337 Results Found

Injection molding process parameters have a great impact on plastic production quality, manufacturing cost, and molding efficiency. This study proposes to apply the method of Latin hypercube sampling, and to combine the response surface model and &ld...

Solving global optimization problems is a significant challenge, particularly in high-dimensional spaces. This paper proposes a selective multistart optimization framework that employs a modified Latin Hypercube Sampling (LHS) technique to maintain a...

Latin Hypercube Design (LHD) is widely used in computer simulation to solve large-scale, complex, nonlinear problems. The high-dimensional LHD (HLHD) problem is one of the crucial issues and has been a large concern in the long run. This paper propos...

A significant number of sample points are often required for surrogate-based optimization when utilizing process simulations to cover the entire system space. This necessity is particularly pronounced in complex simulations or high-dimensional physic...

Sliced Latin hypercube designs (SLHDs) are widely used in computer experiments with both quantitative and qualitative factors and in batches. Optimal SLHDs achieve better space-filling property on the whole experimental region. However, most existing...

Hyperparameters allow metaheuristics to be tuned to a wide range of problems. However, even though formalized tuning of metaheuristic parameters can affect the quality of the solution, it is rarely performed. The empirical selection method and the tr...

In the context of the “Carbon peak, Carbon neutral” target, the introduction of carbon trading and the connection of new energy generation such as wind power and photovoltaics to the power grid have become important means to achieve a red...

This paper proposes a new surrogate optimization routine for optimal design of a direct on line (DOL) squirrel cage induction motor. The geometry of the motor is optimized to maximize its electromagnetic efficiency while respecting the constraints, s...

The axle bridge plays a crucial role in the bogie of low-floor light rail vehicles, impacting operational efficiency and fuel economy. To minimize the total cost of the structure and turning of axle bridges, an optimization model of structural and tu...

Bayesian optimization (BO)-assisted screening was applied to identify improved reaction conditions toward a hundred-gram scale-up synthesis of 2,3,7,8-tetrathiaspiro[4.4]nonane (1), a key synthetic intermediate of 2,2-bis(mercaptomethyl)propane-1,3-d...

The aluminium alloy front subframe of an automobile was developed through multi-operating condition topology optimization and multi-objective optimization methods. By considering the influences of loads on the strength, static stiffness, and modal of...

Range anxiety is a problem that restricts the development of pure electric vehicles. For this reason, much research starts from a shift schedule and strives to improve mileage. However, the proposed shift schedules have poor adaptive ability and are...

To achieve time–energy–impact multi-objective optimization in the trajectory control of underwater manipulators, this paper proposes a Fast Non-Dominated Sorting Tuna Swarm Optimization algorithm (FNS-TSO). The algorithm integrates a fast...

The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local opt...

Under the background of “carbon peaking and carbon neutrality goals” in China, the Highway Self-Consistent Energy System (HSCES) with renewable energy as the main body has become a key research object. To study the operational status of t...

The advent of powered prosthetic ankles provided more balance and optimal energy expenditure to lower amputee gait. However, these types of systems require an extensive setup where the parameters of the ankle, such as the amount of positive power and...

The increasing demand for sustainable development and energy efficiency underscores the importance of optimizing motors in driving the upgrade of energy structures. This paper studies a data-driven approach for the multi-objective optimization of mot...

Identifying residual stresses and the distortions in composite structures during the curing process plays a vital role in coming up with necessary compensations in the dimensions of mold or prototypes and having precise and optimized parts for the ma...

This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved Hippopotamus Optimization Algorithm (IHO). The IHO enhances the traditional Hippopotamus Optim...

Metal–oxide–semiconductor field-effect transistors (MOSFETs) are critical in power electronic modules due to their high-power density and rapid switching capabilities. Therefore, effective thermal management is crucial for ensuring reliab...

The increasing demands for travelling comfort and reduction of carbon dioxide emissions have been considered substantially in the stage of conceptual aircraft design. However, the design of a modern aircraft is a multidisciplinary process, which requ...

In order to improve the level of new energy consumption and reduce the dependence of the power system on traditional fossil energy, this paper proposed a bi-level optimization model for virtual power plant member selection by means of coordination an...

To achieve durable high-performance fiber-reinforced concrete that meets economic requirements, this paper introduces a hybrid intelligent framework based on the Latin hypercube experimental design, response surface methodology (RSM), and the NSGA-II...

This paper pioneers a novel approach in electromagnetic (EM) system analysis by synergistically combining Bayesian Neural Networks (BNNs) informed by Latin Hypercube Sampling (LHS) with advanced thermal–mechanical surrogate modeling within COMS...

The high dimensionality and uncertainty of renewable energy generation restrict the ability of the microgrid to consume renewable energy. Therefore, it is necessary to fully consider the renewable energy generation of each day and time period in a lo...

This study investigates an aerodynamic optimization framework inspired by marine biological morphology, utilizing the sailfish profile as a basis for airfoil configuration. Through Latin hypercube experimental design combined with optimization algori...

The Sparrow Search Algorithm (SSA), proposed by Jiankai Xue in 2020, is a swarm intelligence optimization algorithm that has received extensive attention due to its powerful optimization-seeking ability and rapid convergence. However, similar to othe...

This research paper presents a new Latin hypercube sampling method, aimed at enhancing its performance in quantifying uncertainty and reducing computation time. The new Latin hypercube sampling (LHS) method serves as a tool in reliability-based desig...

With increasing worries about the environment, there is a rising focus on saving energy in various industries. In the e-mobility industry of electric motors, permanent magnet synchronous motors (PMSMs) are widely utilized for saving energy due to the...

With attention increasing towards worldwide ecological conservation, the energy policies of various countries are moving towards renewable energy systems, primarily with solar and wind as the primary power sources. The Western Electricity Coordinatin...

The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve...

Quality control of fresh Lycium barbarum during storage presents significant challenges, particularly regarding the unclear relationship between quality characteristics and storage conditions. This study analyzes the changes in qualitative and struct...

Interior permanent magnet synchronous motors (IPMSMs) are critical for electric vehicle traction and industrial systems, yet optimizing their performance under high-dimensional design spaces remains computationally challenging. This study proposes a...

This paper presents an efficient parameter optimization approach to the plastic injection molding process to achieve high productivity. In collaboration with a company specializing in plastic injection-mold-based production, real process data was col...

Fused filament fabrication (FFF), an additive manufacturing process, is an emerging technology with issues in the uncertainty of mechanical properties and quality of printed parts. The consideration of all main and interaction effects when changing p...

Multimodal optimization deals with problems where multiple feasible global solutions coexist. Despite sharing a common objective function value, some global optima may be preferred to others for various reasons. In such cases, it is paramount to devi...

Due to fabrication difficulties, separately-polished segmented mirrors cannot meet the co-phasing surface shape error requirements in the segmented telescope system. Applying the global radius of curvature (GRoC) actuation system for the individual s...

The approximated non-linear least squares (ALS) tunes or calibrates the computer model by minimizing the squared error between the computer output and real observations by using an emulator such as a Gaussian process (GP) model. A potential defect of...

Aiming to combat the problems of slow speed and poor accuracy of reliability evaluation of the power system in wind farms with energy storage, this paper proposes a method of reliability evaluation based on Latin hypercube important sampling (LHIS)....

In this study, a shape design optimization method is proposed to improve the efficiency of a 3 kW permanent magnet synchronous motor (PMSM) used in an electric compressor intended for use in an electric vehicle. The proposed method improves the effic...

This paper proposes a derivative-variance hybrid global sensitivity measure with optimal sampling method selection. The proposed sensitivity measure is as computationally efficient as the derivative-based global sensitivity measure, which also serves...

Coordination of a hydropower, combined heat and power (CHP), and battery energy storage system (BESS) with multiple renewable energy sources (RES) can effectively reduce the adverse effects of large-scale renewable energy integration in power systems...

Data-driven models are essential tools for the development of surrogate models that can be used for the design, operation, and optimization of industrial processes. One approach of developing surrogate models is through the use of input–output...

Traditional approaches used for analyzing the mechanical properties of auxetic structures are commonly based on deterministic techniques, where the effects of uncertainties are neglected. However, uncertainty is widely presented in auxetic structures...

Feed formulation is a typical multi-objective optimization problem that aims to minimize cost while satisfying multiple nutritional constraints. However, existing methods often suffer from limitations in handling nonlinear constraints, high-dimension...

Reliable project management during planning stages of a building project is a meticulous process typically requiring sufficient precedencies. Typical construction duration estimation is based on previous cases of similar projects used to validate con...

To effectively reduce building energy consumption and enhance indoor thermal comfort, this study developed a sensitivity analysis and multi-objective optimization method for an office building in Hangzhou considering the effects of parameter interact...

As a multi-source vibration machine, the complex vibration of the combine harvester has caused many effects on various components. In this paper, the dynamic response of the plate and shell on the combine harvester is studied, and the influence of va...

Cooperative braking with regenerative braking and mechanical braking plays an important role in electric vehicles for energy-saving control. Based on the parallel and the series cooperative braking models, a combined model with a predictive control s...

This paper discusses various strategies for probabilistic analysis, with a focus on typical engineering applications. The emphasis is on sampling methods and sensitivity analysis. A new sampling method, Latinized particle sampling, is introduced and...