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The weight and number of batteries in the battery engineering vehicle are large, and the overall performance of the vehicle is greatly affected by their layouts. In order to optimize the battery layout of the frame and address the problem of frequently invoking finite element simulation during the optimization process, a battery layout optimization design method combining the response surface approximation model and multi-island genetic algorithm was proposed. Firstly, the parameter optimization model of the battery engineering vehicle frame was established. And the second-order response surface approximation model of the design variables and the objective function was established. Then, the multi-island genetic algorithm was used to solve the optimization model. The dynamic index was used to evaluate the advantages and disadvantages of the solution, and an optimal solution was selected as the optimal battery layout scheme. Finally, the scale frame model was used to verify the optimization results. The results show that the maximum deformation of the frame after battery optimization is reduced by 50. 9%, the maximum equivalent force is decreased by 37.9%, the maximum vertical vibration acceleration is reduced by 14. 6%, and the error between the simulation and test results is 7.1%. The purpose of the optimized design is achieved. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim is to reduce the elastic deformation of the web and side walls of low-stiffness thin-walled beams when the floating fixture method is used. This paper takes the number and position of fixture points as the optimization variables, establishes a calculation model of elastic deformation, and constructs the objective function of maximum total elastic deformation. An optimized solution utilizing the augmented multiplier method is employed, which forms the basis for the fixture layout optimization method to reduce the elastic deformation of low-stiffness thin-walled beams. A theoretical calculation, simulation analysis, and the fixture layout optimization of total maximum elastic deformation were completed using an aluminum alloy low-stiffness thin-walled beam as an example. The results show that based on the optimized layout, the average relative error between the calculated value and the simulated value of total maximum elastic deformation is 17.43%, and the simulated value of maximum elastic deformation is reduced by 48.49% after optimizing the fixture layout. The measured value is reduced by 0.02 mm on average, and deformation is reduced by 74.07%, which verifies the effectiveness of the floating fixture layout optimization control of machining elastic deformation proposed in this paper. [ABSTRACT FROM AUTHOR]

Transforming hand-drawn sketches into digital wireframes is a pivotal yet time-intensive step in the early stages of User Interface (UI) design. Despite advancements in technology aiming to streamline this task, challenges arise from the multi-scale variations of UI elements and inherent imperfections in the geometric information of sketches. We introduce Sketch2Wireframe, a tool designed to swiftly transform these sketches into refined digital wireframes. The process unfolds in two primary phases: UI element detection and UI layout optimization. First, we propose an object detection model named Attention-based Multi-Scale Region-based Convolutional Neural Network (AMS-RCNN). This model efficiently detects multi-scale elements in sketches by leveraging multi-scale convolution and an attention mechanism, ensuring rich feature extraction for prediction. We then implement a hybrid layout optimization method, which initially utilizes heuristic rules to recognize hierarchy and element organization from sketches, followed by an Integer Programming (IP) approach to globally optimize the layout in compliance with design guidelines. Experimental findings suggest that the AMS-RCNN outperforms baseline models, evidencing a 2.2% mAP increase in element detection. Moreover, our hybrid layout optimization method considerably outperforms the heuristic methods in terms of the perceived quality of the layouts. [ABSTRACT FROM AUTHOR]

Beam–slab structures account for 50–65% of a building's total dead load and contribute to 20% of the overall cost and CO2 emissions. Despite their importance, conventional beam–slab structural optimization methods often lack search efficiency and accuracy, making them less effective for practical engineering applications. Such limitations arise from the optimization problem involving a complex solution space, particularly when considering components' arrangement, dimensions, and load transfer paths simultaneously. To address the research gap, this study proposes a novel two-stage genetic algorithm, optimizing beam–slab layout in the first stage and component topological relationships and dimensions in the second stage. Numerical experiments on the prototype case indicate that the algorithm can generate results that meet engineering accuracy requirements within 100 iterations, outperforming comparable algorithms in both efficiency and accuracy. Additionally, this heuristic approach stands out for its independence from prior dataset training and its minimal parameter adjustment requirement, making it highly accessible to engineers without programming expertise. Statistical analysis of the algorithm's optimization process and case studies demonstrate its robustness and adaptability to various beam–slab structural optimization problems, revealing its significant potential for practical engineering scenarios. [ABSTRACT FROM AUTHOR]

Rockfall poses a formidable threat to the ongoing fast-paced construction of large-scale projects in uninhabited areas in high mountain valleys. In this study, an optimization method for arranging passive nets on high and steep slopes was presented to mitigate the threat from rockfalls. This method diverges from the conventional method of subjectively arranging passive nets along the perimeter of protected regions (due to its emphasis on cost considerations), in which the quantitative appraisal of rockfall movement characteristics and interception rates is frequently omitted, consequently failing to comprehensively ensure transportation routes and temporary construction sites. The methodology encompasses the acquisition of terrain data by unmanned aerial vehicles (UAVs), identification of rockfall sources based on UAV point clouds, quantitative assessment of rockfall hazards using a 3D probabilistic model, and optimization of the layout of passive nets based on the assessment results. The aim of the optimization of passive nets is to quantitatively assess the cost–effect relationship of passive nets, accounting for construction feasibility, interception potential, and likelihood of successful rockfall interception. We applied this method to the Feishuiyan slope in southwest China as an example, and the results demonstrated an enhanced interception rate of 99% and cost reduction by a factor of three relative to the original scheme. This innovative approach could enhance rockfall mitigation in high and steep areas, providing a viable strategy for future prevention efforts in these areas. [ABSTRACT FROM AUTHOR]

Efficient healthcare facility design is crucial for providing high-quality healthcare services. This study introduces an innovative approach that integrates artificial intelligence (AI) algorithms, specifically particle swarm optimization (PSO), with building information modeling (BIM) and digital twin technologies to enhance facility layout optimization. The methodology seamlessly integrates AI-driven layout optimization with the robust visualization, analysis, and real-time capabilities of BIM and digital twins. Through the convergence of AI algorithms, BIM, and digital twins, this framework empowers stakeholders to establish a virtual environment for the streamlined exploration and evaluation of diverse design options, significantly reducing the time and manual effort required for layout design. The PSO algorithm generates optimized 2D layouts, which are seamlessly transformed into 3D BIM models through visual programming in Dynamo. This transition enables stakeholders to visualize, analyze, and monitor designs comprehensively, facilitating well-informed decision-making and collaborative discussions. The study presents a comprehensive methodology that underscores the potential of AI, BIM, and digital twin integration, offering a path toward more efficient and effective facility design. [ABSTRACT FROM AUTHOR]

Abstract The variational autoencoder (VAE) architecture has significant advantages in predictive image generation. This study proposes a novel RFCNN-βVAE model, which combines residual-connected fully connected neural networks with VAE to handle multi-heat source arrangements. By integrating analytical solutions, polynomial fitting, and temperature field superposition, we accurately simulated the temperature rise distribution of a single heat source. We further explored the use of multiple equivalent heat sources to replace adiabatic boundary conditions. This enables the analytical method to effectively solve the two-dimensional conjugate convective heat transfer problem, providing a reliable alternative to traditional numerical solutions. Our results show that the model achieved high predictive accuracy. By adjusting the β parameter, we balanced reconstruction accuracy and latent space generalization. During the stable phase of the multi-heat source optimization iteration, 73.4% of the results outperformed the dense dataset benchmark, indicating that the model successfully optimized heat source coordinates and minimized peak temperature rise. This validates the feasibility and effectiveness of deep learning in thermal management system design. This research lays the groundwork for optimizing complex thermal environments and contributes valuable perspectives on the effective design of systems with multiple thermal sources or for applications like multi-beam lighting equalization.

Abstract The lockbolt structure is essential in railway wagons, and a scientific lockbolt layout can ensure uniform load distribution, thereby preventing failure. However, current engineering lacks layout optimization methods that address multidimensional failure modes. This paper presents a new lockbolt structure layout optimization method based on submodel, parametric models, and a multi-strategy integrated NSGA-III (MSNSGA-III), adhering to the DVS EFB 3435-2 standard. This method simultaneously optimizes the number and spacing of lockbolts to prevent tensile, bearing, shear, and other static failure modes under specified load conditions. The proposed method was applied during the design phase of a container flatcar. Optimization results indicate that, compared to NSGA-III, this method achieves the best IGD and HV values across multiple complex test functions, demonstrating superior performance in solving complex Pareto front optimization problems. Additionally, the optimized lockbolt structure's safety margins increased by a maximum of 59.81%, passing the full vehicle strength test and significantly enhancing resistance to multidimensional failure modes. These results highlight the method's significant practical application value in addressing the optimization of railway wagon lockbolt structures under complex multidimensional failure modes.

Aimed at the problem of low efficiency of ship pipeline design, an optimization method of pipeline layout is proposed. An optimization mathematical model is established by comprehensively considering the engineering background of safety, economy, coordination and operability, and the defects of ant colony optimization algorithm in dealing with mixed pipeline layout conditions are improved. A spatial state transition strategy for optimizing feasible solution search, a pheromone diffusion mechanism for improving pheromone inspiration effect and accelerating algorithm convergence are proposed, and a multi-ant colony co-evolution mechanism is designed for mixed pipeline layout conditions. Based on the secondary development technology, the application of this method in the third-party design software is realized, and verified by a nuclear primary pipeline layout project. The results show that the pheromone Gaussian diffusion multi ant colony optimization (PG-MACO) algorithm has a better performance and layout effect than the traditional ant colony algorithm. The routing efficiency is improved by 58.38%, the convergence algebra is shortened by 43.24%, the pipeline length is shortened by 33.88%, and the number of pipeline bends is reduced by 41.67%, which verifies the effectiveness and engineering practicability of the proposed method.

With the growing need for renewable energy, wind farms are playing an important role in generating clean power from wind resources. The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency. This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization (PSO) and artificial neural networks (ANNs). The PSO method was used to explore the solution space and develop preliminary turbine layouts, and the ANN model was used to fine- tune the placements based on the predicted energy generation. The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits. The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches, giving exciting prospects for developing more efficient and sustainable wind farms. The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques. Furthermore, this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency. A notable increase in power generation is observed across various scenarios. The percentage increase in the power generation ranged from approximately 7.7% to 11.1%. Owing to its versatility and adaptability to site-specific conditions, the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

Hybrid manufacturing combines manufacturing processes (typically additive manufacturing and machining) exploiting the benefits of each and enabling repair scenarios. Such an approach can be integrated with a robot, and if made portable, can form a flexible machine tool that can be easily transported anywhere to enable repairs in the field. However, the design of the load-bearing structure determines its transportability, and its stiffness plays a crucial functional role under dynamic loads and affects the product quality. Finding the right balance between weight and stiffness requires accurate boundary conditions and an effective design. In this work, a method is proposed towards process-driven optimization of a portable manufacturing cell structure. The dynamic cutting forces are determined through a machining process model and, via a simplified model of the robot arm, the forces at the base of the robot are estimated. Since the frame consists of beams, the layout optimization method is applied, using the estimated process forces as boundary conditions to optimize the arrangement of beams. The proposed method achieved 0.05 mm displacement in the load-bearing structure under milling and an acceptable accuracy of the position of a hole’s center during drilling, while the overall weight reduced by 17.6%.

The oil and gas field surface gathering pipeline network is an important hub for transporting oil and gas products outward, and its optimal design is crucial to reducing the construction cost of the pipeline system. Star-star topology is often applied to the layout of surface gathering pipeline networks in oil and gas fields. However, the optimization of star-star topology is often carried out under the constraint of the fixed station type, which may easily lead to a waste of station capacity and increase investment or make the optimization results meaningless. In this article, a mixed integer linear programming mathematical model for the overall optimization of star-star pipeline network layout considering the station location-allocation problem is constructed in discrete space with coupled station type constraints, and the branch-and-bound method is used to obtain the global optimal solution. Three cases verify the feasibility and effectiveness of the model and algorithm. The results show that the maximum avoided capacity waste is 20 × 104 m3/d and the maximum investment can be reduced by 8961.6 × 104 Yuan, which shows that the proposed model can effectively improve the economy and rationality of the pipeline system, and has important guiding significance for the construction of oil and gas fields. [ABSTRACT FROM AUTHOR]

Optimizing the layout of agricultural and fishery land utilization space in coastal tidal flats is an essential step in promoting the construction of“Blue Granary”and the sustainable utilization of tidal flat resources. Based on remote sensing and GIS technology, this study revealed the evolution characteristics of agricultural and fishery spatial utilization pattern in the tidal flat reclamation area in the southern Yellow River Delta from 1995 to 2020, as well as combined ecological suitability assessment, ecological network construction, and land use change model. The main land use change pattern of tidal flat agriculture and fishery under different development scenarios in 2035 was predicted by simulation and suggestions for spatial layout optimization and adjustment were proposed. The results showed that: during the study period, the total area of agricultural planting and mariculture utilization of tidal flats increased first and then decreased, which grew from 924.62 km² in 1995 to 1 224.96 km² in 2004, with the scale beginning to decrease after 2004, and dedined to 1 129.38 km² in 2020. The spatial distribution pattern of paddy field and aquaculture pond changed noticeably, as did the spatial aggregation of dry land, the open aquaculture in the sea area gradually expanded away from the coast, and the spatial distribution of main agricultural and fishery utilization in the tidal flat area demonstrated clear land-sea gradient differentiation. The current distribution of agricultural planting and mariculture is ecologically suitable, however, the future growth space of agricultural planting is limited, and the development potentials of beach pond aquaculture and open aquaculture remain certain. Strict environmental protection measures for wetlands limit the expansion of agricultural planting and mariculture utilization space. The land use scale of beach agricultural planting and mariculture will increase significantly under the development scenario of agriculture and fishery, however, attention must be paid to the rational development of unused land, the improvement of the ecological connectivity of beach land and sea, and the application and promotion of efficient ecological agricultural planting and mariculture utilization model. The results may provide decision support for the high-quality agriculture and fishery development in the Yellow River Delta. [ABSTRACT FROM AUTHOR]

Currently, most studies on the optimization of wind farm layouts on flat terrain employ a discrete grid-based arrangement method and result in irregular layouts that may damage the visual appeal of wind farms. To meet the practical requirements of wind farms, a two-step optimization method called "grid–coordinate" based on a genetic algorithm is proposed in this paper. The core idea is to initially determine the number of wind turbines and their initial positions using a grid-based approach, followed by a fine-tuning of the wind farm layout by moving the turbines in a row/column manner. This two-step process not only achieves an aesthetically pleasing arrangement but also maximizes power generation. This algorithm is conducted to optimize a 2 km × 2 km wind farm under three classic wind conditions, one improved wind condition, and a real wind condition employing both the Jensen and Gaussian wake models. To validate the effectiveness of the proposed method, the optimization of configurations based on different wake models was conducted, yielding results including the efficiency, total power output, number of wind turbines, and unit cost of electricity generation. These results were compared and analyzed against the classical literature. The findings indicate that the unit cost of electricity generation using the two-step optimization approach with the Gaussian wake model is higher than that of the initial grid optimization method. Additionally, varying the number of wind turbines can lead to instances of high power generation coupled with low efficiency. This phenomenon should be carefully considered in the wind farm layout optimization process. [ABSTRACT FROM AUTHOR]

With the rapid development of the logistics industry, the demand for logistics activities is increasing significantly. Concurrently, growing urbanization is causing the space for logistics and warehousing to become limited. Thus, more and more attention is being paid to the planning and construction of logistics facilities. However, due to spatiotemporal trajectory data (such as truck GPS data) being used less often in planning, the method of quantitative analysis for freight spatiotemporal activity is limited. Thus, the spatial layout of logistics and warehousing land does not match the current demand very well. In addition, it is necessary to consider the interactive relationship with the urban built environment in the process of optimizing layout, in order to comprehensively balance the spatial coupling with the functions of housing, transportation, industry, and so on. Therefore, the layout of logistics and warehouse land could be treated as a multi-objective optimization problem. This study aims to establish a model for logistics and warehouse land layout optimization to achieve a supply–demand matching. The proposed model comprehensively considers economic benefits, time benefits, cost benefits, environmental benefits, and other factors with freight GPS data, land-use data, transportation network data, and other multi-source data. A genetic algorithm is built to solve the model. Finally, this study takes the Wuhan urban development area as an example to practice the proposed method in three scenarios in order to verify its effectiveness. The results show that the optimization model solves the problem of mismatch between the supply and demand of logistics spaces to a certain extent, demonstrating the efficiency and scientificity of the optimization solutions. Based on the results of the three scenarios, it is proven that freight activities could effectively enhance the scientific validity of the optimization solution and the proposed model could optimize layouts under different scenario requirements. In summary, this study provides a practical and effective tool for logistics- and warehouse-land layout evaluation and optimization for urban planners and administrators. [ABSTRACT FROM AUTHOR]

To reduce the impact of the cold chain logistics center layout on economic benefits, operating efficiency and carbon emissions, a layout optimization method is proposed based on the improved dung beetle algorithm. Firstly, based on the analysis of the relationship between logistics and non-logistics, a multi-objective optimization model is established to minimize the total logistics cost, maximize the adjacency correlation and minimize the carbon emissions; secondly, based on the standard Dung Beetle Optimization (DBO) algorithm, in order to further improve the global exploration ability of the algorithm, Chebyshev chaotic mapping and an adaptive Gaussian–Cauchy hybrid mutation disturbance strategy are introduced to improve the DBO (IDBO) algorithm; finally, taking an actual cold chain logistics center as an example, the DBO algorithm and the improved DBO algorithm are applied to optimize its layout, respectively. The results show that the total logistics cost after optimization of the IDBO algorithm is reduced by 25.54% compared with the original layout, the adjacency correlation is improved by 29.93%, and the carbon emission is reduced by 6.75%, verifying the effectiveness of the proposed method and providing a reference for the layout design of cold chain logistics centers. [ABSTRACT FROM AUTHOR]

This paper provides a rigorous and computationally efficient means of identifying compliance-based optimal truss topologies from high-resolution ground structures—problems involving over 30 million potential elements can be solved in under 1 h on a typical laptop. The adaptive ‘member adding’ approach is shown to provide significant savings in computational time and memory usage compared to directly solving the full optimization problem, whilst obtaining the same optimal solution; this paper presents the first application of this powerful principle to the well-known linear-elastic design problem. The computational advantages are particularly notable for multiple load-case problems, as the numerical structure of these cannot be effectively exploited without understanding of the physical nature of the problem. For such cases, the member adding process reduces the computational time required from approximately O (m 2) to O(m), where m is the number of potential elements; here, the time required is reduced by a factor of up to 60 for the relatively small problems that could be solved with both approaches. By using the member adding approach, compliance-optimized structures are obtained at a significantly higher resolution than has previously been possible. The findings of this paper have the potential to deliver a step change in the size of problems that can be solved in compliance-based truss topology optimization, and an accompanying Python code is provided to facilitate this. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the growing need for renewable energy, wind farms are playing an important role in generating clean power from wind resources. The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency. This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization (PSO) and artificial neural networks (ANNs). The PSO method was used to explore the solution space and develop preliminary turbine layouts, and the ANN model was used to finetune the placements based on the predicted energy generation. The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits. The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches, giving exciting prospects for developing more efficient and sustainable wind farms. The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques. Furthermore, this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency. A notable increase in power generation is observed across various scenarios. The percentage increase in the power generation ranged from approximately 7.7% to 11.1%. Owing to its versatility and adaptability to site-specific conditions, the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future. [ABSTRACT FROM AUTHOR]

This paper presents a non-uniform curved grid-stiffened shell design method aiming to enhance structural performance using various stiffener patterns, allowing simultaneous optimization of stiffener thickness and stiffener layout. Firstly, the grid-stiffened cell description function is defined using quadratic polynomial functions, comprising the orthogrid, the triangle grid, the rotated triangle grid, and the Kagome grid. Then, the non-uniform stiffener layout description function is established using the sawtooth function, while a filter function is employed to ensure the smooth and continuous of the stiffeners. Moreover, the analytical sensitivity is thoroughly derived, and the optimization problem is formulated. Finally, the effectiveness of the proposed method is demonstrated through three representative optimization examples: the cantilever beam, the special-shaped plate, and the S-shape shell. The study concludes that the proposed method can optimize arbitrary flat plates by embedding the design domain into the background domain. Additionally, the proposed method can be extended to perform stiffener design on complex surfaces by establishing projection relationship between the flat surface and the curved surface. Optimization results indicate that the non-uniform curved grid-stiffened shell design exhibits superior structural performance compared to the uniform grid-stiffened shell design. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Village classification is an important foundation for preparing village planning and formulating development strategies. This study takes a typical plateau mountain area-Yulong County, Yunnan Province as the research object. First, the analytical hierarchy process is used to build a “three evaluations” (ecological sensitivity, development potential, and construction suitability)indicator system. Second, spatial correlation analysis of the “three evaluations” was carried out to obtain the initial village classification results. Finally, the state space method was used to calculate the village bearing capacity, which could be used to correct the initial village classification. Thus, the final village classification could be obtained. The results show that a significant plateau mountain space distribution of heterogeneity was shown in the villages of the research area, and the spatial autocorrelation coefficients of the “three evaluations” were 0.343, 0.253, and 0.110, respectively; more than 60% of the villages were in a bearing state and the higher bearing capacity was concentrated in the southeast, northwest, and central regions of the research area. After the comprehensive carrying capacity was calculated and corrected, the special protection category accounted for 23%, the suburban integration category accounted for 4%, the relocation and withdrawal category accounted for 15%, the agglomeration and promotion category accounted for 40%, and the temporary unclear category accounted for 18%. For different types of villages, the layout optimization strategy should be proposed according to the carrying capacity calculation results. The results of this research can provide a reference and basis for the formulation of township(town)-level territorial spatial planning and the implementation of “multi-planning” practical village planning. [ABSTRACT FROM AUTHOR]

Population aging is a global issue. China is facing the same challenge, especially in its megacities, with more than 10 million permanent urban residents. These densely populated cities urgently need the scientific planning and optimization of the layout of service facilities for older people. Taking Guangzhou, a megacity in China, as an example, this study uses point-of-interest (POI) data and the ID3 machine learning decision tree algorithm to train a site selection model for service facilities for older people. The model can help to select appropriate locations for new service facilities for older people more scientifically and accurately, and it can provide targeted suggestions to optimize the layout of the service facilities for older people in Guangzhou. First, Guangzhou city is divided into 29,793 grids of 500 m × 500 m based on the range of activities of older people, and 985 grids are found to contain service facilities for older people. Then, the POI data of the grid are fed into the ID3 algorithm for training to obtain a prediction model for the selection of sites for service facilities for older people. The effective prediction rate of the model reaches 87.54%. Then, we apply the site selection model to predict the whole city of Guangzhou, and 4534 grids are suitable for service facilities for older people. In addition, considering the degree of concentration of the elderly population in each street, we further filter out 1066 priority grids as the final site selection. Finally, taking into account the situation of the streets in different districts, we propose several strategies to optimize the layout of the construction of service facilities for older people. [ABSTRACT FROM AUTHOR]

Abstract System design is a challenging and time-consuming task which often requires close collaboration between several multidisciplinary design teams to account for complex interactions between components and sub-systems. As such, there is a growing demand in industry to create better performing, efficient, and cost-effective development tools to assist in the system design process. Additionally, the ever-increasing complexity of systems today often necessitates a shift away from manual expertise and a movement towards computer-aided design tools. This work narrows the scope of the system design process by focusing on one critical design aspect: the packaging of system components. The algorithm presented in this paper was developed to optimize the packaging of system components with consideration of practical, system-level functionalities and constraints. Using a dynamic acceleration methodology, the algorithm packages components from an initial position to a final packed position inside of a constrained volume. The motion of components from initial to final positions is driven by several acceleration forces imposed on each component. These accelerations are based on physical interactions between components and their surrounding environment. Various system-level performance metrics such as center of mass alignment and rotational inertia reduction are also considered throughout optimization. Results of several numerical case studies are also presented to demonstrate the functionality and capability of the proposed packaging algorithm. These studies include packaging problems with known optimal solutions to verify the efficacy of the algorithm. Finally, the proposed algorithm was used in a more practical study for the packaging of an urban air mobility nacelle to demonstrate the algorithm’s prospective capabilities in solving real-world packaging problems.

To solve the problems of empiricism, subjectivity and randomness in the manual layout of human machine interface in wheelhouse, an optimization layout design method of human machine interface that combines virtual simulation and optimization algorithm is proposed. First, the optimal operating point and the operating comfort of each area of the human machine interface are obtained through virtual simulation. Then, the layout criteria and ergonomics criteria of human machine interface are quantified as objective functions and constraints, a layout optimization mathematical model of human machine interface is established, and a large number of initial positions of the particles in the particle swarm optimization (PSO) algorithm are obtained through the differential evolution algorithm. Finally, the optimal layout scheme is obtained, and virtual simulation evaluation is conducted. Taking a control panel as an example for layout optimization and virtual simulation, the optimal layout scheme obtained is proved to satisfy ergonomic criteria, and the performance of the human machine interface has been improved. This paper can provide references for the layout design of the high performance human machine interface in wheelhouse.

Socio-economic development has accelerated urban expansion, leading to the encroachment of urban space into agricultural space land and seriously undermining the stability of ecosystems. Scientifically optimizing the layout of urban-agricultural space can guarantee China's food security, promote urban development and maintain ecological stability. In this study, we focused on the rapidly growing city of Nankang. We used land, ecological and geographic location data to calculate land suitability, predicted future urban space through the GeoSOS-FLUS model, and finally optimized the agricultural-urban space conflict. The results show that: the areas of farmland with quality evaluation results for grades 1–4 were 7783.13 hm2, 9411.01 hm2, 8738.99 hm2 and 3032.73 hm2, and the areas suitable for construction, more suitable for construction, unsuitable for construction and prohibited from construction accounted for 12.19 %, 16.23 %, 41.34 % and 30.24 %, respectively. Combining the FLUS model with the land suitability evaluation results, we found that there is a conflict area of 1841.96 hm2 in the delineation scheme. Adjusting their layout according to the principle of conflict coordination, the optimized area of agricultural-urban space is 28429.34 hm2 and 8968.23 hm2 respectively, accounting for 16.32 % and 5.15 % of the study area. The result of spatial contradiction optimization satisfies the balance between production and residential space, promotes urban development, protects ecological security, and can provide scientific reference for regional urban ecological environment construction.

System design is a challenging and time-consuming task which often requires close collaboration between several multidisciplinary design teams to account for complex interactions between components and sub-systems. As such, there is a growing demand in industry to create better performing, efficient, and cost-effective development tools to assist in the system design process. Additionally, the ever-increasing complexity of systems today often necessitates a shift away from manual expertise and a movement towards computer-aided design tools. This work narrows the scope of the system design process by focusing on one critical design aspect: the packaging of system components. The algorithm presented in this paper was developed to optimize the packaging of system components with consideration of practical, system-level functionalities and constraints. Using a dynamic acceleration methodology, the algorithm packages components from an initial position to a final packed position inside of a constrained volume. The motion of components from initial to final positions is driven by several acceleration forces imposed on each component. These accelerations are based on physical interactions between components and their surrounding environment. Various system-level performance metrics such as center of mass alignment and rotational inertia reduction are also considered throughout optimization. Results of several numerical case studies are also presented to demonstrate the functionality and capability of the proposed packaging algorithm. These studies include packaging problems with known optimal solutions to verify the efficacy of the algorithm. Finally, the proposed algorithm was used in a more practical study for the packaging of an urban air mobility nacelle to demonstrate the algorithm's prospective capabilities in solving real-world packaging problems. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive investigation into the application of the Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for the sizing and layout optimization of truss benchmark structures. Truss structures play a crucial role in engineering and architecture, and optimizing their designs can lead to more efficient and cost-effective solutions. The ISRES algorithm, known for its effectiveness in multi-objective optimization, is adapted for the single-objective optimization of truss designs with multiple design constraints. This study encompasses a wide range of truss benchmark structures, including 10-bar, 15-bar, 18-bar, 25-bar, and 72-bar configurations, each subjected to distinct loading conditions and stress constraints. The objective is to minimize the truss weight while ensuring stress and displacement limits are met. Through extensive experimentation, the ISRES algorithm demonstrates its ability to efficiently explore the solution space and converge to optimal solutions for each truss benchmark structure. The algorithm effectively handles the complexity of the problems, which involve numerous design variables, stress constraints, and nodal displacement limits. A comparative analysis is conducted to assess the performance of the ISRES algorithm against other state-of-the-art optimization methods reported in the literature. The comparison evaluates the quality of the solutions and the computational efficiency of each method. Furthermore, the optimized truss designs are subjected to finite element analysis to validate their structural integrity and stability. The verification process confirms that the designs adhere to the imposed constraints, ensuring the safety and reliability of the final truss configurations. The results of this study demonstrate the efficacy of the ISRES algorithm in providing practical and reliable solutions for the sizing and layout optimization of truss benchmark structures. The algorithm's competitive performance and robustness make it a valuable tool for structural engineers and designers, offering a versatile and powerful approach for complex engineering optimization tasks. Overall, the findings contribute to the advancement of optimization techniques in structural engineering, promoting the development of more efficient and cost-effective truss designs for a wide range of engineering and architectural applications. The study's insights empower practitioners to make informed decisions in selecting appropriate optimization strategies for complex truss-design scenarios, fostering advancements in structural engineering and sustainable design practices. [ABSTRACT FROM AUTHOR]

Agricultural production has a very serious "spatial dislocation and functional disorganization" in China at present. It is of great significance to optimize the spatial layout of farmland and orchards. This study aims to optimize the spatial layout of farmland for the farmland downhill fruit trees uphill, in order to improve the quality of arable land. The study area was taken as Xunwu County, Ganzhou City, Jiangxi Province, China. The forestry and fruit industries were also dominated by the agricultural industry. 13 indicators were selected to evaluate the farmland quality under the contradiction of spatial mismatch between farming and fruiting. The topography, slope and elevation were used to classify the "on the mountain" and "under the mountain". Land quality, geographic location and landscape pattern were first utilized to evaluate the farmland quality. The topographic space was then coupled with the geographic definition of "on the mountain" and "under the mountain", in order to identify the dislocation space between farmland and orchards. Finally, a spatial decision-making model was constructed using GIS technology. The contradiction zones of farmland and orchards were identified to coordinate the spatial layout. The efficiency of land use was promoted with agricultural activities. The results showed that: 1) The overall quality of finely divided farmland was achieved at the medium level. Among them, the area of farmland with grade Ⅰ was 2 227.02 hm² after quality evaluation, accounting for 24.17% of the total farmland area; The area of farmland with the quality of grade V was 671.70 hm², accounting for 7.29%. The topographic space was divided into "under the mountain" of 24.06% and "on the mountain" of 48.19%. 2) The "on the mountain" and "under the mountain" were taken as the zoning criteria. The farmland space was initially delineated into the protected areas and the areas to be transferred. The forest and fruit space was divided into the suitable for the cultivation and fruits, as well as the reserved areas. The transfer and arable areas belonged to the dislocation space of agricultural production. Specifically, the dislocation spaces of farmland and orchards were 1 221.77 and 5 169.39 hm², respectively. 3) The dislocation space of farmland and orchards was optimized under the spatial decision-making model, according to the contradiction coordination. The 1 247.25 hm² orchards "under the mountain" was adjusted to the farmland, whereas, the 1 221.77 hm² farmland "on the mountain" was to the orchards. The optimal layout of farmland and orchards had improved the quality of farmland, as well as the degree of concentration and contiguity. The finding can also provide a strong reference and theoretical basis to optimize the layout of farmland from "on the mountain" to "under the mountain". [ABSTRACT FROM AUTHOR]

Decentralized offshore wind power to hydrogen production is one of the potential solutions for wind energy utilization in deep sea area, of which the electrolysis is integrated with the wind turbine, so that how to gather and transport the produced hydrogen from the decentralized wind turbines to the processing terminals becomes very crucial. This paper focuses on this topic and proposes a mixed-integer linear programming model to optimize the layout of the "ship+pipe" transmission system, aiming at minimizing the average annual total cost. Through the model, the locations and sizes of hydrogen gathering platforms, their allocations with wind turbines, the shipping routes could all be figured out. Two aspects of contributions are made in this work. First, the concept of "Pipe + Ship" transmission mode is proposed, providing a feasible solution for offshore hydrogen transportation with long distance; Second, the proposed model integrates subsea pipe network and the sea level shipping route as a whole system, revealing their interactions. Case studies present the model's validity and feasibility as well as providing inspirations about the investment management. [Display omitted] • Hydrogen transmission for decentralized offshore wind power-hydrogen is studied. • Layout optimization model for the "Pipe + Ship" transmission mode is proposed. • Interaction between pipe network and shipping routes are revealed. • Results of case studies indicate the characteristic of each cost components. • The study provides inspirations about the investment management. [ABSTRACT FROM AUTHOR]

Although various studies on vibration control schemes exist, most of them focus on a one-time optimization of actuator layout. However, numerical results indicate significant drawbacks of fixed layouts on control performance, a factor rarely mentioned in the field of vibration control. This study aims to address this gap by proposing a novel rearrangement strategy that adaptively varies the actuator layout based on the vibration intensity pattern. The inspiration for this approach comes from the reorientation of cortical microtubules in plant cells, which adapt to stress patterns. The study first derives the definitions of vibration patterns and the index of intensity based on energy transfer. These definitions then guide the optimization of actuator layout throughout the control process. The study compares the fixed layout with the proposed adaptive varying layout, considering both full-state feedback and recognition feedback. The numerical results demonstrate that the proposed adaptive rearrangement of actuator layout significantly improves suppression performance, leading to higher utilization efficiency of control forces. Additionally, the presented strategy is tolerant to computation errors from recognition feedback, resulting in robust enhancements in vibration attenuation. Our study has introduced new possibilities for improving existing control schemes. • First-time practical dynamic rearrangement strategy adapts actuator layout based on system responses. • A bioinspired optimization framework aligns the actuator layout with a vibration intensity pattern in a fast-computing manner. • The strategy is compatible with both full-state feedback and recognition reconstructed from sensing signals. • Simulations show improved control performance with increased force utilization and error tolerance. [ABSTRACT FROM AUTHOR]

When directly solving the layout optimization design problem of grid-stiffened structures with large-scale discrete design variables, gradient-based methods and intelligent optimization algorithms are rendered ineffective owing to multilocal optimal traps and the discrete variable space with extremely large dimensionality. A new layout optimization design scheme for grid-stiffened structures is proposed in this study, based on the continuous representation of discrete variables and a dimensionality reduction method. In this method, first, a discrete stiffener layout is described by a bounded field with space correlation and transformed into continuous design variables using material interpolation. Then, to significantly reduce the dimensionality of the design space, a series expansion approach is adopted. Finally, a sequential optimization strategy combined with the kriging surrogate model is used to successfully solve optimization problems. Layout optimization designs, including a stiffened plate and a space-borne antenna reflector, are proposed to demonstrate the validity and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

The development and popularization of new energy vehicles have become a global consensus. The shortage and unreasonable layout of electric vehicle charging infrastructure (EVCI) have severely restricted the development of electric vehicles. In the literature, many methods can be used to optimize the layout of charging stations (CSs) for producing good layout designs. However, more realistic evaluation and validation should be used to assess and validate these layout options. This study suggested an agent-based simulation (ABS) model to evaluate the layout designs of EVCI and simulate the driving and charging behaviors of electric taxis (ETs). In the case study of Shenzhen, China, geographical positioning system (GPS) trajectory data were used to extract the temporal and spatial patterns of ETs, which were then used to calibrate and validate the actions of ETs in the simulation. The ABS model was developed in a geographic information system (GIS) context of an urban road network with traveling speeds of 24 h to account for the effects of traffic conditions. After the high-resolution simulation, evaluation results of the performance of EVCI and the behaviors of ETs can be provided in detail and in summary. Sensitivity analysis demonstrates the accuracy of simulation implementation and aids in understanding the effect of model parameters on system performance. Maximizing the time satisfaction of ET users and reducing the workload variance of EVCI were the two goals of a multiobjective layout optimization technique based on the Pareto frontier. The location plans for the new CS based on Pareto analysis can significantly enhance both metrics through simulation evaluation. [ABSTRACT FROM AUTHOR]

As global energy crises and climate change intensify, offshore wind energy, as a renewable energy source, is given more attention globally. The wind power generation system is fundamental in harnessing offshore wind energy, where the control and design significantly influence the power production performance and the production cost. As the scale of the wind power generation system expands, traditional methods are time-consuming and struggle to keep pace with the rapid development in wind power generation systems. In recent years, artificial intelligence technology has significantly increased in the research field of control and design of offshore wind power systems. In this paper, 135 highly relevant publications from mainstream databases are reviewed and systematically analyzed. On this basis, control problems for offshore wind power systems focus on wind turbine control and wind farm wake control, and design problems focus on wind turbine selection, layout optimization, and collection system design. For each field, the application of artificial intelligence technologies such as fuzzy logic, heuristic algorithms, deep learning, and reinforcement learning is comprehensively analyzed from the perspective of performing optimization. Finally, this report summarizes the status of current development in artificial intelligence technology concerning the control and design research of offshore wind power systems, and proposes potential future research trends and opportunities. [ABSTRACT FROM AUTHOR]

The pursuit of more efficient transport has led engineers to develop a wide variety of aircraft configurations with the aim of reducing fuel consumption and emissions. However, these innovative designs introduce significant aeroelastic couplings that can potentially lead to structural failure. Consequently, aeroelastic analysis and optimization have become an integral part of modern aircraft design. In addition, aeroelastic testing of scaled models is a critical phase in aircraft development, requiring the accurate prediction of aeroelastic behavior during scaled model construction to reduce costs and mitigate the risks associated with full-scale flight testing. Achieving a high degree of similarity between the stiffness, mass distribution and flow field characteristics of scaled models and their full-scale counterparts is of paramount importance. However, achieving similarity is not always straightforward due to the variety of configurations of modern lightweight aircraft, as identical geometry cannot always be directly scaled down. This configuration diversity has a direct impact on the aeroelastic response, necessitating the use of computational aeroelasticity tools and optimization algorithms. This paper presents the development of an aeroelastic scaling framework using multidisciplinary optimization. Specifically, a parametric Finite Element Model (FEM) of the wing is created, incorporating the parameterization of both thickness and geometry, primarily using shell elements. Aerodynamic loads are calculated using the Doublet Lattice Method (DLM) employing twist and camber correction factors, and aeroelastic coupling is established using infinite plate splines. The aeroelastic model is then integrated within an Ant Colony Optimization (ACO) algorithm to achieve static and dynamic similarity between the scaled model and the reference wing. A notable contribution of this work is the incorporation of internal geometry parameterization into the framework, increasing its versatility and effectiveness. [ABSTRACT FROM AUTHOR]

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In wireless power transfer systems, geometries of a transmitter coil and a receiver coil considerably influence power transmission efficiency (PTE) and the magnetic field leakage. However, a method to simultaneously optimize the geometries of both the transmitter and receiver coils is yet to be devised, and custom design for each electronic device remains unfeasible. In this study, using mesh current distribution analysis, we optimized the current distribution in the design area of the transmitter and receiver coils to simultaneously optimize the geometries of the transmitter coil and the receiver coil. Two types of design methods were devised by using two types of optimization: one to maximize PTE and the other to maximize PTE under cancellation conditions of the magnetic field leakage. The first method designs the coil geometries for maximizing PTE and the second method designs for canceling the magnetic field leakage. Electromagnetic simulations and experimental analysis using a metal three-dimensional printer revealed that the design objectives were accomplished because it was confirmed that the coils designed to maximize PTE achieved higher PTE and the coils designed to cancel the magnetic field leakage achieved lower magnetic field leakage intensity. The results of this study proved that the automatic coil design can become a feasible method in various applications.

Precast concrete components have attracted a lot of attention due to their efficient production on off-site production lines. However, in the precast component production process, unreasonable production sequence and mold layout will reduce production efficiency and affect the workload balance between each process. Due to the multi-species and small-lot production characteristics of precast concrete components, the number of molds corresponding to each precast concrete component is generally limited. In this paper, a production sequence and layout optimization model for assembling precast concrete components under a limited number of molds is proposed, aiming to improve the comprehensive utilization efficiency of the mold tables and balance the workload between each production process of precast components. In order to obtain a better production sequence and a richer combination of mold layout schemes, a multi-objective teaching-learning-based optimization algorithm based on the Pareto dominance relation is developed, and an enhancement mechanism is embedded in the proposed algorithm. To verify the superior performance of the enhanced teaching-learning-based optimization algorithm in improving the comprehensive utilization efficiency of the mold tables and balancing the workload between various processes, three different sizes of precast concrete component production cases are designed. The research results show that the proposed model and optimization algorithm can help production managers to efficiently formulate more reasonable precast component production sequence and layout schemes, especially for those enterprises that are struggling to improve the efficiency of precast concrete component production.

Beam–slab structures account for 50–65% of a building’s total dead load and contribute to 20% of the overall cost and CO2 emissions. Despite their importance, conventional beam–slab structural optimization methods often lack search efficiency and accuracy, making them less effective for practical engineering applications. Such limitations arise from the optimization problem involving a complex solution space, particularly when considering components’ arrangement, dimensions, and load transfer paths simultaneously. To address the research gap, this study proposes a novel two-stage genetic algorithm, optimizing beam–slab layout in the first stage and component topological relationships and dimensions in the second stage. Numerical experiments on the prototype case indicate that the algorithm can generate results that meet engineering accuracy requirements within 100 iterations, outperforming comparable algorithms in both efficiency and accuracy. Additionally, this heuristic approach stands out for its independence from prior dataset training and its minimal parameter adjustment requirement, making it highly accessible to engineers without programming expertise. Statistical analysis of the algorithm’s optimization process and case studies demonstrate its robustness and adaptability to various beam–slab structural optimization problems, revealing its significant potential for practical engineering scenarios.

The aim is to reduce the elastic deformation of the web and side walls of low-stiffness thin-walled beams when the floating fixture method is used. This paper takes the number and position of fixture points as the optimization variables, establishes a calculation model of elastic deformation, and constructs the objective function of maximum total elastic deformation. An optimized solution utilizing the augmented multiplier method is employed, which forms the basis for the fixture layout optimization method to reduce the elastic deformation of low-stiffness thin-walled beams. A theoretical calculation, simulation analysis, and the fixture layout optimization of total maximum elastic deformation were completed using an aluminum alloy low-stiffness thin-walled beam as an example. The results show that based on the optimized layout, the average relative error between the calculated value and the simulated value of total maximum elastic deformation is 17.43%, and the simulated value of maximum elastic deformation is reduced by 48.49% after optimizing the fixture layout. The measured value is reduced by 0.02 mm on average, and deformation is reduced by 74.07%, which verifies the effectiveness of the floating fixture layout optimization control of machining elastic deformation proposed in this paper.