Optimization Algorithm For Design Research Articles

C-ShipGen: a conditional guided diffusion model for parametric ship hull design

Ship design is a complex design process that may take a team of naval architects many years to complete. Improving the ship design process can lead to significant cost savings, while still delivering high-quality designs to customers. A new technology for ship hull design is diffusion models, a type of generative artificial intelligence. Prior work with diffusion models for ship hull design created high-quality ship hulls with reduced drag and larger displaced volumes. However, the work could not generate hulls that meet specific design constraints. This paper proposes a conditional diffusion model that generates hull designs given specific constraints, such as the desired principal dimensions of the hull. In addition, this diffusion model leverages the gradients from a total resistance regression model to create low-resistance designs. Five design test cases compared the diffusion model to a design optimization algorithm to create hull designs with low resistance. In all five test cases, the diffusion model was shown to create diverse designs with a total resistance less than the optimized hull, having resistance reductions over 25%. The diffusion model also generated these designs without retraining. This work can significantly reduce the design cycle time of ships by creating high-quality hulls that meet user requirements with a data-driven approach.

Multi-population particle swarm optimization algorithm for automatic design of steel frames

Multi-population particle swarm optimization algorithm for automatic design of steel frames

A two-loop RBDO approach for steel frame structures using EVPS, GWO, and Monte Carlo simulation

This paper evaluates Enhanced Vibrating Particle System (EVPS) and Grey Wolf Optimizer (GWO) algorithms for Reliability-based Design Optimization (RBDO) of steel frames. The two-loop RBDO method minimizes frame weight while satisfying strength, drift, and reliability constraints, considering uncertainties in material properties and loads. Both EVPS and GWO effectively reduce structure weight and ensure required reliability levels, demonstrating their applicability in sustainable steel frame design.

Seismic design optimization of space steel frame buildings equipped with triple friction pendulum base isolators

To increase the seismic resilience of structures, it is necessary to utilize seismic isolation to increase structural periods and reduce story drifts. When seismic isolation is implemented, structures are capable of deforming by remaining within safe limits. Thus, more seismically resilient designs are achieved compared to conventional fixed-based structures since lighter structural design weights are attained. The principal aim of this study is to achieve the comparative design optimization of space steel frame buildings equipped with seismic base isolation and to examine the effect of seismic base isolation on optimum structural design weight by comparing the same structures with conventional rigid-based ones. For seismic base isolation, triple friction pendulums are implemented into steel frame buildings. For this aim, four different space steel frame buildings with 4-, 6-, 8-, and 10-story are treated as design examples. A novel design optimization algorithm is encoded, which includes nature-inspired metaheuristic artificial bee colony, crow search, and Archimedes optimization algorithms to achieve design optimizations of triple friction pendulums equipped with seismic-isolated space steel frame buildings. The practice code provisions of load and resistance factor design specification for structural steel buildings have been applied to control the structural design constraints of inter-story drift, top-story drift, strength, displacement, and connections geometry of beams and columns. Eventually, it has been remarked that seismic base isolation significantly reduces the structural optimal design weight of space steel frame buildings.

Visual Attention Analysis and Optimization Algorithm in Packaging Design

Visual attention analysis and optimization algorithms in packaging design refer to computational methods aimed at understanding and improving the visual appeal and effectiveness of packaging materials. This study proposes a novel approach for enhancing packaging design through the integration of visual attention analysis and optimization algorithms, employing autoencoder and whale optimization techniques. Visual attention analysis and optimization algorithm in packaging design is to enhance the effectiveness of packaging materials by maximizing their ability to capture and retain consumer attention. This includes identifying key visual elements within packaging designs that are most likely to attract consumers, optimizing the layout and composition of these elements to increase visual impact, and ultimately improving the overall consumer experience and brand perception. Autoencoders are utilized to capture intricate visual features within packaging designs, aiming to create more creative and eye-catching packaging design, while the whale optimization algorithm facilitates the optimization process. Experimental results demonstrate the superior performance of the developed algorithm across various metrics such as accuracy, precision, recall, and f-measure, compared to existing techniques like RNN, CNN, ANN and SVM. The algorithm's scalability and adaptability are further highlighted through consistent performance across increasing data volumes. Overall, this approach holds promise for revolutionizing packaging design by effectively capturing and retaining consumer attention, thereby enhancing brand perception and product satisfaction.

METAL: Methodology for liquid metal fast reactor core economic design and fuel loading pattern optimization

The design of Liquid Metal-cooled Fast Reactor (LMFR) cores encompass two levels of design. The first is the physical core design, which is concerned with the design of the fuel assembly geometry in the context of a full core. The second is the fuel loading design, which is an in-core fuel management problem concerned with the design of fuel enrichment and core loading pattern. In this paper, an optimized fuel design search is developed to improve core loading patterns. As part of the implementation, the multiphysics simulation suite LUPINE has been enhanced to allow for fuel shuffles and an in-core fuel management optimization methodology has been added with the objective of reducing the fuel cost. The design methodology is referred to as Methodology for Economical opTimization of Applied LMFR (METAL), and this methodology is demonstrated using the popular Super Power Reactor Innovative Small Modular (SPRISM) sodium-cooled fast reactor (SFR) as a reference core. One challenge currently facing the deployment of LMFRs is the availability of TRansUranium (TRU) and high assay low enriched uranium (HALEU) driver fuel. The two forms of fuel are expensive and not widely available, which poses a challenge on the startup of LMFR cores. To address the availability of driver fuel, and to lower fuel costs, low enriched uranium (LEU) blankets are investigated as replacements to the natural uranium or depleted uranium blankets typically used. The advantage of this solution is the wide availability of LEU and its ability to lower the amount of TRU or HALEU driver fuel needed. The design objectives, constraints, and optimization algorithm for a SFR are identified. Then, METAL is applied to design a SFR core fueled by TRU driver fuel assemblies and LEU blanket assemblies. To demonstrate the advantage of introducing LEU blankets, METAL is also used to design another SFR core following the same objectives and constraints, but using naturally enriched blankets. By comparing the two cores, it was found that the introduction of LEU blankets results in a ∼28% reduction in the driver fuel mass requirements. Upon development of a levelized fuel cycle cost (LFCC) model, the 28% reduction in driver fuel mass corresponds to a 10% decrease in the LFCC. Additional advantages of using LEU blankets include reducing the core conversion ratio (CR), and improving the assembly radial power peaking factor (RPPF), which enhances the safety and non-proliferation performances of the SFR core.

Implementation of Art Design Optimization Algorithm Based on Big Data and CAD Technology

Implementation of Art Design Optimization Algorithm Based on Big Data and CAD Technology

Application of Landscape Design Optimization Algorithm Based on Big Data in CAD Platform

Application of Landscape Design Optimization Algorithm Based on Big Data in CAD Platform

Key Issues and Solutions in the Study of Quantitative Mechanisms for Tropical Islands Zero Carbon Buildings

Faced with the challenges of global climate change, zero-carbon buildings (ZCB) serve as a crucial means to achieve carbon peak and carbon neutrality goals, particularly in the development of tropical island regions. This study aims to establish a ZCB technology system suitable for the unique climatic conditions of tropical islands. By employing methods such as energy flow boundaries, parametric design, and data-driven optimization algorithms, the research systematically analyzes the integrated mechanisms and optimization solutions for energy utilization, energy conservation, energy production, and intelligent systems. The study identifies and addresses key technical challenges faced by ZCB in tropical island regions, including the accurate identification of system design parameters, the precise quantification of the relationship between design parameters and building performance, and the comprehensive optimization of technical and economic goals for zero-carbon operational design solutions. The research results not only provide a comprehensive theoretical framework, promoting the development of architectural design theory, but also establish a practical framework for technology and methods, advancing the integration and application of ZCB technology. The study holds significant practical implications for the green transformation of the tropical island construction industry and the realization of national dual-carbon strategic goals. Future research should further explore the applicability of the technology system and the economic feasibility of optimized design solutions, promoting continuous innovation and development in ZCB technology.

Derivative-free search approaches for optimization of well inflow control valves and controls

AbstractDecisions regarding problem conceptualization, search approach, and how best to parametrize optimization methods for practical application are key to successful implementation of optimization approaches within georesources field development projects. This work provides decision support regarding the application of derivative-free search approaches for concurrent optimization of inflow control valves (ICVs) and well controls. A set of state-of-the-art approaches possessing different search features is implemented over two reference cases, and their performance, resource requirements, and specific method configurations are compared across multiple problem formulations for completion design. In this study, problem formulations to optimize completion design comprise fixed ICVs and piecewise-constant well controls. The design is optimized by several derivative-free methodologies relying on parallel pattern-search (tAPPS), population-based stochastic sampling (tPSO) and trust-region interpolation-based models (tDFTR). These methodologies are tested on a heterogeneous two-dimensional case and on a realistic case based on a section of the Olympus benchmark model. Three problem formulations are applied in both cases, i.e., one formulation optimizes ICV settings only, while two joint configurations also treat producer and injector controls as variables. Various method parametrizations across the range of cases and problem formulations exploit the different search features to improve convergence, achieve final objectives and infer response surface features. The scope of this particular study treats only deterministic problem formulations. Results outline performance trade-offs between parallelizable algorithms (tAPPS, tPSO) with high total runtime search efficiency and the local-search trust-region approach (tDFTR) providing effective objective gains for a low number of cost function evaluations. tAPPS demonstrates robust performance across different problem formulations that can support exploration efforts, e.g., during a pre-drill design phase while multiple independent tDFTR runs can provide local tuning capability around established solutions in a time-constrained post-drill setting. Additional remarks regarding joint completion design optimization, comparison metrics, and relative algorithm performance given the varying problem formulations are also made.

Benchmarking inverse optimization algorithms for materials design

Machine learning-based inverse materials discovery has attracted enormous attention recently due to its flexibility in dealing with black box models. Yet, many metaheuristic algorithms are not as widely applied to materials discovery applications as machine learning methods. There are ongoing challenges in applying different optimization algorithms to discover materials with single- or multi-elemental compositions and how these algorithms differ in mining the ideal materials. We comprehensively compare 11 different optimization algorithms for the design of single- and multi-elemental crystals with targeted properties. By maximizing the bulk modulus and minimizing the Fermi energy through perturbing the parameterized elemental composition representations, we estimated the unique counts of elemental compositions, mean density scan of the objectives space, mean objectives, and frequency distributed over the materials’ representations and objectives. We found that nature-inspired algorithms contain more uncertainties in the defined elemental composition design tasks, which correspond to their dependency on multiple hyperparameters. Runge–Kutta optimization (RUN) exhibits higher mean objectives, whereas Bayesian optimization (BO) displayed low mean objectives compared with other methods. Combined with materials count and density scan, we propose that BO strives to approximate a more accurate surrogate of the design space by sampling more elemental compositions and hence have lower mean objectives, yet RUN will repeatedly sample the targeted elemental compositions with higher objective values. Our work sheds light on the automated digital design of materials with single- and multi-elemental compositions and is expected to elicit future studies on materials optimization, such as composite and alloy design based on specific desired properties.

Inverse design of a Mamyshev oscillator with MW peak power by a particle swarm optimization algorithm

In this study we demonstrate implementation of a particle swarm optimization algorithm for inverse design of a fiber Mamyshev oscillator. We perform an extensive search in high-dimensional space of cavity parameters to maximize the output pulses’ peak power after external compression. The best obtained pulses have peak power exceeding 2 MW, energy >140nJ, and duration <40fs, and exhibit capabilities of handling ∼75π nonlinear phase accumulation.

Analysis of the Designs and Applications of AI Chip

The rapid evolution of deep learning model architectures and the increasing scale of model parameters have imposed heightened demands on deep learning training, inference, and deployment, leading to the swift advancement and unprecedented prosperity of AI chips. Therefore, this study sets out to analyze the designs and applications of AI chips by considering their unique requirements compared to conventional chips, and by combining software and hardware aspects. The paper delineates the classification of common AI chips along with their distinct design strategies and optimization algorithms. It commences with the fundamental hardware design of AI chips, elucidating the basic design process and addressing the specialized demands of AI computation, particularly data parallelism and storage optimization. Subsequently, transitioning to the manufacturing process, it examines how current AI chips circumvent fabrication bottlenecks and achieve significant breakthroughs in architecture and performance through chip stacking techniques. The paper then bridges hardware and software through the AI compiler, expounding on model optimization approaches, e.g., quantization and pruning, completing the comprehensive journey from AI chip design to deployment. It identifies current developmental challenges in the AI chip realm and provides a glimpse into future prospects. Through a holistic perspective spanning design, manufacturing, algorithms, and applications of AI chips, this paper offers insights that steer upcoming innovations and practical implementations in artificial intelligence, paving the way for a dynamic future in AI chip development.

User behavior data analysis and product design optimization algorithm based on deep learning

User behavior data analysis and product design optimization algorithm based on deep learning

C-Shells: Deployable Gridshells with Curved Beams

We introduce a computational pipeline for simulating and designing C-shells , a new class of planar-to-spatial deployable linkage structures. A C-shell is composed of curved flexible beams connected at rotational joints that can be assembled in a stress-free planar configuration. When actuated, the elastic beams deform and the assembly deploys towards the target 3D shape. We propose two alternative computational design approaches for C-shells: (i) Forward exploration simulates the deployed shape from a planar beam layout provided by the user. Once a satisfactory overall shape is found, a subsequent design optimization adapts the beam geometry to reduce the elastic energy of the linkage while preserving the target shape. (ii) Inverse design is facilitated by a new geometric flattening method that takes a design surface as input and computes an initial layout of piecewise straight linkage beams. Our design optimization algorithm then calculates the smooth curved beams to best reproduce the target shape at minimal elastic energy. We find that C-shells offer a rich space for design and show several studies that highlight new shape topologies that cannot be achieved with existing deployable linkage structures.

The effect of vehicle-to-grid integration on power grid stability: A review

This article explores the promising potential of vehicle-to-grid (V2G) technology, which has attracted considerable interest due to its capacity to enhance power grid stability, decrease carbon emissions, and boost renewable energy utilization. The study begins by examining recent developments in V2G technology, then proceeds to discuss the challenges associated with integrating V2G into the power grid, and finally presents strategies to improve grid stability. Despite the potential of V2G technology, certain challenges must be addressed, including grid stress, complexities in load management, and grid imbalances. The article outlines various strategies to improve power grid stability, such as smart energy metering, time-of-use (TOU) schemes, rolling prediction decision frameworks, and an array of control approaches. Additionally, the research highlights the application of machine learning algorithms for efficient EV scheduling and power control units, voltage stabilization systems, and energy management units to ensure grid reliability. Future research should focus on developing scalable infrastructure solutions, establishing universal standards, designing optimization algorithms, and devising strategies to enhance battery life, to facilitate efficient V2G integration.

Automated actual evapotranspiration estimation: Hybrid model of a novel attention based U-Net and metaheuristic optimization algorithms

Actual evapotranspiration (ETa) plays a crucial role in the water and energy cycles of the earth. An accurate estimate of the ETa is essential for management of the water resources, agriculture, and irrigation, as well as research on atmospheric variations. Despite the importance of accurate ETa values, estimating and mapping them remains challenging due to the physical and biological complexity of the ET process. As a novel approach for rapid and reliable estimation of ETa, the present study develops automated deep learning (AutoDL) models that incorporate a metaheuristic optimization algorithm for image processing, architectural design, and hyperparameter tuning. The proposed AutoDL models integrate three different spatial and channel attention mechanisms, including a novel activated spatial attention mechanism (ASPAM), with the U-Net architecture. Bypassing the need for meteorological inputs, the proposed framework uses Moderate Resolution Imaging Spectrometer (MODIS) products and Digital Elevation Model (DEM) data as inputs. To evaluate the performance of the models, they are applied to three study areas in the United States with various climatic characteristics. According to the results, during the spring and summer, when the target values have higher certainty, the estimations are highly promising, with R2 as high as 0.91 and MAPE as low as 6.40%. Furthermore, the proposed ASPAM module improves the accuracy of ETa estimations compared to attention gate (AG) and squeeze and excitation (SE) attention modules. The results also indicate that the MODIS raw products and derived vegetation and water indices can predict ETa within a reliable range of accuracy, with the addition of DEM data marginally enhancing the models' performance. The automatic workflow of this model makes it significantly easy to use, contributing to its applicability and generalizability for enhancing atmospheric research.

A Review of Optimization for Corrugated Boards

This paper presents a comprehensive review of optimization practices in the corrugated board industry, which has recently experienced significant interest in using optimization methodologies driven by sustainable demands and increasing computational capabilities. The authors cover different review perspectives, including historical context, manufacturing applications, design optimization, and numerical optimization algorithms used. The main findings of this study indicate that the corrugated board industry has experienced a shift from trial-and-error and expert-driven approaches to data-centric strategies, particularly since the beginning of the 21st century. Interestingly, the industry has also adopted Multi-Disciplinary Optimization techniques from other fields, which demonstrates the importance of knowledge convergence across sectors. However, due to the complex nature of corrugated boards—including materials, design, and manufacturing processes—there is still much research to be done in this area. This work provides guidance for future research directions and encourages innovation and improvement in corrugated board optimization practices. In particular, the strong developments of material models for paper in recent years will boost the use of optimization tools in this field.

Joint Base Station and IRS Deployment for Enhancing Network Coverage: A Graph-Based Modeling and Optimization Approach

Intelligent reflecting surface (IRS) can be densely deployed in complex environment to create cascaded line-of-sight (LoS) paths between multiple base stations (BSs) and users via tunable IRS reflections, thereby significantly enhancing the coverage performance of wireless networks. To achieve this goal, it is vital to optimize the deployed locations of BSs and IRSs in the wireless network, which is investigated in this paper. Specifically, we divide the coverage area of the network into multiple non-overlapping cells and decide whether to deploy a BS/IRS in each cell given a total number of BSs/IRSs available. We show that to ensure the network coverage/communication performance, i.e., each cell has a direct/cascaded LoS path with at least one BS, as well as such LoS paths have the average number of IRS reflections less than a given threshold, there is a fundamental trade-off with the deployment cost or the number of BSs/IRSs needed. To optimally characterize this trade-off, we formulate a joint BS and IRS deployment problem based on graph theory, which, however, is difficult to be optimally solved due to the combinatorial optimization involved. To circumvent this difficulty, we first consider a simplified problem with given BS deployment and propose the optimal as well as an efficient suboptimal IRS deployment solution to it, by applying the branch-and-bound method and iteratively removing IRSs from the candidate locations, respectively. Next, an efficient sequential update algorithm is proposed for solving the joint BS and IRS deployment problem. Numerical results are provided to show the efficacy of the proposed design approach and optimization algorithms for the joint BS and IRS deployment. The trade-off between the network coverage performance and the number of deployed BSs/IRSs with different cost ratios is also unveiled.

A data-driven discrete simulation-based optimization algorithm for car-sharing service design

This paper formulates a discrete simulation-based optimization (SO) algorithm for a family of large-scale car-sharing service design problems. We focus on the profit-optimal assignment of vehicle fleet across a network of two-way (i.e., round-trip) car-sharing stations. The proposed approach is a metamodel SO approach. A novel metamodel based on a mixed-integer program (MIP) is formulated. The metamodel is embedded within a general-purpose discrete SO algorithm. The proposed algorithm is validated with synthetic toy network experiments. The algorithm is then applied to a high-dimensional Boston case study using reservation data from a major US car-sharing operator. The method is benchmarked versus several algorithms, including stochastic programming. The experiments indicate that the analytical network model information, provided by the MIP to the SO algorithm, is useful both at the first iteration of the algorithm and across subsequent iterations. The solutions derived by the proposed method are benchmarked versus the solution deployed in the field by the car-sharing operator. Via simulation, the proposed solutions improve those deployed with an average improvement of profit of 6% and of vehicle utilization of 3%.The combination of the problem-specific analytical MIP with a general-purpose SO algorithm enables the discrete SO algorithm to: (i) address high-dimensional problems, (ii) become computationally efficient (i.e., it can identify good quality solutions within few simulation observations), (iii) become robust to the quality of the initial points and of the stochasticity of the simulator. More generally, the information provided by the MIP to the SO algorithm enables it to exploit problem-specific structural information. This leads to an algorithm with both asymptotic convergence guarantees as well as good short term performance (i.e., performance given few simulation observations). We view this general idea of combining analytical MIP formulations with general-purpose SO algorithms, or more broadly with general-purpose sampling strategies of high-resolution data, as an innovative and promising area of future research.