Iterative Optimization Research Articles

3D Bioprinting and Artificial Intelligence-Assisted Biofabrication of Personalized Oral Soft Tissue Constructs.

Regeneration of oral soft tissue defects, including mucogingival defects associated with the recession or loss of gingival and/or mucosal tissues around teeth and implants, is crucial for restoring oral tissue form, function, and health. This study presents a novel approach using three-dimensional (3D) bioprinting to fabricate individualized grafts with precise size, shape, and layer-by-layer cellular organization. A multicomponent polysaccharide/fibrinogen-based bioink is developed, and bioprinting parameters are optimized to create shape-controlled oral soft tissue (gingival) constructs. Rheological, printability, and shape-fidelity assays, demonstrated the influence of thickener concentration and print parameters on print resolution and shape fidelity. Artificial intelligence (AI)-derived tool enabled streamline the iterative bioprinting parameter optimization and analysis of the interaction between the bioprinting parameters. The cell-laden polysaccharide/fibrinogen-based bioinks exhibited excellent cellular viability and shape fidelity of shape-controlled, full-thickness gingival tissue constructs over the 18-day culture period. While variations in thickener concentrations within the bioink minimally impact the cellular organization and morphogenesis (gingival epithelial, connective tissue, and basement membrane markers), they influence the shape fidelity of the bioprinted constructs. This study represents a significant step toward the biofabrication of personalized soft tissue grafts, offering potential applications in the repair and regeneration of mucogingival defects associated with periodontal disease and dental implants.

ACCELERATED SUBSPACE ITERATION METHOD FOR SIMULATING VARYING EIGENPAIRS OF THIN-WALLED WORKPIECES

This paper presents an innovative accelerated subspace iteration method tailored for simulating varying eigenpairs of thin-walled workpieces. The proposed approach significantly boosts computational efficiency by selecting optimal start iteration vectors for a given cutter location. The optimal start iteration vectors are generated based on the previously calculated eigenvectors from the preceding cutter location. A numerical example illustrates the efficiency enhancement achieved by the proposed method, showcasing a notable reduction in computation time. Furthermore, to evaluate the numerical accuracy of the accelerated subspace iteration method, comprehensive comparisons are conducted with results obtained from Abaqus simulations. The outcomes affirm the numerical accuracy of the proposed approach.

Adaptive blind control strategy based on iterative optimization to minimize cooling and lighting demands in office space

Adaptive blind control strategy based on iterative optimization to minimize cooling and lighting demands in office space

Optimization of Low-Loss, High-Birefringence, Single-Layer, Annular, Hollow, Anti-Resonant Fiber Using a Surrogate Model-Assisted Gradient Descent Method

This paper proposes a novel optimization method for hollow-core, anti-resonant fiber based on a gradient descent algorithm assisted via a radial basis-function surrogate model. This approach significantly reduces the number of optimization iterations, achieving a stable improvement in birefringence performance by an order of magnitude across the operating wavelength band. Furthermore, various optimization algorithms were compared, and the indicators of their Pareto sets were analyzed to demonstrate the effectiveness of the proposed method in multi-objective optimization.

Probabilistic inverse design of metasurfaces using mixture density neural networks

Abstract Metasurfaces are planar sub-micron structures that can outperform traditional optical elements and miniaturize optical devices. Optimization-based inverse designs of metasurfaces often get trapped in a local minimum, and the inherent non-uniqueness property of the inverse problem plagues approaches based on conventional neural networks. Here, we use mixture density neural networks to overcome the non-uniqueness issue for the design of metasurfaces. Once trained, the mixture density network can predict a probability distribution of different optimal structures given any desired property as the input, without resorting to an iterative local optimization. As an example, we use the mixture density network to design metasurfaces that project structured light patterns with varying fields of view. This approach enables an efficient and reliable inverse design of fabrication-ready metasurfaces with complex functionalities without getting trapped in local optima.

An Integrated Modeling Framework for Automated Product Design, Topology Optimization, and Mechanical Simulation

The present study introduces an integrated software approach that provides an automated product design toolkit for customized products like knives, incorporating topology optimization (TO) and numerical simulations in order to streamline engineering workflows during the product development procedure. The modeling framework combines state-of-the-art technologies into a single platform, enabling the design and the optimization of mechanical structures with minimal human intervention. In particular, the proposed solution leverages artificial intelligence (AI), shape optimization methods, and computational tools in order to iteratively optimize material utilization as well as the design of products based on certain criteria. By embedding simulation within the design optimization loop, the developed software module ensures that performance constraints are respected throughout the design process. The case studies are concentrated in designing knives, demonstrating the platform’s ability to reduce design time, enhance product performance and provide rapid iterations of structurally optimized geometries. Finally, it should be noted that this research showcases the potential of integrated modeling technologies towards the transformation of traditional design paradigms, in this way contributing to faster, more reliable and efficient product development in various engineering industries through the training and deployment of AI models in these scientific fields.

TRFSA-HQS: Transformer-based MRI Compressed Sensing Network with Frequency Domain Self-Attention

Compressive sensing (CS) can reconstruct undersampled magnetic resonance images, but its iterative optimization process tends to be computationally intensive and time-consuming. Convolutional neural networks (CNNs) have demonstrated impressive reconstruction performance through non-linear feature extraction and mapping capabilities. However, CNNs often struggle to effectively learn and capture global dependencies in the data. Therefore, constructing a MRI reconstruction method that meets clinical real-time imaging and captures dynamic global correlation is crucial in fast and high-precision MRI tasks. We propose a deep unfolding network (TRFSA-HQS) for fast and accurate CS reconstruction, which combines frequency domain self-attention (FSA) based Transformer and half-quadratic splitting (HQS) iterative optimization scheme. TRFSA-HQS adopts an iterative scheme based on HQS to effectively decouple and update optimization problems. The decoupled data subproblems are updated by minimizing the objective function with competitive terms, while the regularization subproblems are updated using the TRFSA deep prior network. The TRFSA module employs an asymmetric UNet architecture, where the encoder and decoder utilize a frequency-domain discriminative feedforward network (DFFN) and FSA. The DFFN selectively extracts deep features from different frequency components, while the FSA captures global dependencies in the frequency domain. The experiment demonstrate that the proposed model achieves an average reconstruction PSNR of 35.11dB on a 0.2 sampling rate test set on FastMRI knee joint data, with an inference time of 0.74s. It has good reconstruction performance and noise robustness, meeting the clinical real-time imaging requirements.

Sparse Uniform Traversal of Model Parameter Space for Estimating the Nonlinear Displacement Response of Instrumented Buildings to Earthquakes

ABSTRACTThe displacement response of a building structure to earthquake excitations is crucial for assessing its seismic damage, which is usually accompanied by structural nonlinear behavior. This study proposes an efficient method of quickly estimating the nonlinear displacement responses of seismically damaged buildings. By designing a set of sparsely and uniformly distributed samples in the multi‐dimensional parameter space, this method traverses all these samples to find the best set of parameters for a parametric numerical model of the building that minimizes the error between the simulated and the measured responses. Compared to existing model‐driven methods, the proposed method can efficiently match the high‐dimensional parameters of the assumed parametric model without tedious and less robust iterative optimization, even if the instrumented building sustains severe seismic damage and deviates significantly from its initial state. The shaking table test data of a full‐scale four‐story reinforced concrete moment‐resisting frame structure is used to justify the advantage of the method over the state‐of‐the‐art optimization‐based model‐driven method. The proposed method successfully estimated the structural responses of all the stories of the building with an average error of 4.6% for the maximum inter‐story drift across the five earthquake loading runs. It took only approximately 19 min to complete the calculation on a personal computer, which could be greatly accelerated given more computation cores because the traversal is inherently friendly to parallel computation.

Linear quadratic control and estimation synthesis for multi‐agent systems with application to formation flight

AbstractThis paper concerns the optimality problem of distributed linear quadratic control in a linear stochastic multi‐agent system (MAS). The main challenge stems from MAS network topology that limits access to information from non‐neighbouring agents, imposing structural constraints on the control input space. A distributed control‐estimation synthesis is proposed which circumvents this issue by integrating distributed estimation for each agent into distributed control law. Based on the agents' state estimate information, the distributed control law allows each agent to interact with non‐neighbouring agents, thereby relaxing the structural constraint. Then, the primal optimal distributed control problem is recast to the joint distributed control‐estimation problem whose solution can be obtained through the iterative optimization procedure. The stability of the proposed method is verified and the practical effectiveness is supported by numerical simulations and real‐world experiments with multi‐quadrotor formation flight.

ScMSI: Accurately inferring the sub-clonal Micro-Satellite status by an integrated deconvolution model on length spectrum.

Microsatellite instability (MSI) is an important genomic biomarker for cancer diagnosis and treatment, and sequencing-based approaches are often applied to identify MSI because of its fastness and efficiency. These approaches, however, may fail to identify MSI on one or more sub-clones for certain cancers with a high degree of heterogeneity, leading to erroneous diagnoses and unsuitable treatments. Besides, the computational cost of identifying sub-clonal MSI can be exponentially increased when multiple sub-clones with different length distributions share MSI status. Herein, this paper proposes "scMSI", an accurate and efficient estimation of sub-clonal MSI to identify the microsatellite status. scMSI is an integrative Bayesian method to deconvolute the mixed-length distribution of sub-clones by a novel alternating iterative optimization procedure based on a subtle generative model. During the process of deconvolution, the optimized division of each sub-clone is attained by a heuristic algorithm, aligning with clone proportions that adhere optimally to the sample's clonal structure. To evaluate the performance, 16 patients diagnosed with endometrial cancer, exhibiting positive responses to the treatment despite having negative MSI status based on sequencing-based approaches, were considered. Excitingly, scMSI reported MSI on sub-clones successfully, and the findings matched the conclusions on immunohistochemistry. In addition, testing results on a series of experiments with simulation datasets concerning a variety of impact factors demonstrated the effectiveness and superiority of scMSI in detecting MSI on sub-clones over existing approaches. scMSI provides a new way of detecting MSI for cancers with a high degree of heterogeneity.

Time-lapse amplitude variation with offset difference inversion based on the reformulated Biot-Squirt model

Time-lapse AVO (Amplitude variation with offset) inversion is a significant technique for the estimation of dynamic reservoir changes. The main theoretical foundation is Zoeppritz equations in single-phase media. At present, there are studies on the inversion method of using Biot's theory. Although it has some improvements in accuracy compared with the method of using Zoeppritz equations, the squirt-flow mechanism is not considered. Furthermore, the precision of difference data equations still needs to be improved in difference inversion. In view of the above problems, the reformulated BISQ (Biot-Squirt) model is introduced into the inversion, and the iterative optimization is used to increase the precision of difference data equations continually in this paper. First, we derive the difference data equations about the reservoir-parameter changes. The reformulated BISQ model is applied, and can describe the characteristics of P-wave reflection accurately and expediently. Secondly, the objective function about the reservoir-parameter changes is constructed. The Bayesian theory and the model smoothing constraint are used, and are helpful to obtain accurate and stable solutions. Thirdly, we derive the equation for estimating the reservoir-parameter changes, and the inversion is iterative for the continuous improvement of accuracy. The difference inversion method is applied, and is helpful to enhance the data repeatability and reduce the calculation amount. Finally, synthetic and real data are applied for the inversion and testing to prove the effectiveness and feasibility of the method.

A Novel and Effective Method to Directly Solve Spectral Clustering.

Spectral clustering has been attracting increasing attention due to its well-defined framework and excellent performance. However, most traditional spectral clustering methods consist of two separate steps: 1) Solving a relaxed optimization problem to learn the continuous clustering labels, and 2) Rounding the continuous clustering labels into discrete ones. The clustering results of the relax-and-discretize strategy inevitably result in information loss and unsatisfactory clustering performance. Moreover, the similarity matrix constructed from original data may not be optimal for clustering since data usually have noise and redundancy. To address these problems, we propose a novel and effective algorithm to directly optimize the original spectral clustering model, called Direct Spectral Clustering (DSC). We theoretically prove that the original spectral clustering model can be solved by simultaneously learning a weighted discrete indicator matrix and a structured similarity matrix whose connected components are equal to the number of clusters. Both of them can be used to directly obtain the final clustering results without any post-processing. Further, an effective iterative optimization algorithm is exploited to solve the proposed method. Extensive experiments performed on synthetic and real-world datasets demonstrate the superiority and effectiveness of the proposed method compared to the state-of-the-art algorithms.

Novel imbalanced multi-class fault diagnosis method using transfer learning and oversampling strategies-based multi-layer support vector machines (ML-SVMs)

For health monitoring and fault diagnosis of critical mechanical system components, historical data related to equipment failures are often limited and exhibit varying imbalanced multi-class characteristics (e.g., with noisy and time-series data). Moreover, fault diagnosis frameworks based on traditional resampling algorithms (e.g., SMOTE) mostly heavily rely on manual feature extraction, making them difficult to adapt to diverse working conditions or objects. To address these challenges, we propose a novel end-to-end imbalanced multi-class fault diagnosis architecture using transfer learning and oversampling strategies-based multi-layer support vector machines (ML-SVMs). ML-SVMs utilize a VGG-based deep migration feature extraction method to extract features from original time-domain vibration signals, employing natural source domain weights to reduce dependence on human experience and sample size. Then, ML-SVMs introduce ISCOTE (i.e., the first and second layers of ML-SVMs), an improved version of the sample-characteristic over-sampling technique (SCOTE). ISCOTE generates more effective and reasonable samples for each fault class through a scaling factor and iterative optimization mechanism, whether in noisy feature spaces with fuzzy boundaries or in clear boundary feature spaces. Finally, in the third layer of ML-SVMs, multi-class SVMs (e.g., LS-SVMs and standard SVMs) are utilized to train balanced feature samples and derive classification models with strong generalization ability. The effectiveness of ML-SVMs is demonstrated through 16 fault diagnosis instances using CWRU and IMS bearing data, PHM 2010 and TTWD tool wear data. Results indicate that ML-SVMs outperform 8 well-known oversampling-based algorithms in fault diagnosis recognition rates and algorithm robustness. It has offered a feasible architecture for multi-class imbalanced fault scenarios with limited data and multiple adverse features.

Topologic-thermal synergism analysis for wedge-shaped channels leveraging data mining and self-organization geometries

Heat transfer enhancement is particularly difficult to achieve within narrow space and turning channels due to strong structural constraints plus flow recirculations. Traditional designs usually follow periodic topology layout of simple geometric features, such as pin-fin arrays, to distribute cooling for narrow turning channels, lacking alignment with specific design objectives and the constraints. This is attributed to three main issues: the absence of control methods for complex structures, the lack of data for variable topologies, and insufficient understandings of Topologic-thermal Synergism. To address these challenges, the integration of self-organization geometry and the constrained Bayesian optimization method is employed. The channel is divided into 15 regions: the left region is subjected to solid presence control and anisotropy determination, while other regions are assigned control parameters, enabling self-organizing parameterization in design region. The parameters then are optimized using a constrained Bayesian optimization approach, aimed at maximizing the thermal performance factor (TPF) while constraining the area of low heat transfer regions. The optimization process begins with 216 samples to build the initial surrogate model, followed by 30 optimization iterations, resulting in the creation of high-performance complex structures and the establishment of a topology database. Furthermore, the SHAP method is utilized to extract topology design guidelines based on the most influential parameters and their beneficial variation directions. The guideline serves to guide the topology layout of other cooling structures in similar design scenarios. Results demonstrate that optimized self-organized structure enhanced the overall thermal parameter by 70% and decreased the low heat transfer zones by 74% when compared with pin fin structures. Data mining identified material orientation and generation on the left-side region as critical factors in topological structures, significantly influencing overall heat transfer performance. Leveraging the obtained design guidelines, a topological layout for a guiding pin fin structure is redesigned in wedge-shaped channels with varying contraction ratios, achieving performance improvements without the need for additional computational resources. The outcomes of this work hold scientific significance and industrial application value in enabling rapid design of high performance heat transfer structures.

Concurrent multi-scale design optimization of fiber-reinforced composite material based on an adaptive normal distribution fiber optimization scheme for minimum structural compliance and additive manufacturing

Structural lightweight is a core technical requirement for the structural design of aerospace and new energy power equipment structures. For multi-scale variable stiffness design optimization of discrete fiber-reinforced composite laminates, one of the challenges is how to avoid the explosion of design variable combinations caused by the increase in the number of candidate discrete fiber laying angles. The Normal Distribution Fiber Optimization (NDFO) interpolation scheme has the numerical advantage that the number of design variables does not increase with an increase in the number of candidate discrete fiber laying angles. However, the traditional NDFO interpolation scheme uses uniform penalty parameters across all elements, which means that normalizing the penalty parameters for all the elements ignores the convergence differences of discrete fiber laying angles in different elements within the macro-scale structure topology. This leads to time-consuming and unstable optimization iteration of the macro-scale structural topology and micro-scale discrete fiber laying angle selection. Especially, it easily causes the micro-scale discrete fiber laying angle selection to fall into the local optimum prematurely. Therefore, considering the difficulties and challenges of the traditional NDFO interpolation scheme in the multi-scale variable stiffness design optimization of fiber-reinforced composites. This paper proposes an Adaptive Normal Distribution Fiber Optimization (ANDFO) interpolation scheme, and the feedback mechanism of the convergence rate of the element design variable and the objective function is introduced to achieve the adaptive reduction of the penalty parameters. Based on the proposed ANDFO interpolation scheme, a multi-scale design optimization model of fiber-reinforced composite laminates is established, considering the macro-scale structure topology and micro-scale discrete fiber laying angel selection. The explicit sensitivity of the objective function of minimizing structural compliance to the macro-scale topological design variables and the micro-scale fiber laying angle design variables is derived. Considering the manufacturability of additive manufacturing based on the optimized design results, a multi-scale nonlinear continuous filtering strategy for discrete fiber laying angle is adopted to improve the continuity of the local fiber laying path. Numerical examples systematically present the coupling effects of macro-scale structural topology and micro-scale fiber laying path, multi-scale nonlinear discrete fiber continuous filtering laying path structure, and continuous fiber additive manufacturing multi-scale optimized structure. The proposed ANDFO scheme provides a new theoretical and methodological approach for the lightweight and integrated multi-scale design and manufacturing of fiber-reinforced composite laminates through additive manufacturing technology.

Study on the Optimization of Three-Layer Glass Thickness Using Ant Colony Algorithm to Reduce UV Transmission

This study introduces an ant colony algorithm-based method for optimizing the thickness of triple-layer glass to minimize ultraviolet (UV) transmission, thereby enhancing protection against harmful rays within indoor environments. A physical model of triple glazing was employed to systematically analyze how variations in glass thickness impact UV transmittance. Utilizing the ant colony algorithm, the research team conducted extensive iterative optimizations to determine the most effective thickness combinations, which proved successful in substantially reducing the transmission of particularly damaging short-wave UV rays. Extensive simulations conducted in MATLAB supported the experimental results, confirming the significant improvement in UV protection offered by the optimized glass structure. This innovative approach not only establishes a solid foundation for the future development of advanced glass design but also underscores the versatility and efficacy of the ant colony algorithm in material engineering. Looking ahead, the research will focus on refining the algorithm's parameters to enhance its precision and extend its application to other areas of architectural and material science, aiming to meet broader environmental and health safety standards.

Multi-collimator proton minibeam radiotherapy with joint dose and PVDR optimization.

The clinical translation of proton minibeam radiation therapy (pMBRT) presents significant challenges, particularly in developing an optimal treatment planning technique. A uniform target dose is crucial for maximizing anti-tumor efficacy and facilitating the clinical acceptance of pMBRT. However, achieving a high peak-to-valley dose ratio (PVDR) in organs-at-risk (OAR) is essential for sparing normal tissue. This balance becomes particularly difficult when OARs are located distal to the beam entrance or require patient-specific collimators. This work proposes a novel pMBRT treatment planning method that can achieve high PVDR at OAR and uniform dose at target simultaneously, via multi-collimator pMBRT (MC-pMBRT) treatment planning method with joint dose and PVDR optimization (JDPO). MC-pMBRT utilizes a set of generic and premade multi-slit collimators with different center-to-center distances and does not need patient-specific collimators. The collimator selection per field is OAR-specific and tailored to maximize PVDR in OARs while preserving target dose uniformity. Then, the inverse optimization method JDPO is utilized to jointly optimize target dose uniformity, PVDR, and other dose-volume-histogram based dose objectives, which is solved by iterative convex relaxation optimization algorithm and alternating direction method of multipliers. The need and efficacy of MC-pMBRT is demonstrated by comparing the single-collimator (SC) approach with the multi-collimator (MC) approach. While SC degraded either PVDR for OAR or dose uniformity for the target, MC provided a good balance of PVDR and target dose uniformity. The proposed JDPO method is validated in comparison with the dose-only optimization (DO) method for MC-pMBRT, in reference to the conventional (CONV) proton RT (no pMBRT). Compared to CONV, MC-pMBRT (DO and JDPO) preserved target dose uniformity and plan quality, while providing unique PVDR in OAR. Compared to DO, JDPO further improved PVDR via PVDR optimization during treatment planning. A novel pMBRT treatment planning method called MC-pMBRT is proposed that utilizes a set of generic and premade collimators with joint dose and PVDR optimization algorithm to optimize OAR-specific PVDR and target dose uniformity simultaneously.

Iterative Optimization RCO: A “Ruler & Compass” Deterministic Method

Iterative Optimization RCO: A “Ruler & Compass” Deterministic Method

An Iterative Optimization Algorithm for Planning Spacecraft Pathways Through Asteroids

In this article, we explore the use of meta-heuristic algorithms for costly black-box permutation optimization problems. These combinatorial problems are defined by solution spaces that consist of permutations of elements, with an objective function that lacks a closed mathematical representation and is expensive to evaluate. The focus of our investigation is the Asteroid Routing Problem (ARP), which seeks to determine the optimal sequence of asteroids to be visited by a spacecraft while minimizing energy consumption and travel time. Specifically, we assess the performance of a simple algorithm called FAT-RLS, which primarily relies on a randomized local search approach, enhanced with a tabu structure and a mechanism to adjust the perturbation strength. We conducted a series of experiments on well-established instances of the ARP to compare the effectiveness of FAT-RLS against two recognized meta-heuristics designed for this problem, namely, UMM and CEGO. Experiments were conducted in both uninformed and informed settings, where the meta-heuristics are initialized with a specifically designed constructive algorithm for the ARP. The results demonstrate that FAT-RLS is consistently superior to UMM, while there is no conclusive evidence for the comparison with CEGO, though the FAT-RLS results seem slightly better.

Two-step graph propagation for incomplete multi-view clustering

Incomplete multi-view clustering addresses scenarios where data completeness cannot be guaranteed, diverging from traditional methods that assume fully observed features. Existing approaches often overlook high-order correlations present in multiple similarity graphs, and suffer from inefficiencies due to iterative optimization procedures. To overcome these limitations, we propose a graph-based model leveraging graph propagation to effectively handle incomplete data. The proposed method translates incomplete instances into incomplete graphs, and infers missing entries through a graph propagation strategy, ensuring the inferred data is meaningful and contextually relevant. Specifically, a self-guided graph is constructed to capture global relationships, while partial graphs represent view-specific similarities. The self-guided graph is first completed through self-guided graph propagation, which subsequently aids in the propagation of the partial graphs. The key contribution of graph propagation is to propagate information from complete data to incomplete data. Furthermore, the high-order correlation across multiple views is captured by low-rank tensor learning. To enhance computational efficiency, the optimization procedure is decoupled and implemented in a stepwise manner, eliminating the need for iterative updates. Extensive experiments validate the robustness of the proposed method, demonstrating superior performance compared to state-of-the-art methods, even when all instances are incomplete.