Efficient Optimization Algorithm Research Articles

An efficient hybrid differential evolution-golden jackal optimization algorithm for multilevel thresholding image segmentation.

Image segmentation is a crucial process in the field of image processing. Multilevel threshold segmentation is an effective image segmentation method, where an image is segmented into different regions based on multilevel thresholds for information analysis. However, the complexity of multilevel thresholding increases dramatically as the number of thresholds increases. To address this challenge, this article proposes a novel hybrid algorithm, termed differential evolution-golden jackal optimizer (DEGJO), for multilevel thresholding image segmentation using the minimum cross-entropy (MCE) as a fitness function. The DE algorithm is combined with the GJO algorithm for iterative updating of position, which enhances the search capacity of the GJO algorithm. The performance of the DEGJO algorithm is assessed on the CEC2021 benchmark function and compared with state-of-the-art optimization algorithms. Additionally, the efficacy of the proposed algorithm is evaluated by performing multilevel segmentation experiments on benchmark images. The experimental results demonstrate that the DEGJO algorithm achieves superior performance in terms of fitness values compared to other metaheuristic algorithms. Moreover, it also yields good results in quantitative performance metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) measurements.

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.

AN OPTIMIZATION ALGORITHM FOR SPARSE CODE MULTIPLE ACCESS CODEBOOK TO MAXIMIZE MUTUAL INFORMATION IN 5G

Sparse code multiple access (SCMA) codebook optimization algorithms strive to reduce complexity with negligible loss at the transmitting end but add more complexity at the receiver, such that the retrieved information is minimal. To optimize mutual information in 5G using SCMA, an efficient codebook optimization algorithm is presented. The SCMA signal model is considered first using a superposition modulation structure with a column expanded channel matrix. The superposition model accurately describes the link between the received signal and the codebook matrix. An iterative codebook optimization approach based on the above model is suggested to maximize (continuous output and discrete input) mutual information. This method effectively adapts random channel coefficients to multiuser channels; both additive white Gaussian noise (AWGN) and non-AWGN channels are available to perform well with the proposed technique, according to simulation findings. Furthermore, the proposed optimization algorithm for SCMA codebook works well with the message passing algorithm.

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.

Many-to-Few Decomposition: Linking R2-Based and Decomposition-Based Multiobjective Efficient Global Optimization Algorithms

Many-to-Few Decomposition: Linking R2-Based and Decomposition-Based Multiobjective Efficient Global Optimization Algorithms

БЛОЧНЫЙ ФИЛЬТР КАЛМАНА С ЛИНЕЙНОЙ ВЫЧИСЛИТЕЛЬНОЙ СЛОЖНОСТЬЮ ДЛЯ КОМПЛЕКСИРОВАННЫХ С ТЕХНИЧЕСКИМ ЗРЕНИЕМ ИНЕРЦИАЛЬНЫХ НАВИГАЦИОННЫХ СИСТЕМ

As the use of computer vision in vehicle navigation systems increases, there is a growing need for computationally efficient optimal filtering algorithms that can jointly process measurements from inertial sensors and a large number of visual information sources. Contribution. This paper proposes a new modification of the Kalman filter (the block Kalman filter) with linear computational complexity in the number of measurement information sources. A numerically stable version of the algorithm is derived using LDL-factorization of covariance matrices. Purpose of the study. The aim is to develop an LDL-factorized block Kalman filter algorithm with linear computational complexity with respect to the number of information sources in the measurement system and demonstrate its applicability in a integrated visual-inertial navigation system. Materials and Methods. This research demonstrates the algebraic equivalence of the block Kalman filter to the standard one in a specific case. A method is proposed for approximating the estimates of the block filter to those of the standard one with desired accuracy by expanding the state vector. The computational complexity of the block Kalman filter is compared to the computational complexity of standard one within a numerical experiment. Numerical modeling of the block filter operation within a navigation system is conducted for comparison with the standard filter. Results. The equations of the block LDL-factorized Kalman filter are obtained. Its linear computational complexity with respect to the number of information sources is verified. A method for approximating the estimates of the block Kalman filter to those of the standard filter by expanding the state vector is proposed and verified within the framework of navigation system simulation. Conclusion. The main theoretical properties of the block Kalman filter were confirmed in numerical experiments. Further research will explore alternative methods of forming the extended state vector of the filter; the Kalman block filter will be tested within more complex scenarios.

Curvature Regularization for Non-Line-of-Sight Imaging From Under-Sampled Data.

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes by using time-of-flight photon information after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is highly likely to be degraded due to noises and distortions. In this paper, we propose novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (signal and object)-domain curvature regularization model. In what follows, we develop efficient optimization algorithms relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, for which all solvers can be implemented on GPUs. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. Based on GPU computing, our algorithm is the most effective among iterative methods, balancing reconstruction quality and computational time. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.

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

Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions.

Most existing graph neural networks (GNNs) learn node embeddings using the framework of message passing and aggregation. Such GNNs are incapable of learning relative positions between graph nodes within a graph. To empower GNNs with the awareness of node positions, some nodes are set as anchors. Then, using the distances from a node to the anchors, GNNs can infer relative positions between nodes. However, position-aware GNNs (P-GNNs) arbitrarily select anchors, leading to compromising position awareness and feature extraction. To eliminate this compromise, we demonstrate that selecting evenly distributed and asymmetric anchors is essential. On the other hand, we show that choosing anchors that can aggregate embeddings of all the nodes within a graph is NP-complete. Therefore, devising efficient optimal algorithms in a deterministic approach is practically not feasible. To ensure position awareness and bypass NP-completeness, we propose position-sensing GNNs (PSGNNs), learning how to choose anchors in a backpropagatable fashion. Experiments verify the effectiveness of PSGNNs against state-of-the-art GNNs, substantially improving performance on various synthetic and real-world graph datasets while enjoying stable scalability. Specifically, PSGNNs on average boost area under the curve (AUC) more than 14% for pairwise node classification and 18% for link prediction over the existing state-of-the-art position-aware methods. Our source code is publicly available at: https://github.com/ZhenyueQin/PSGNN.

Noise and distortion suppression for industrial confocal microscopy

Industrial confocal microscopy is an optical imaging measurement technology used in the fields of material research, defect detection and industrial inspection. Noise will affect the quality of the image and thus affect its performance. We propose a denoising algorithm based on CNN minimisation in this paper. First, we give the object function of the algorithm and the adaptive calculation formula of the capped value. Then, by using the difference convex (DC) optimisation algorithm framework, the sub-gradient optimisation condition, and the feedback framework, we develop a simple and efficient adaptive-stop iterative optimisation algorithm to solve this model and provide the closed solution of the model at each iteration. The experimental results show that for industrial confocal images, our algorithm can not only effectively suppress noise, but also effectively avoid the distortion of denoised images. The signal-to-noise ratio of industrial confocal denoised images is increased by 1.93 times. In addition, experiments show that the algorithm has good noise reduction ability in other optical imaging instruments, and can suppress not only Gaussian noise, but also Gaussian-Poisson noise.

Optimized aspect and self-attention aware LSTM for target-based semantic analysis (OAS-LSTM-TSA)

PurposeAspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.Design/methodology/approachThis research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.FindingsTo detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.Originality/valueThe novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.

An aerodynamic optimization method based on virtual Nash equilibrium for global and local parameter decoupling

The parameterization of aerodynamic shape in the aircraft design optimization usually includes both global and local configuration features. The global parameters determine the wing span, aspect ratio, angle of attack, twist angle, and sweep angle. Meanwhile, the local parameters define the geometric shape of the wing section, including thickness and camber, etc. The changes in different types of parameters during the optimization process will lead to perturbations in the corresponding wavelength of the geometric shape, which in turn will cause different spatial frequency responses in the flow field. Generally, local parameter changes will generate high-frequency perturbations in the flow field and propagate only near the disturbance source; While global parameter changes will cause low-frequency perturbations in the flow field and propagate throughout the entire flow field. If these two different types of parameters are optimized at all once, it will lead to significant mutual interference, thereby reducing optimization efficiency. Therefore, this paper proposes an efficient optimization algorithm NashGL based on the concept of virtual Nash equilibrium. It decouples global and local variables and optimizes them simultaneously on different populations, effectively isolating interference between these two types of design variables. On the other hand, due to the small number of global variables, their rapid convergence can guide local variables towards the optimal solution. Through two aerodynamic optimization cases of NACA0012 airfoil and wing body aircraft, it is shown that compared with the method of simultaneously optimizing global and local parameters, the NashGL algorithm in this paper has the highest optimization efficiency, with an improvement of about 30% and 60%, respectively.

Multi-objective topology optimization of heat transfer surface using level-set method and adaptive mesh refinement in OpenFOAM

The present study proposes a new efficient and robust algorithm for multi-objectives topology optimization of heat transfer surfaces to achieve heat transfer enhancement with a less pressure drop penalty based on a continuous adjoint approach. It is achieved with a customized OpenFOAM solver, which is based on a volume penalization method for solving a steady and laminar flow around iso-thermal solid objects with arbitrary geometries. The fluid-solid interface is captured by a level-set function combined with a newly proposed robust reinitialization scheme ensuring that the interface diffusion is always kept within a single local grid spacing. Adaptive mesh refinement is applied in near-wall regions automatically detected by the level-set function to keep high resolution locally, thereby reduces the overall computational cost for the forward and adjoint analyses. The developed solver is first validated in a drag reduction problem of a flow around a two-dimensional cylinder at the Reynolds numbers of 10 and 40 by comparing reference data. Then, the proposed scheme is extended to heat transfer problems in a two-dimensional flow at the Prandtl number of 0.7 and 6.9. Finally, three-dimensional topology optimization for multi-objective problems is considered for cost functionals with different weights for the total drag and heat transfer. Among various solutions obtained on the Pareto front, 4.0% of heat transfer enhancement with 12.6% drag reduction is achieved at the Reynolds number of 10 and the Prandtl number of 6.9. Moreover, the optimization of a staggered pin-fin array demonstrates that the optimal shapes and arrangement of the fins strongly depend on the number of rows from the inlet. Specifically, the pin-fins in the first and third rows extended in the upstream direction further enhance heat transfer, while the fins in the second row vanish to reduce pressure loss.

Advancements in Optimal Sensor Placement for Enhanced Structural Health Monitoring: Current Insights and Future Prospects

Structural health monitoring (SHM) is critical to maintaining safe and reliable civil infrastructure, but the optimal design of an SHM sensing system, i.e., optimal sensor placement (OSP), remains a complex challenge. Based on the existing literature, this paper presents a comprehensive review of OSP strategies for SHM. It covers the key steps in OSP, from evaluation criteria to efficient optimization algorithms. The evaluation criteria are classified into six groups, while the optimization algorithms are roughly categorized into three classes. The advantages and disadvantages of each group of methods have been summarized, aiming to benefit the OSP strategy selection in future projects. Then, the real-world implementation of OSP on bridges, high-rise buildings, and other engineering structures, is presented. Based on the current progress, the challenges of OSP are recognized; its future development directions are recommended. This study equips researchers/practitioners with an integrated perspective on state-of-the-art OSP. By highlighting key developments, persistent challenges, and prospects, it is expected to bridge the gap between theory and practice.

IBJA: An improved binary DJaya algorithm for feature selection

Feature Selection (FS) is a special preprocessing step in Machine Learning (ML) that reduces the number of unwanted features in datasets to increase the accuracy of ML classifiers. A popular binary variant of the continuous Jaya algorithm is the Discrete Jaya (DJaya) algorithm. It is commonly used for addressing optimization problems with binary design variables (also known as binary decision variables). Nevertheless, DJaya tends to prematurely converge to local optimal solutions, and its performance deteriorates as the complexity of the optimization problem grows. The DJaya algorithm is improved in this article by introducing the Improved Binary DJaya Algorithm (IBJA), which is specially designed to solve the FS problem. IBJA includes three techniques in DJaya. First, it incorporates the update equation of the Harris Hawks Optimization (HHO) algorithm into the optimization loop of DJaya to enhance DJaya’s searching process and exploration abilities. Second, it uses a new Dynamic Opposition-based Learning in the final steps of the optimization loop of DJaya to boost its searching and exploring capabilities. Third, it employs binary transfer functions to calculate binary solutions from the real-valued solutions generated by HHO and DOBL. Using 15 UCI datasets, IBJA’s performance was assessed and compared against four ML classifiers and ten efficient optimization algorithms. Besides, the Friedman statistical test was employed to investigate the reliability of the experimental findings. According to the overall experimental and statistical findings, IBJA scored the highest accuracy, best objective value, and fewest chosen features for each of the 15 UCI datasets.

Optimal floating offshore wind farms for Mediterranean islands

Offshore wind will play an important role in achieving the Green Deal's goals of green transition and renewable energy generation. The European Union (EU) has launched an initiative to support a clean energy transition towards sustainable and renewable energy production in the EU islands. In fact, the Mediterranean islands obtain most of their energy supply from imported fossil fuels, making electricity costs generally very high due to their remote location. In this paper, we compare the optimal offshore wind farm layout, type and number of wind turbines for four selected sites next to Mediterranean islands. Each layout is evaluated in terms of levelised cost of energy, taking into account aerodynamic wake and transmission losses in the productivity estimation and a detailed cost model. Aerodynamic losses are modelled using a Jensen kinematic wake model and an area overlapping model to consider partial wake shadowing between wind turbines, while transmission losses are estimated as Ohmic losses of the inter-array and export transmission cables. Compared to the state-of-the-art PyWake code, the in-house MATLAB wake model demonstrated higher computational efficiency and is integrated into an efficient optimisation algorithm consisting of several iterations of the Interior-point optimisation algorithm. Results show that offshore wind turbines can provide renewable energy at a competitive price, especially at the most energetic site with an LCOE about 80 €/MWh. The normalised optimal LCOE and capacity factor as a function of the number of wind turbines are not significantly influenced by the chosen location and reach a maximum difference of 2–3 %, showing that they mainly depend on the type of wind turbine. The lowest optimal LCOE depending on the number of wind turbines is reduced by about 10 % and 25 % by the higher rated wind turbine.

Computational Design of Flexible Planar Microstructures

Mechanical metamaterials enable customizing the elastic properties of physical objects by altering their fine-scale structure. A broad gamut of effective material properties can be produced even from a single fabrication material by optimizing the geometry of a periodic microstructure tiling. Past work has extensively studied the capabilities of microstructures in the small-displacement regime, where periodic homogenization of linear elasticity yields computationally efficient optimal design algorithms. However, many applications involve flexible structures undergoing large deformations for which the accuracy of linear elasticity rapidly deteriorates due to geometric nonlinearities. Design of microstructures at finite strains involves a massive increase in computation and is much less explored; no computational tool yet exists to design metamaterials emulating target hyperelastic laws over finite regions of strain space. We make an initial step in this direction, developing algorithms to accelerate homogenization and metamaterial design for nonlinear elasticity and building a complete framework for the optimal design of planar metamaterials. Our nonlinear homogenization method works by efficiently constructing an accurate interpolant of a microstructure's deformation over a finite space of macroscopic strains likely to be endured by the metamaterial. From this interpolant, the homogenized energy density, stress, and tangent elasticity tensor describing the microstructure's effective properties can be inexpensively computed at any strain. Our design tool then fits the effective material properties to a target constitutive law over a region of strain space using a parametric shape optimization approach, producing a directly manufacturable geometry. We systematically test our framework by designing a catalog of materials fitting isotropic Hooke's laws as closely as possible. We demonstrate significantly improved accuracy over traditional linear metamaterial design techniques by fabricating and testing physical prototypes.

Two-step affinity matrix learning for multi-view subspace clustering

Multi-view subspace clustering aims to learn an appropriate affinity matrix to investigate the relationship between data. However, the learned affinity matrix always has limited representation ability and unclear intrinsic structure. To address the problem, we propose a novel two-step learning strategy to enhance the quality of the affinity matrix. First, we design a generalized framework to calculate the affinity matrix for each view. Unlike most subspace clustering methods that focus on constructing the coefficient matrices, the proposed framework combines the subspace-based and graph-based models to simultaneously learn the coefficient and similarity matrices under a consistency constraint. Thus, the data structure information and multiple views correlation can be captured and propagated between these matrices, which is conducive to generating an informative affinity matrix. Further, we present a post-processing technology called unified good neighbors learning to make the obtained affinity matrix have good sparsity and connectivity. Specifically, we find the good neighbors of each sample by all the views and reassign their coefficients in the final affinity matrix to reflect the essential correlation between samples. Finally, we develop an efficient optimization algorithm and provide the theoretical analyses for the proposed problem. Extensive experiments conducted on six popular multi-view datasets demonstrate the effectiveness of our method. The source code is available at https://github.com/taozhang1223/TAML.