Optimization Framework Research Articles

Trust region framework-based design of sub-6 GHz m-MIMO antenna and evaluation of SAR

Purpose Currently, massive multiple-input multiple-output (m-MIMO) antennas are typically designed using complex trial-and-error methods. The purpose of this study is to determine an effective optimization method to achieve more efficient antenna design processes. Design/methodology/approach This paper presents the design stages of a m-MIMO antenna array compatible with 5G smartphones operating in long term evolution (LTE) bands 42, 43 and 46, based on a specific algorithm. Each antenna element in the designed 10-port m-MIMO antenna array is intended to perfectly cover the three specified LTE bands. The optimization methods used for this purpose include the Nelder–Mead simplex algorithm, covariance matrix adaptation evolution strategy, particle swarm optimization and trust region framework (TRF). Findings Among the primary optimization algorithms, the TRF algorithm met the defined objectives most effectively. The achieved antenna efficiency values exceeded 60.81% in the low band and 68.39% in the high band, along with perfect coverage of the desired bands, demonstrating the success of the design with the TRF algorithm. In addition, the potential electromagnetic field exposure caused by the designed m-MIMO antenna array is elaborated upon in detail using computational human models through specific absorption rate analysis. Originality/value The comparison of four different algorithms (two local and two global) for use in the design of a 10-element m-MIMO antenna array with a complex structural configuration and the success of the design implemented with the selected algorithm distinguish this study from others.

Simultaneous stochastic optimisation of mining complexes: Integrating progressive reclamation and waste management with contextual bandits

ABSTRACT Managing environmental performance of waste dump facilities in mining complexes is an integral part of long-term production planning. Sustainable long-term production scheduling solutions are desired to mitigate risk and return the environment to a productive post-mining state. A simultaneous stochastic optimisation framework for long-term production scheduling in mining complexes is developed that integrates waste management and progressive reclamation. The waste dump placement schedule is jointly optimised with the extraction sequence, destination policy, and stockpiling decisions in a single stochastic mathematical programming framework. This includes the timing of progressive reclamation activities in parallel with production to enhance waste dump rehabilitation. Uncertainty related to the production of acid rock drainage is quantified by simulating geochemical properties of waste and managing the blending of uncertain waste properties within the optimisation framework. With respect to the framework for simultaneous stochastic optimisation, contextual bandits are explored to improve the metaheuristic solution approach and solve the corresponding large-scale optimisation model. The framework is tested in a multi-mine copper-gold mining complex leading to improved environmental performance. Risk of acid rock drainage is decreased by 52.5% in the waste dump facilities. Reclamation planning activities for meeting environmental requirements are scheduled prior to closure. The solution approach more effectively improves the objective function with contextual bandits leading to a 24% improvement in the study presented.

Retraction Note: A novel machine learning-based framework for channel bandwidth allocation and optimization in distributed computing environments

Retraction Note: A novel machine learning-based framework for channel bandwidth allocation and optimization in distributed computing environments

Understanding Substrate Effects on 2D MoS2 Growth: A Kinetic Monte Carlo Approach

AbstractControlling the morphology of 2D transition metal dichalcogenides (TMDs) plays a key role in their applications. Although chemical vapor deposition can achieve wafer‐scale growth of 2D TMDs, a comprehensive theoretical framework for effective growth optimization is lacking. Atomistic modeling methods offer a promising approach to delve into the intricate dynamics underlying the growth. In this study, kinetic Monte Carlo (kMC) simulations are employed to identify crucial parameters that govern the morphology of MoS2 flakes grown on diverse substrates. The simulations reveal that large adsorption rates significantly enhance growth speed, which however necessitates rapid edge migration to achieve compact triangles. Substrate etching can tune the adsorption–desorption process of adatoms and enable preferential growth within a specific substrate region, controlling the flake morphology. This study unravels the complex dynamics governing 2D TMD morphology, offering a theoretical framework for decision‐making in the design and optimization of TMD synthesis processes.

TinyNS: Platform-aware Neurosymbolic Auto Tiny Machine Learning

Machine learning at the extreme edge has enabled a plethora of intelligent, time-critical, and remote applications. However, deploying interpretable artificial intelligence systems that can perform high-level symbolic reasoning and satisfy the underlying system rules and physics within the tight platform resource constraints is challenging. In this article, we introduce TinyNS , the first platform-aware neurosymbolic architecture search framework for joint optimization of symbolic and neural operators. TinyNS provides recipes and parsers to automatically write microcontroller code for five types of neurosymbolic models, combining the context awareness and integrity of symbolic techniques with the robustness and performance of machine learning models. TinyNS uses a fast, gradient-free, black-box Bayesian optimizer over discontinuous, conditional, numeric, and categorical search spaces to find the best synergy of symbolic code and neural networks within the hardware resource budget. To guarantee deployability, TinyNS talks to the target hardware during the optimization process. We showcase the utility of TinyNS by deploying microcontroller-class neurosymbolic models through several case studies. In all use cases, TinyNS outperforms purely neural or purely symbolic approaches while guaranteeing execution on real hardware.

Additive manufacturing-driven simultaneous optimization of topology and print direction for thermoelastic structures considering strength failure

Abstract This paper presents a strength-based simultaneous optimization method for optimizing thermoelastic structural topology and print direction in the presence of anisotropy induced by additive manufacturing. The approach utilizes the bi-directional evolutionary structural optimization (BESO) framework and defines design variables including element density and print-off angle. Firstly, an anisotropic thermoelastic constitutive model is established for finite element analysis. By introducing the Tsai-Hill failure criteria, the strength constraint to evaluate the stress level of additively manufactured anisotropic components is formulated. The P-norm aggregation function is employed to approximate the maximum strength failure coefficient. Then, the aggregated strength constraint is augmented to the optimization objective through a Lagrange multiplier. Sensitivity analysis of the new objective function with respect to the elemental design variables is performed, and an analytical approach is proposed to optimize the print-off angle. To improve the stability of the optimization procedure, a series of numerical algorithms and parameter updating strategies are developed. The effectiveness of our proposed method is demonstrated through typical numerical examples, highlighting a desirable match between the structural topology and the print direction can greatly improve the structural performance.

Parametric Dynamic Simulation and Bayesian Design Optimization of a Front-Loading Washing Machine

Abstract Objective This paper presents the dynamic modeling and design optimization of a fully parametrized front-loading washing machine. Methods A thorough mathematical analysis is performed to capture the effect of nine design variables (including rotation speed, spring stiffness, weight balancer mass density, damping coefficient, spring and damper angles) on vibrational characteristics of the washing machine. The parametric simulation reveals a complex relationship between the vibrational dynamics and design variables, entailing a computationally efficient optimization approach. A Bayesian Optimization (BO) framework is developed, in which a Gaussian process model and Genetic Algorithm (GA) are utilized along with carefully selected acquisition functions to enable adaptive sampling and search for optimal design values. The objective function is to minimize the maximum amplitude of the tub displacement inside the washing machine body. Two case studies are performed to consider a different number of design variables and choices of various infill methods. Results Results show that the fully parametrized front-loading washing machine model is applicable for parametric simulation and design optimization. The proposed BO is able to successfully find a set of design variable values corresponding to the lowest maximum displacement for vibration reduction. A comparison analysis is also carried out to exhibit the difference in optimization convergence and computational cost caused by the choice of acquisition functions. Conclusion It is shown that the fully parametrized washing machine model yields a lower maximum displacement than the model with fewer design variables. Of the two acquisition functions studied for optimization, the Expected Improvement function converges more rapidly than the Lower Bound criterion.

Risk-averse electrolyser sizing in industrial parks: An efficient stochastic-robust approach

Hydrogen is called to be one of the most important energy vectors in future energy systems. Nowadays, its use in the industry sector is prominent, finding multiple applications in fertilizer production or oil refining. In this sense, some industry sectors demand a considerable amount of hydrogen for their processes. In many cases, hydrogen must be purchased externally, which supposes a challenge due to hydrogen transportation is costly and few efficient. In this context, local hydrogen production through mature electrolysis technology may suppose an attractive alternative in industrial parks. This paper focuses on this topic, in particular, a risk-averse electrolyser sizing methodology is developed. The new approach accounts for uncertainties in electricity prices as well as local renewable generation and demand through an original hybrid stochastic-robust model. The developed uncertainty modelling is integrated into a novel four-level optimization framework, whose main result is the optimal electrolyser rated power. To efficiently attain the solution, an original hybridization of the Benders' decomposition and the Column and Constraint Generation Algorithm is proposed. The developed methodology results efficient in an illustrative three-industry park, showing that installing local hydrogen generation may reduce the amount of hydrogen purchased externally (by 38%) and project costs by 2.5%. Furthermore, increasing the robustness level leads to increase the project cost by 8%, while assuming unfavourable realization of uncertainties. Moreover, the developed tool is further validated in larger parks, involving an increasing number of industries, showing that the proposed methodology scales well with the size of the park.

EXPRESS: Crowd-starting a Shared (Shuttle) Service with Customer Suggestions

Digital platforms have improved the effciency and quality of smart city operations by soliciting more customer inputs, for example, in the form of suggestions. One innovative option in urban transportation is the shared shuttle service, which lies between traditional public transportation and ride-hailing services. Platforms that offer these services can gather customer suggestions in a \crowd-starting" manner, which provides valuable insights into customer needs. However, this also presents a challenge in balancing service coverage and quality to meet customer needs implied by their suggestions. To address this issue, we introduce an optimization framework designed to maximize expected profit by leveraging customer response models which characterize how customers will respond to different service attributes and how their suggestions inform these responses. When estimating these response models, we present methods involving isotonic penalty and shrinkage tailored for handling small datasets. To demonstrate the practical implications, we apply our model to a shared shuttle service case study and discuss practical considerations, such as the value of information, the effectiveness of our estimation approaches, and the benefits of involving customers in the service design process.

Microstructure Design of Multiphase Compositionally Complex Alloys II: Use of a Genetic Algorithm and a Vanishing Cracked Particle Model

AbstractAchieving high strength, ductility, and toughness via microstructure design is challenging due to the interrelated dependencies of strength and ductility on microstructural variables. As a natural extension of the microstructure design work in Bhattacharyya and Agnew (Microstructure design of multiphase compositionally complex alloys I: effects of strength contrast and strain hardening, 2024), an optimization framework to obtain the microstructure that maximizes the toughness is described. The strategy integrates a physics-based crystal plasticity model, which accounts for damage evolution within the reinforcement through a “vanishing cracked particle” model that is governed by Weibull statistics, and a genetic algorithm-based optimization routine. Optimization constraints are imposed in the form of bounds on the microstructure parameters such that they are most likely attainable by conventional thermomechanical processing. Various matrix strain hardening behaviors are considered, as well as the strength contrast between the two phases and fracture behavior of the reinforcement. It is shown that the addition of a fine-grained (hard) reinforcing phase is preferred as is a matrix that exhibits sustained strain hardening such as is observed under TRIP/TWIP scenarios. Finally, the Pareto-optimal set of solutions for several scenarios are presented which offer new insights into the linkages between microstructure and mechanical properties.

The Integration of Dual Evaluation and Minimum Spanning Tree Clustering to Support Decision-Making in Territorial Spatial Planning

Spatial planning, a policy instrument for creating sustainable environments that meet the needs of the current and future generations, has been implemented extensively worldwide. However, it is difficult for urban planners to thoroughly determine the spatial value of a territory and make informed decisions regarding the efficient utilization of regional resources in the real world. This study proposes a novel methodological framework for spatial pattern optimization that can guide future land use by integrating Minimum Spanning Tree (MST) clustering with a comprehensive evaluation system (dual evaluation). Furthermore, the validity of this framework is demonstrated through a case study of territorial spatial planning in Deyang, China. The findings indicate that (1) the methodological framework presented in this study offers valuable guidance for the spatial arrangement of territorial resources, especially in practical projects; and (2) the combination of dual evaluation and MST clustering can facilitate automatic regionalization to identify spatial clusters exhibiting functional similarity in terms of land use. By focusing on methodological advancements, this study concludes that the integration of dual evaluation (DE) and MST clustering not only simplifies the identification of optimal land-use patterns but also promotes a more systematic and efficient approach to support spatial planning.

PRACTICE OF USING IMPLANTS IN GYNECOLOGICAL SURGICAL INTERVENTIONS (SCIENTIFIC LITERATURE REVIEW)

Introduction. The use of implants during surgical interventions in gynecology can contribute to the anatomical restoration of the genital organs. The most commonly used implants in gynecology are polypropylene meshes, which promote tissue ingrowth, thus restoring reproductive organs. One of the reasons for using implants in gynecology is pelvic organ prolapse, including uterine prolapse, anterior or posterior vaginal wall prolapse, and vaginal vault prolapse. Synthetic implants are often utilized to reinforce surgical interventions for gynecological disorders, with better indicators of anatomical success in the long term. The aim of the research is to investigate the biomechanical properties of implants in gynecology as effective means for the surgical restoration of female reproductive organs. Materials and methods. Bibliosemantic, comparative, and systematic analysis methods were employed. The proposed recommendations were developed based on the analysis of contemporary literature, results of randomized trials, and meta-analyses dedicated to studying the impact of implants in surgical interventions in gynecological practice. Results. The analyzed results indicate that the optimal supportive framework of implants for gynecological surgery, particularly in the treatment of prolapse, should aim to facilitate the formation of a new matrix for fascial reinforcement, thereby minimizing the development of scar tissue. Conclusions. The primary objective of using implants in gynecological surgical interventions is to search for optimal designs to complement vaginal surgery. This is essential for prolonging treatment outcomes, increasing patient satisfaction, and reducing complications.

Analyzing Hydrogen Flow Behavior Based on Deep Learning Sensor Selection Optimization Framework

Abstract With tremendous potential to lower the carbon footprint of power generation and as an alternative energy carrier for many applications, hydrogen has emerged as a key potential energy carrier. Understanding of geological conditions and the injection and production changes over time for hydrogen storage are paramount, requiring in situ reservoir sensing options. Determining the flow behavior is of critical importance in order to estimate hydrogen volumes within the reservoir. A novel AI-driven methodology for hydrogen flow behavior and volume estimation was demonstrated. For determining the S1 through S3 predicted hydrogen storage quantities, the framework is linked to an uncertainty estimate framework. The Pohokura field in New Zealand served as the basis for the framework's evaluation, and it performed acceptably in terms of identifying the hydrogen flow behaviors and quantities inside the subsurface reservoir. The framework is a significant first step in assessing the amount of hydrogen that can be stored in subterranean reservoirs for long-term hydrogen storage.

Constraint‐aware optimization model for plane truss structures via single‐agent gradient descent

AbstractThis study introduces the constraint‐aware optimization model (CAOM), a novel optimization framework designed to optimize the size, shape, and topology of plane truss structures simultaneously. Unlike traditional optimization models, which rely on gradient descent and frequently struggle with managing various constraints due to their dependence on a single optimization agent, CAOM effectively addresses this challenge. It does so by incorporating a variety of assistant modules along with the Adam optimizer, a variant of the gradient descent method. Uniquely, CAOM employs the leaky rectified linear unit (ReLU) activation function beyond its conventional use in neural networks, applying it as a mechanism to integrate constraints and losses seamlessly. The model's effectiveness was validated through two numerical examples and a practical application, demonstrating that CAOM can reduce structural weight by up to 84% compared to unoptimized designs while fully adhering to structural, dimensional, and moveable constraints. Furthermore, the study found that while shape optimization plays a key role for stiffness‐governed structures, size optimization is crucial for strength‐governed structures. Optimizing size, shape, and topology together consistently leads to the most weight‐efficient designs. This emphasizes the significance of a holistic approach in the optimization processes.

Synergistic Integration of EVs and Renewable DGs in Distribution Micro-Grids

This paper proposes a multi-objective optimization framework for safe, reliable, and economic integration of electric vehicles (EVs) and renewable distributed generators (DGs) in distribution micro-grids. EV and DG coordination optimization with the use of vehicle-to-grid (V2G) technology along with system reconfiguration optimization is developed to provide collective revenues and address integrational complications that may occur by additional system loading due to EV charging and EV-DG energy exchanges. A Genetic Algorithm (GA) optimizes the EV charging/discharging in synergies with renewable DGs to maximize benefits that can be captured by their collaborative participation in electricity market and through renewable energy arbitrage. The developed EV charging/discharging optimization is implemented in a real 134-bus distribution network and is evaluated for its potential operational implications, namely, increased system losses. A system reconfiguration is then proposed to reduce the system losses by optimizing the flow of power through switching on/off the connections within the micro-grid and/or with other distribution systems. Simulation results demonstrate the efficiency of the proposed method in not only providing collective revenues, but also in enhancing the system operation by reducing the losses of the distribution grid. The collective benefits proposed by the developed optimization and validated by the simulation results facilitate transitioning to clean and eco-friendly sources of energy for generation and transportation, which in turn leads to more sustainable development of societies and communities.

Low-Resolution Optimization for an Unmanned Aerial Vehicle Communication Network under a Passive Reconfigurable Intelligent Surface and Active Reconfigurable Intelligent Surface

This paper investigates the optimization of an unmanned aerial vehicle (UAV) network serving multiple downlink users equipped with single antennas. The network is enhanced by the deployment of either a passive reconfigurable intelligent surface (RIS) or an active RIS. The objective is to jointly design the UAV’s trajectory and the low-bit, quantized, RIS-programmable coefficients to maximize the minimum user rate in a multi-user scenario. To address this optimization challenge, an alternating optimization framework is employed, leveraging the successive convex approximation (SCA) method. Specifically, for the UAV trajectory design, the original non-convex optimization problem is reformulated into an equivalent convex problem through the introduction of slack variables and appropriate approximations. On the other hand, for the RIS-programmable coefficient design, an efficient algorithm is developed using a penalty-based approximation approach. To solve the problems with the proposed optimization, high-performance optimization tools such as CVX are utilized, despite their associated high time complexity. To mitigate this complexity, a low-complexity algorithm is specifically tailored for the optimization of passive RIS-programmable reflecting elements. This algorithm relies solely on closed-form expressions to generate improved feasible points, thereby reducing the computational burden while maintaining reasonable performance. Extensive simulations are created to validate the performance of the proposed algorithms. The results demonstrate that the active RIS-based approach outperforms the passive RIS-based approach. Additionally, for the passive RIS-based algorithms, the low-complexity variant achieves a reduced time complexity with a moderate loss in performance.

Reverse engineering morphogenesis through Bayesian optimization of physics-based models

Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology, a key challenge is determining optimal cellular interactions for predicting organ shape, size, and function. Physics-based models defining the subcellular force distribution facilitate this, but it is challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the experimentally observed organ shapes. This integrative framework employs Gaussian Process Regression, a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that maintain the final organ shape. We calibrated and tested the method on Drosophila wing imaginal discs to study mechanisms that regulate epithelial processes ranging from development to cancer. The parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with imaging data of wing discs perturbed with collagenase. The computational pipeline identifies distinct parameter sets mimicking wild-type shapes. It enables a global sensitivity analysis to support the regulation of actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with experimental imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This workflow is extensible toward reverse-engineering morphogenesis across organ systems and for real-time control of complex multicellular systems.

Influence of Surface Roughness on Symmetric Airfoil Flow Properties and Reynolds Number

Collaborative Aircraft Design Optimization: Investigating Roughness Management of Wings to Reduce Drag and Improve Performance This study investigates the potential of roughness control of aircraft wings based on joint design optimization. By adding roughness points to specific wings, our goal is to reduce drag and improve the overall performance of the aircraft. A multi-modal design optimization (MDO) framework combining aerodynamics, modelling, and performance analysis was used in this study. We measure the effect of tissue on the boundary layer, the progression of laminar flow, and the potential for weight savings due to reduced friction. This study investigates how roughness control interacts with other design factors such as flow components or blade materials to achieve optimal performance. The ultimate goal is to create a collaborative design that uses tissue management to contribute to next-generation fuel efficiency and high performance. Keywords – CFD, Surface Roughness, Wind Tunnel, Aerodynamic Efficiency.

Surrogate-model-based optimization of a nonideal polymerization reactor for producing tailored molecular weight distribution

The optimization of polymerization reactors with nonideal mixing has been greatly hindered by a tremendous computation burden. In this study, a global optimization framework to integrate full MWD calculations and detailed reactor dynamics with nonideal mixing behaviors was developed by reducing the considerable computation time required for implementing full MWD. A combination of surrogate-model-based optimization and a computational fluid dynamics (CFD)-compartment reactor model was applied to produce the desired MWD in a nonideal reactor. A neural network (NN) surrogate model was trained using 3,000 MWD data produced from the mechanistic reactor model. The results showed that the model successfully described asymmetric MWD with bimodality resulting from the nonideal mixing behaviors in the reactor. The surrogate model was used to construct operation maps for the asymmetry and bimodality factors, with SHapley Additive exPlanations (SHAP) values showing that the flow rate of the chain transfer agent had the largest impact on the asymmetry factor, whereas temperature significantly influenced the bimodality factor. Finally, the optimization strategy based on the asymmetric and bimodality factors successfully determined operating conditions to produce four tailored MWDs, reported in actual polymerization reactors with R scores of 0.988–0.997.

Optimal determination of valve setting coefficient for the multi-tank water feeder system

ABSTRACT Filling up water to several water distribution service tanks equitably needs a proper valve setting in the operation. Setting by trial and error requires considerable time which could invite more labour-level exercise. This study addresses the issue of unequal water distribution to elevated service tanks (ESTs). It is possible to achieve an optimal valve loss coefficient, which ensures equal water supply to all ESTs. Throttle and control valves play a crucial role in regulating the flow rate and pressure of water. An optimization framework for the valve setting coefficient is proposed. The genetic algorithm is used as an optimization model and hydraulic simulation is performed using EPANET 2.0 software through its toolkits. The proposed model is applied to a hypothetical system and illustrated. The obtained valve setting coefficients make equitable distribution of water to all the ESTs.