithm for resource optimization in IRS-mmWave-NOMA B5G wireless networks

The effectiveness of implementing intelligent reflecting surface (IRS) for millimeter-wave (mmWave)-non-orthogonal multiple-access (NOMA) systems has allowed for significant sum-rate improvements. The majority of recent research has not discussed how well the IRS-mmWave-NOMA combination performs. Therefore, a new technique for resource optimization in IRS-mmWave-NOMA B5G wireless networks is proposed in this research. The key concept is to use an iterative algorithm to solve the optimization issue while incorporating many crucial constraints like the selection of the IRS beam, transmit power distribution, and decoding order, among others. Simulation results show that the proposed approach outperforms existing state-of-the-art algorithms in terms of computation delay, sum rate and NMSE. The computational complexity also validated the simplicity and hardware-friendly feature of the proposed algorithm.

Covariance matrix adaptation evolution strategy based on correlated evolution paths with application to reinforcement learning

Proven as an efficient population-based optimization algorithm, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) features two evolution paths, one to update the covariance matrix and the other to adapt its mutation strength. Considering the time and space complexity of CMA-ES, there are several attempts in the literature to realize a single-path algorithm. However, such attempts require altering the original structure of CMA-ES and consequently eliminating some vital features crucial to the overall algorithm performance. In this paper, we show that the two evolution paths of CMA-ES are highly correlated and one can be expressed in terms of the other thus reducing the computational cost of the algorithm while preserving the original algorithmic framework. Based on experimental studies conducted using 30 functions from the IEEE CEC 2014 benchmark suite, the proposed algorithm shows comparable results with the standard CMA-ES as well as five other state-of-the-art CMA-ES variants. Furthermore, it is shown that the proposed algorithm can be applied to policy search in Deep Reinforcement Learning (DRL). Performance results based on selected DRL problems from different application domains prove the efficiency of the proposed algorithm compared to other population-based algorithms often applied for policy search in DRL.

DITRA: an efficient event-driven multi-objective optimization algorithm for bandwidth allocation in IoT environments

DITRA: an efficient event-driven multi-objective optimization algorithm for bandwidth allocation in IoT environments

Exploring optimization algorithms for establishing patient-based real-time quality control models

BackgroundPatient-based real-time quality control (PBRTQC) models must be optimized for use in different clinical laboratories, but the grid search (GS) algorithm explored in recent studies for this purpose is inefficient. Thus, finding an efficient optimization algorithm is critical for future research and implementation of the PBRTQC. MethodsWe compared the efficiency and performance of five commonly used optimization algorithms, including GS, simulated annealing (SA), genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO), to optimize conventional PBRTQC and regression-adjusted real-time quality control (RARTQC) models for serum alanine aminotransferase and sodium. ResultsThe GS, GA, DE, and PSO provided models with similar performances. However, GA and DE required significantly less computation time than GS. The results also demonstrate a general tradeoff between the optimization method's chance of discovering the optimum and the computation time required. ConclusionMore efficient optimization methods should be adopted when establishing PBRTQC or RARTQC models to save time and computing power that will enable the development of more complex models and increase the scalability of extensive PBRTQC applications.

Mean-based Borda count for paradox-free comparisons of optimization algorithms

Numerical comparisons are essential for selecting an efficient optimization algorithm for specific problems arising from various human practices. Note that recent researches have shown that paradoxes may occur for comparisons of the numerical performance of optimization algorithms, particularly the cycle-ranking paradox. Paradox-free is still open for some popular data analysis methods based on hypothesis testing (HT), which motivates us to design a class of HT-based paradox-free data analysis methods. The numerical comparison of optimization algorithms is analyzed for dimensional reduction in a matrix. The data collected during the experiment is stored in a four-dimensional matrix, which is then reduced to three-dimensional, two-dimensional, and finally a one-dimensional ranking vector. Then a mean-based Borda count (MeanBordaCount/T) is proposed to eliminate the cycle-ranking paradox that arises from the HT-based data analysis methods where HTs are performed on each problem. Specifically, hypothesis testing is replaced primarily by mean comparison, which has been proven that the result of hypothesis testing is the same as the result based only on mean comparisons, except that the former contains more equal or tied results. The Borda count is adopted to eliminate cycle-ranking in the final dimensional reduction. Finally, MeanBordaCount/T is proved to be the best choice among all HT-type methods, at least in the sense that it can minimize the error of pairwise comparisons.

Bayesian optimization of gradient trajectory for parallel-transmit pulse design.

Spoke pulses improve excitation homogeneity in parallel-transmit MRI. We propose an efficient global optimization algorithm, Bayesian optimization of gradient trajectory (BOGAT), for single-slice and simultaneous multislice imaging. BOGAT adds an outer loop to optimize kT-space positions. For each position, the RF coefficients are optimized (e.g., with magnitude least squares) and the cost function evaluated. Bayesian optimization progressively estimates the cost function. It automatically chooses the kT-space positions to sample, to achieve fast convergence, often coming close to the globally optimal spoke positions. We investigated the typical features of spokes cost functions by a grid search with field maps comprising 85 slabs from 14 volunteers. We tested BOGAT in this database, and prospectively in a phantom and in vivo. We compared the vendor-provided Fourier transform approach with the same magnitude least squares RF optimizer. The cost function is nonconvex and seen empirically to be piecewise smooth with discontinuities where the underlying RF optimum changes sharply. BOGAT converged to within 10% of the global minimum cost within 30 iterations in 93% of slices in our database. BOGAT achieved up to 56% lower flip angle RMS error (RMSE) or 55% lower pulse energy in phantoms versus the Fourier transform approach, and up to 30% lower RMSE and 29% lower energy in vivo with 7.8 s extra computation. BOGAT efficiently estimated near-global optimum spoke positions for the two-spoke tests, reducing flip-angle RMSE and/or pulse energy in a computation time (˜10 s), which is suitable for online optimization.

Hybrid Optimization for High Aspect Ratio Wings with Convolutional Neural Networks and Squirrel Optimization Algorithm

An efficient hybrid optimization algorithm is introduced in this paper to optimize the lightweight design of high-aspect-ratio wings, tackling the complexities associated with the mixed optimization design of layout and size variables within these wing structures. A hybrid binary unified coding description facilitates the optimization process for layout and size variables. The study influences one-dimensional convolutional neural networks to establish an aeroelastic surrogate model, primarily chosen for their exceptional performance in handling multi-parameter aeroelastic regression problems. Additionally, the squirrel optimization algorithm is chosen over the genetic algorithm for the mixed optimization problem, leading to notable savings in computational costs. The research demonstrates that the proposed hybrid optimization method, integrating the one-dimensional convolutional neural network and the squirrel optimization algorithm, offers superior performance in optimizing high aspect ratio wings. Specifically, it results in a reduction of 4.1% in the weight of the wing structure. Moreover, the study highlights the necessity of this hybrid approach due to the observed coupling between the layout variables of the wing ribs and the size variables of the wing beams.

Football team training algorithm: A novel sport-inspired meta-heuristic optimization algorithm for global optimization

A more efficient optimization algorithm has always been the pursuit of researchers, but the performance of the current optimization algorithm in some complex test functions is not always satisfactory. In order to solve this problem, a new meta-heuristic optimization algorithm—Football Team Training Algorithm (FTTA) is proposed according to the training method of the football team, which simulates the three stages of the training session: Collective Training, Group Training and Individual Extra Training. By the test on two groups of test functions, CEC2005 and CEC2020, the proposed optimization algorithm (FTTA) achieves the best results, which far exceeds the traditional Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA) algorithms and so on. In the engineering application, a new hybrid wind speed prediction system is proposed based on FTTA. The FTTA is used to optimize variational mode decomposition (VMD) to improve the effect of data denoising. At the same time, based on unconstrained weighting algorithm, FTTA and combination prediction model build a new hybrid prediction strategy. Through the experiments on four groups of wind speed data in Dalian, the accuracy, stability, advancement, and CPU running speed of the system are verified. It is obvious that the practical application ability of the system is much better than previous methods, which can effectively improve the utilization efficiency of renewable energy.

Estimating Functional Brain Networks by Low-Rank Representation With Local Constraint.

The functional architecture undergoes alterations during the preclinical phase of Alzheimer's disease. Consequently, the primary research focus has shifted towards identifying Alzheimer's disease and its early stages by constructing a functional connectivity network based on resting-state fMRI data. Recent investigations show that as Alzheimer's Disease (AD) progresses, modular tissue and connections in the core brain areas of AD patients diminish. Sparse learning methods are powerful tools for understanding Functional Brain Networks (FBNs) with Regions of Interest (ROIs) and a connectivity matrix measuring functional coherence between them. However, these tools often focus exclusively on functional connectivity measures, neglecting the brain network's modularity. Modularity orchestrates dynamic activities within the FBN to execute intricate cognitive tasks. To provide a comprehensive delineation of the FBN, we propose a local similarity-constrained low-rank sparse representation (LSLRSR) method that encodes modularity information under a manifold-regularized network learning framework and further formulate it as a low-rank sparse graph learning problem, which can be solved by an efficient optimization algorithm. Specifically, for each modularity structure, the Schatten p-norm regularizer reduces the reconstruction error and provides a better approximation of the low-rank constraint. Furthermore, we adopt a manifold-regularized local similarity prior to infer the intricate relationship between subnetwork similarity and modularity, guiding the modeling of FBN. Additionally, the proximal average method approximates the joint solution's proximal map, and the resulting nonconvex optimization problems are solved using the alternating direction multiplier method (ADMM). Compared to state-of-the-art methods for constructing FBNs, our algorithm generates a more modular FBN. This lays the groundwork for further research into alterations in brain network modularity resulting from diseases.

Simple population-based algorithms for solving optimization problems

Heuristic algorithms are simple yet powerful tools that are capable of yielding acceptable results in a reasonable execution time. Hence, they are being extensively used for solving optimization problems by researchers nowadays. Due to the quantum of computing power and hardware available today, a large number of dimensions and objectives are considered and analyzed effectively. This paper proposes new population-based metaheuristic algorithms that are capable of combining different strategies. The new strategies help in fast converging as well as trying to avoid local optima. The proposed algorithms could be used as single-phase as well as two-phase algorithms with different combinations and tuning parameters. “Best”, “Mean” and “Standard Deviation” are computed for thirty trials in each case. The results are compared with many efficient optimization algorithms available in the literature. Sixty-one popular un-constrained benchmark problems with dimensions varying from two to thousand and fifteen constrained real-world engineering problems are used for the analyses. The results show that the new algorithms perform better for several test cases. The suitability of the new algorithms for solving multi-objective optimization problems is also studied using five numbers of two-objective ZDT problems. Pure Diversity, Spacing, Spread and Hypervolume are the metrics used for the evaluation.

An Efficient Metaheuristic algorithm for optimization for Location of Optimum Connectivity Services with Drones in Healthcare

Drones are being extensively used for a variety of practical purposes, including providing medical care. For instance, giving vaccinations, healthcare equipment, and samples of blood to individuals in isolated locations as well as during calamities. This study examined an efficient metaheuristic algorithm called MAP-LOCS, which optimizes the location of optimal connectivity amenities using drones. The use of drones in relation to facility placement and navigation is the issue. Within certain drone range limits, it entails choosing drone launch spots to optimize patient care availability. This work develops a heuristic called Location of Optimal Connection Services. The algorithm's basic premise is to start by randomly selecting a few services to open from among those that are most capable of meeting patient requests. Following that, patients are matched with the nearest open facility that can accommodate them.

Optimization study for RF cavity and beam dynamics simulation in the electron linear accelerator using the multi-objective genetic algorithm

This work presents an optimization study on the RF cavity geometry design and beam dynamics of an electron injector linac for the Korea Fourth-Generation Storage Ring (Korea-4GSR). It is challenging to achieve an optimal design for a linac system due to the nonlinear nature of accelerator system and the associated beam dynamics with numerous knobs to control. To address this challenge, we employed the Multi-Objective Genetic Algorithm (MOGA) for efficient optimization. Through the utilization of MOGA, we effectively optimized the electron linac for the Korea-4GSR. The optimized design yielded a horizontal normalized rms emittance of approximately 3.79 mm-mrad and an energy spread of about 307.37 keV, which met design requirements of the Korea-4GSR. This approach facilitated the design of an electron linac tailored for the Korea-4GSR with enhanced efficiency and effectiveness.

Age of Information for Partial Earliest Relay Aided Short Packet Status Update With Energy Harvesting

This paper focuses on the timely delivery of status updates for a multi-relay assisted energy harvesting (EH) Internet of Things system with short packet communications (SPC), where an EH-powered source transmits status updates to a destination with the aid of multiple EH-powered relays. To improve the energy efficiency and information freshness, an earliest-ℓ partial relay selection scheme is proposed, in which the earliest ℓ relays that successfully receive the status update from the source will be selected to forward the status update to the destination, and the selection combining (SC) and maximal-ratio combining (MRC) schemes are used at the destination. Firstly, we investigate the update packet delivery schemes of source-relay (S-R) and relay-destination (R-D) links, and present the packet error probabilities under the SC and MRC schemes based on SPC theory. Secondly, after characterizing the full charge time durations of both the source and relay, we investigate the statistical description of the number of the packet transmission attempts from the source to relay as well as the one from the relay to destination by using order statistics. With the analysis of the evolution of the instantaneous age of information (AoI), the expression for average AoI is derived. Finally, we investigate the average AoI minimization, which is formulated as a coupled triplet problem of the packet lengths at the source and relays and the value of ℓ. To overcome the coupling, we present the convexity approximation of the average AoI as well as the concise proof. Then, an efficient gradient-based two-step optimal search algorithm is proposed to solve the optimization problem, which searches the local optimal packet lengths for S-R and R-D links by iteratively updating the value of ℓ until the global optimal objective parameters are found. Numerical analyses give the effect of the key parameters on the average AoI, and the comparison demonstrates that the proposed gradient-based method converges quickly than the conventional greedy search method.

BinCOA: An Efficient Binary Crayfish Optimization Algorithm for Feature Selection

BinCOA: An Efficient Binary Crayfish Optimization Algorithm for Feature Selection

Automatic design of mutation parameter adaptation for differential evolution

In this paper the Efficient Global Optimization algorithm is applied to design the adaptation strategy for mutation parameter in Differential Evolution. The adaptation strategy is represented as a Taylor series, to allow exploring a search space of different curves. The tuning of the adaptation is performed on the L-NTADE algorithm using the benchmark of Congress on Evolutionary Computation competition on single-objective numerical optimization 2017. The experimental results show that the discovered dependence between the success rate and the parameter in current-to-pbest mutation strategy allows improving the algorithm performance in various cases.

Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer's Disease Diagnosis.

In Alzheimer's disease (AD) diagnosis, joint feature selection for predicting disease labels (classification) and estimating cognitive scores (regression) with neuroimaging data has received increasing attention. In this paper, we propose a model named Shared Manifold regularized Joint Feature Selection (SMJFS) that performs classification and regression in a unified framework for AD diagnosis. For classification, unlike the existing works that build least squares regression models which are insufficient in the ability of extracting discriminative information for classification, we design an objective function that integrates linear discriminant analysis and subspace sparsity regularization for acquiring an informative feature subset. Furthermore, the local data relationships are learned according to the samples' transformed distances to exploit the local data structure adaptively. For regression, in contrast to previous works that overlook the correlations among cognitive scores, we learn a latent score space to capture the correlations and employ the latent space to design a regression model with l2,1 -norm regularization, facilitating the feature selection in regression task. Moreover, the missing cognitive scores can be recovered in the latent space for increasing the number of available training samples. Meanwhile, to capture the correlations between the two tasks and describe the local relationships between samples, we construct an adaptive shared graph to guide the subspace learning in classification and the latent cognitive score learning in regression simultaneously. An efficient iterative optimization algorithm is proposed to solve the optimization problem. Extensive experiments on three datasets validate the discriminability of the features selected by SMJFS.

Performance Assessment of an ITU-T Compliant Machine Learning Enhancements for 5G RAN Network Slicing

Network slicing is a technique introduced by 3GPP to enable multi-tenant operation in 5 G systems. However, the support of slicing at the air interface requires not only efficient optimization algorithms operating in real time but also its tight integration into the 5 G control plane. In this paper, we first present a priority-based mechanism enabling defined performance isolation among slices competing for resources. Then, to speed up the resource arbitration process, we propose and compare several supervised machine learning (ML) techniques. We show how to embed the proposed approach into the ITU-T standardized ML architecture. The proposed ML enhancement is evaluated under realistic traffic conditions with respect to the performance criteria defined by GSMA while explicitly accounting for 5 G millimeter wave channel conditions. Our results show that ML techniques are able to provide suitable approximations for the resource allocation process ensuring slice performance isolation, efficient resource use, and fairness. Among the considered algorithms, polynomial regressions show the best results outperforming the exact solution algorithm by 5–6 orders of magnitude in terms of execution time and both neural network and random forest algorithms in terms of accuracy (by 20–40 %), sensitiveness to workload variations and training sample size. Finally, ML algorithms are generally prone to service level agreements (SLA) violation under high load and time-varying channel conditions, implying that an SLA enforcement system is needed in ITU-T's 5 G ML framework.

Parameter-Free Multiview K -Means Clustering With Coordinate Descent Method.

Recently, more and more real-world datasets have been composed of heterogeneous but related features from diverse views. Multiview clustering provides a promising attempt at a solution for partitioning such data according to heterogeneous information. However, most existing methods suffer from hyper-parameter tuning trouble and high computational cost. Besides, there is still an opportunity for improvement in clustering performance. To this end, a novel multiview framework, called parameter-free multiview k -means clustering with coordinate descent method (PFMVKM), is presented to address the above problems. Specifically, PFMVKM is completely parameter-free and learns the weights via a self-weighted scheme, which can avoid the intractable process of hyper-parameters tuning. Moreover, our model is capable of directly calculating the cluster indicator matrix, with no need to learn the cluster centroid matrix and the indicator matrix simultaneously as previous multiview methods have to do. What's more, we propose an efficient optimization algorithm utilizing the idea of coordinate descent, which can not only reduce the computational complexity but also improve the clustering performance. Extensive experiments on various types of real datasets illustrate that the proposed method outperforms existing state-of-the-art competitors and conforms well with the actual situation.

D2D Spectrum Efficiency Optimization Algorithm for Many-to-One Reusing Scenarios

D2D Spectrum Efficiency Optimization Algorithm for Many-to-One Reusing Scenarios