Optimization Model For Design Research Articles

Multiscale design optimization of lattice structures considering dynamic response minimization and reaction force uniformity

A multiscale design optimization model is proposed for a vibrating hierarchical lattice structure, considering minimization of vibration response and uniformity of reaction forces at the fixed boundary. Two different lattice unit cells are hybridized to improve the manufacturability and mechanical properties of the lattice structures. Lattice microstructures are designed and controlled by multiple design variables. Surrogate models are used to fit the equivalent performance with the design variables. An optimization formulation is established for minimizing the displacement response amplitude at specified locations of the structure. The variance and mean value of the reaction force are introduced as constraints for the uniform reaction force at the structure boundary. Sensitivities of the objective and constraint functions are derived with the adjoint method. Finally, the effectiveness of the method is verified by several numerical examples, and the influence of the variance constraint of the reaction force at the structure boundary is discussed.

Optimizing Learning Path Design in Mobile Learning Platforms for Online Courses

With the rapid advancement of information technology, online education, particularly vocational education, has become a vital avenue for enhancing skills and knowledge. Vocational education necessitates flexible and personalized learning path design to accommodate the diverse needs and behaviors of learners. Mobile learning platforms, as a pivotal form of modern online education, provide learners with the convenience of accessing educational resources anytime and anywhere. However, existing methods for optimizing learning paths exhibit notable limitations, primarily in accurately capturing learners’ dynamic behaviors and in providing personalized and intelligent path design. Therefore, how to optimize learning paths based on learners’ dynamic behavior data has become a research hotspot in academia and educational practice. At present, many studies focus on matching analysis based on students’ static characteristics with course content but overlook learners’ behavioral changes and dynamic needs during the learning process. Traditional recommendation algorithms and rule-based path design methods are difficult to cope with complex learning behaviors and diverse learner needs. This study addresses these limitations by proposing an optimization model for mobile learning path design based on multi-view prediction of dynamic student behaviors. The model extracts mobile interaction features from student groups, constructs a multiple mobile behavior collaborative encoder, and employs multiple task label prediction techniques to achieve personalized and intelligent optimization of learning paths. The results demonstrate that this approach significantly enhances the learning efficiency and experience of learners, offering novel insights and technological support for the path design of mobile learning platforms.

A Multi-Objectives Optimization Model for the Joint Design of Statistical Process Control and Engineering Process Control

A Multi-Objectives Optimization Model for the Joint Design of Statistical Process Control and Engineering Process Control

An optimization model for network design in disaster relief planning

An optimization model for network design in disaster relief planning

A novel mathematical model for mixed-feed multi-effect distillation system optimization design

A novel mathematical model for mixed-feed multi-effect distillation system optimization design

Cooperative Optimization Design Method of Multitrajectory Profile in “Well Factory” for Oil and Gas Development

Summary A “well factory” refers to the arrangement of multiple or even a large number of similar wells on the same platform and the drilling and completion of these wells with standardized equipment and a similar operational approach. The well factory mode can effectively handle the challenges posed by complex terrains in oceans and mountainous areas and achieve economical and efficient development of oil and gas resources. Conventional studies mainly focus on the optimization of a single trajectory or the sequential optimization of multiple trajectories. However, there is a lack of cooperative optimization of multiple trajectories, making it difficult to obtain better design results for well trajectories of the well factory mode. Owing to the complicated interdependencies among the design variables of each trajectory (such as the kickoff point, target section length, well inclination, azimuth, and buildup rate), traditional methods struggle to directly address the cooperative optimization problem. This problem involves collaboration and interaction between different trajectories to achieve common objectives and enhance the overall optimization effectiveness. This study established a multitrajectory profile cooperative optimization design model with the objective of minimizing the overall drilling footage, trajectory complexity, trajectory similarity, and collision risk. Next, a cooperative coevolution (CC) approach was introduced and combined with a directed artificial bee colony (DABC) algorithm to develop a collaborative update strategy for multitrajectory optimization. Finally, the CC-DABC algorithm was tested, and a case study was conducted using the new design method. The results indicated that the new method could effectively handle multitrajectory profile cooperative optimization design for various well layouts, including single-layer, multilayer, and extremely small well-spacing scenarios. The CC-DABC algorithm significantly outperformed other combined algorithms in terms of optimization effectiveness, robustness, and overall search efficiency and exhibited excellent performance under small well spacing conditions. In the case study, the trajectories optimized with the new method had less drilling footage and smaller trajectory complexity than the original designs. Additionally, the anticollision risk and staggered arrangement of the kickoff points between adjacent wells effectively met the engineering requirements, demonstrating the application potential of this new method.

Refractance Window Drying: A New Approach For Producing High-Quality Powdered Dairy Products.

Refractance window drying is an emerging technology that allows the development of new dried foods with an acceptable shelf life from products widely consumed in the world with high nutritional content and health benefits, such as Dairy products. The present study aimed to determine the effect of temperature and product thickness during Refractance window drying in a laboratory scale dryer on the physicochemical properties of whole bovine milk and commercial flavored yogurt, using an optimal design model. The drying temperature range was between 40°C and 80°C, while the evaluated thickness ranged from 1 to 3 mm. Energy consumption, pH, moisture, water activity, color, solubility, hygroscopicity, and protein denaturation were determined by the reconstitution of the powders obtained and employing techniques such as gravimetric method, color analysis, and FTIR. Based on the results obtained the drying temperatures and thicknesses studied did not affect the secondary structure of proteins; the optimal operational conditions were 64.7°C and 0.001 m for whole milk and 63.7°C and 0.001 m for yogurt. These findings suggest that Refractance window drying is a suitable technology for obtaining dried dairy products, particularly for viscous foods such as yogurt.

Energy optimization design for the driving source for rock fragmentation by high-voltage pulsed discharge (RHPD)

The energy parameter of the driving source for rock fragmentation by high-voltage pulsed discharge (RHPD) determines the effectiveness of rock fragmentation. The mapping relationship between the energy parameter of the RHPD driving source and the stress parameter is established, and an energy optimization design criterion for the RHPD driving source is proposed. The time-varying impedance of the plasma channel is simulated, and the spatial and temporal distribution of stress within the rock under the channel pressure loading is analyzed. A rock fragmentation criterion based on the stress impulse criterion is proposed, and a nonlinear constrained mathematical model for the energy optimization design for the RHPD driving source is developed. Using the existing driving source and load parameters as examples, the feasibility and accuracy of the proposed energy optimization design criterion for the RHPD driving source are verified, which provides theoretical guidance for the design of the RHPD driving source.

Accelerating deep learning model development—towards scalable automated architecture generation for optimal model design

Accelerating deep learning model development—towards scalable automated architecture generation for optimal model design

The application of ICPA optimization algorithm in multi-objective optimization structural design of prefabricated buildings

With the prefabricated buildings developing, traditional architectural design methods can no longer meet the requirements of efficient, green, and sustainable development. In view of this, based on the analysis of the framework structure of the cyclical parthenogenesis algorithm, the study introduced chaotic optimization algorithm for improvement. And the improved new algorithm was applied to multi-objective problems in the optimization design of prefabricated building structures. Finally, a novel structural design optimization model was proposed. These experiments confirmed that the improved algorithm had the least 160 iterations and 17 optimal solutions, which was an increase of 15 compared to traditional aphid algorithms. Two function solutions of this new structural design optimization model were both between –0:5 and 0.5, with relatively smaller values. In addition, this model could effectively optimize and transform physical buildings, increasing their structural stability by 2.43%, increasing their structural quality coefficient by 4.49%, reducing vibration cycles by 0.06%, and reducing inter story displacement angles by 0.04%. In summary, the improved cyclical parthenogenesis algorithm has good performance in solving multi-objective problems in prefabricated structures, and can quickly and accurately find the global optimal solution. This study aims to provide guidance for the prefabricated building structure design.

Developing an Optimized Energy-Efficient Sustainable Building Design Model in an Arid and Semi-Arid Region: A Genetic Algorithm Approach

The building sector is a major contributor to resource consumption, energy use, and greenhouse gas emissions. Sustainable architecture offers a solution, leveraging Building Energy Modeling (BEM) for early-stage design optimization. This study explores the use of genetic algorithms for optimizing sustainable design strategies holistically. A comprehensive analysis and optimization model was developed using genetic algorithms to individually optimize various sustainable strategies. The optimized strategies were then applied to a pre-existing building in Kabul City, a region facing significant environmental challenges. To enhance accuracy, this study integrated energy simulations with Computational Fluid Dynamics (CFD). This research combines genetic algorithms with energy simulation and CFD analysis to optimize building design for a specific climate. Furthermore, it validates the optimized strategies through a real-world case study building. Optimizing the Window-to-Wall Ratio (WWR) and shading devices based on solar exposure significantly improved the building’s energy performance. South (S)-facing single windows and specific combinations of opposing and adjacent windows emerged as optimal configurations. The strategic optimization of building component materials led to substantial energy savings: a 58.6% reduction in window energy loss, 78.3% in wall loss, and 69.5% in roof loss. Additionally, the optimized pre-existing building achieved a 48.1% reduction in cooling demand, a 97.5% reduction in heating demand, and an overall energy reduction of 84.4%. Improved natural ventilation and controlled solar gain led to a 72.2% reduction in peak-month CO2 emissions. While this study focused on applicable passive design strategies, the integration of advanced technologies like Phase Change Materials (PCMs), kinetic shading devices, and renewable energy systems can further improve building performance and contribute to achieving net-zero buildings.

Accessibility enhancement of mass transit system through GIS based modeling of feeder routes

Access and ingress to and from the metro stations plays a crucial role in elevating the efficacy of transportation systems. This study presents a novel approach to optimize and integrate feeder route network with mass transit system using GIS. An initial accessibility analysis of existing feeder routes revealed only 0.12% of the total population being served. To address the accessibility challenge a comprehensive GIS based feeder route design model based on population data and road network attributes was developed. The model efficiently identified candidate feeder stops which were further subjected to a route analysis model for optimal route design. A total of 29 feeder routes with an average length of 10.2 ​km were identified by the route analysis model. The results demonstrated that the feeder routes obtained through this method exhibited exceptional coverage across the study area effectively bridging the accessibility gaps. The effectiveness of the proposed model was validated through the service area analysis and density analysis, verifying a noticeable increase in route density and substantial population served corresponding to 0.25%. which is doubled as compared to the population served existing feeder routes. The study provides an opportunity for transit authorities to design efficient feeder networks complying with better integration and accessibility.

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.

Hybrid Intelligent optimization model for Animation Design in Virtual Reality and Augmented Reality

Hybrid Intelligent optimization model for Animation Design in Virtual Reality and Augmented Reality

Modeling human decomposition: A Bayesian approach

Environmental and individualistic variables affect the rate of human decomposition in complex ways. These effects complicate the estimation of the postmortem interval (PMI) based on observed decomposition characteristics. In this work, we develop a generative probabilistic model for decomposing human remains based on PMI and a wide range of environmental and individualistic variables. This model explicitly represents the effect of each variable, including PMI, on the appearance of each decomposition characteristic, allowing for direct interpretation of model effects and enabling the use of the model for PMI inference and optimal experimental design. In addition, the probabilistic nature of the model allows for the integration of expert knowledge in the form of prior distributions. We fit this model to a diverse set of 2529 cases from the GeoFOR dataset. We demonstrate that the model accurately predicts 24 decomposition characteristics with an ROC AUC score of 0.85. Using Bayesian inference techniques, we invert the decomposition model to predict PMI as a function of the observed decomposition characteristics and environmental and individualistic variables, producing an R-squared measure of 71 %. Finally, we demonstrate how to use the fitted model to design future experiments that maximize the expected amount of new information about the mechanisms of decomposition using the Expected Information Gain formalism.

A multi-objective optimization model for cropland design considering profit, biodiversity, and ecosystem services

More sustainable agricultural methods are needed to alleviate the decreases in biodiversity and ecosystem services that have occurred because of industrial agriculture. One such method is the inclusion of alternative crops into croplands that can support biodiversity, reduce erosion and chemical runoff, and sequester carbon in the soil. However, the question of where such crops should be planted to balance competing economic and environmental objectives remains open. To this end, we develop a mixed-integer quadratically constrained program to optimize the layout of a cropland considering economic, biodiversity, greenhouse gas emissions, and water quality objectives. We include spatially varying fertilization as a decision variable in addition to crop establishment location. We further include the effect of core area and edges between different crops on biodiversity. To demonstrate the applicability of the model, we apply it to an example field, showing how the optimal cropland design changes as a decision-maker prioritizes different objectives and as edges have different impacts on biodiversity.

Product perceptual design optimization model based on BP neural network

BackgroundUser reviews of online shopping platforms can truly reflect users’ feelings about the use of products. They have the advantages of large sample size, wide range and uniform distribution, and can help optimize the perceptual model of product design.MethodsThe web crawler crawls user comments, and TFIDF-SP quantifies them. The principal component analysis method selects the perceptual evaluation index, the morphological analysis method disassembles the product into several main structures, and the BP neural network constructs the perceptual optimization model.ResultsTaking the paint tray as an example, according to the trained BP neural network, the shape factor combination with the highest perceptual evaluation value is predicted. The experimental results verify the accuracy of the model.ConclusionThe model based on BP neural network has the ability to quickly and accurately combine the best form factors, improves the design efficiency of product design perceptual optimization, improves the rationality of product design, and provides a new idea for consumer demand market-oriented product design.

Data-Driven Models Using Impedance Spectroscopy for Predicting Degradation and Optimizing Electrochemical Systems

As the demand for sustainable energy storage and conversion technologies grows, advanced modeling techniques are essential to optimize the performance, cost, and lifespan of electrochemical systems. Batteries, electrolyzers, and fuel cells experience complex degradation mechanisms, which traditional physics-based models struggle to predict due to the difficulty in measuring all necessary material parameters. Supporting industry partners for this work have identified gaps in plant-level design optimization models, where understanding potential failures and degradation of energy storage technologies is highly sought after. Data-driven models demonstrate the potential to provide elevated confidence to pursue projects with these emerging electrochemical storage technologies.Data-driven models show promise in forecasting issues like battery capacity fade and voltage drop, but they typically require large historical datasets and continuous monitoring, which may not always be feasible. A novel method using Electrochemical Impedance Spectroscopy (EIS) overcomes this limitation, predicting lithium-ion battery degradation without prior cell cycling history or continuous monitoring. EIS measurements capture complex variables contributing to degradation, such as temperature distribution, catalyst agglomeration (in fuel cells and electrolyzers), and solid electrolyte interphase (SEI) growth (in batteries), which are difficult to measure directly. By combining EIS with data-driven approaches, patterns can be identified, providing detailed insights into system health and enabling real-time diagnostics and predictive maintenance, ultimately extending the lifespan of electrochemical devices. With insights into electrochemical degradation mechanisms, EIS interrogated features have proven to be strong indicators of future non-linear battery degradation. We have developed an EIS informed binary classification algorithm for sorting batteries based on their future rate of degradation that yields over 90% accuracy. Moving forward we look to apply these methods to other electrochemical systems as EIS shows promise as a rapid system assessment tool.The HYER (HYdrogen Electrolyzer Research) project, focused on PEM electrolyzers, is currently developing proof-of-concept models, with full models to follow. Accelerated stress tests (ASTs) will evaluate electrolyzer cells and stacks while real-time EIS data is collected. Three machine learning models are being developed from this dataset using techniques such as autoencoders, neural networks, equivalent circuit modeling and time-series feature engineering: (1) a degradation forecasting model that doesn't rely on previous cell history, (2) a lower-resolution predictive tool for rapid prototyping, and (3) a high-resolution time-domain digital twin for capturing current and future cell behavior. These models combine equivalent circuit data from EIS with predictive analytics, offering valuable tools for researchers, OEM’s and operators.This research significantly advances the operando understanding of electrochemical degradation across batteries and electrolyzers. It provides practical tools for real-time diagnostics, predictive modeling, and design optimization, enabling more efficient research and operations for industry and academia. These tools will drive the development of next-generation electrochemical devices while supporting better maintenance and design strategies.

Vehicle Attitude Control of Magnetorheological Semi-Active Suspension Based on Multi-Objective Intelligent Optimization Algorithm

A multi-objective intelligent optimization algorithm-based attitude control strategy for magnetorheological semi-active suspension is proposed to address the vehicle attitude imbalance generated during steering and braking. Firstly, the mechanical properties of the magnetorheological damper (MRD) are tested, and the parameters in the hyperbolic tangent model of the magnetorheological damper are identified through experiments. Secondly, a simulation model of the whole vehicle multi-degree-of-freedom vehicle dynamics including magnetorheological damper is established, and the whole-vehicle Linear Quadratic Regulator (LQR) controller is designed. Then, the optimization design model of the joint vehicle controller and vehicle dynamics is established to design the optimization fitness function oriented to the body attitude control performance, and the attitude optimal controller is calculated with the help of multi-objective intelligent optimization algorithm. Simulation results show that the proposed control method is able to improve the body roll angle, body pitch angle, and suspension dynamic deflection well on the basis of ensuring no deterioration in other performance indexes, ensuring good attitude control capability of the vehicle and verifying the feasibility of the control strategy.

Hybrid regret-based p-robust and distributionally robust optimization models for electric vehicle charging station network design

The rise of electric vehicles (EVs) in recent years has underscored the need for well-established Electric Vehicle Charging Station (EVCS) infrastructure. The lack of a reliable network of EVCS is a significant obstacle to the widespread adoption of EVs. Also, one often overlooked aspect of electric vehicles is the importance of using renewable energy sources to recharge their batteries. Without sustainable energy, EVs could produce more greenhouse gas emissions than traditional vehicles. Therefore, it seems essential to design networks of EVCSs that depend on renewable sources to meet their energy needs and ensure convenient access for customers while reducing environmental impact. This study proposes a two-stage stochastic optimization model to determine the optimal locations and capacities for EVCSs and supporting sustainable energy plants. The model considers uncertain factors such as vehicle flows and renewable energy supply. The model combines a hybrid adjusted p-robust and min–max regret approach to address uncertainties to ensure robustness in the network design process. Two approaches including Sample Average Approximation (SAA) and Fuzzy C-means Clustering are employed to solve the model efficiently. Additionally, the Distributionally Robust Optimization (DRO) approach accounts for uncertainty in the probability distribution function of scenarios. The results of these solution approaches are validated through parameter realization, which involves simulating uncertain parameters and verifying the robustness of the solutions obtained from each approach. This validation process is done by comparing the obtained results with the optimal values based on two criteria: the sum of deviation from optimality and the maximum deviation from optimality. By adopting these solution methods, the proposed methodology contributes to developing efficient and sustainable EV infrastructure. This methodology gives investors and city officials robust decision support for establishing EVCS.