Optimization Method Research Articles

ROBUST DESIGN OPTIMIZATION OF A COMPRESSOR ROTOR USING RECURSIVE COKRIGING BASED MULTI-FIDELITY UNCERTAINTY QUANTIFICATION AND MULTI-FIDELITY OPTIMIZATION

Abstract This work focuses on the application of multi-fidelity methods for the robust design optimization of engine components. The robust design optimization approach yields geometric designs with high efficiency and low sensitivity to uncertainties from manufacturing and wear. The uncertainty quantification techniques required to evaluate the robustness are computationally expensive, which limits their use in robust optimization. Multi-fidelity methods offer a promising solution to reduce the computational cost while maintaining the accuracy in both uncertainty quantification and optimization. A Kriging and a multi-fidelity recursive Cokriging framework are developed and applied to a test function. Furthermore, a multi-fidelity super efficient global optimization algorithm is developed. The optimizer is surrogate based and handles constraints. The developed methods are then applied to a compressor test case of a high pressure compressor blade row with 9 uncertainty and 24 design parameters of the geometry. The 2.5 % quantile of the stage efficiency is used as a robustness measure and is optimized. Design bounds and performance constraints are applied. Various uncertainty quantification techniques are analyzed. A multi-fidelity uncertainty quantification approach is developed that combines simplified coarse-grid low-fidelity with high-fidelity results to reduce the computational cost while maintaining a high accuracy. Uncertainty quantification techniques of three fidelity levels are developed and used for the multi-fidelity approach in the design space. The robust design optimization is performed and the optimal designs obtained from the multi-fidelity approach show superior performance compared to existing robust design optima in the literature.

METAHEURISTIC-ASSISTED HYBRID RECOGNITION MODEL FOR BRAIN ACTIVITY DETECTION

Electroencephalogram (EEG) recordings typically capture the integrals of active brain potentials, which vary in latencies and populations. Anomalies in EEG data, often associated with epilepsy, play a crucial role in identifying conditions such as brain death, encephalopathy, coma, depth of anesthesia, and sleep disturbances. To get early warnings for these diseases, this work intends to propose a novel approach for brain activity detection from EEG signals. A new Hybrid Classification of Combined Coot Blue Monkey Optimization (HC-CCBO) method is proposed in this work. Initially, improved [Formula: see text]-score normalization was used to preprocess the EEG signal. Further, Discrete Wavelet Transform (DWT), improved correlation and statistical features were extracted. After that, we set up hybrid classification, which exploited Bi-Directional Gated Recurrent Unit (Bi-GRU) and Deep Max Out (DMO) models. Further, weights of BI-GRU and DMO were optimized via Combined Coot Blue Monkey Optimization (CCBO) optimization. Finally, we obtain the output scores of the rest, left fist, both fists, right fist, and both feet from the suggested hybrid brain activity recognition model. The effectiveness of the suggested HC-CCBO model is compared to conventional techniques using a variety of metrics. Compared to the existing models, the suggested model obtains a high maximum accuracy of 0.93 at the 90th learning percentage.

A FMEA Optimization Method Based on TODIM and Best Worst Method-Water Filling Theory in Pythagorean Fuzzy Language Environment for Reliability Assessment of Industrial Robot

Abstract In view of the shortcomings of traditional failure modes and effects analysis (FMEA) in risk evaluation language, weight information, risk priority number (RPN), this paper proposes an FMEA optimization method. First, using the Pythagorean fuzzy language as the evaluation language, the hesitation psychology of the evaluator is truly reflected. Then, the best worst method (BWM) is used to calculate the weight of the evaluator, it can reduce the number of pairwise comparison evaluations. Second, water filling theory (WFT) uses mean values instead of extreme values to determine the discreteness of evaluation information, which is more consistent with FMEA. Therefore, WFT is used to calculate the weight of influencing factors. Finally, the tomada de-decisao iterativa multicriterio (TODIM) method is used for compromise calculation to obtain the risk ranking of failure modes. Compared with RPN, TODIM can avoid the situation that the failure mode scores are the same. At the end of the paper, the robustness and superiority of the new method are verified by taking the reliability assessment of reversing system of industrial robots as an example.

GSEditPro: 3D Gaussian Splatting Editing with Attention‐based Progressive Localization

AbstractWith the emergence of large‐scale Text‐to‐Image(T2I) models and implicit 3D representations like Neural Radiance Fields (NeRF), many text‐driven generative editing methods based on NeRF have appeared. However, the implicit encoding of geometric and textural information poses challenges in accurately locating and controlling objects during editing. Recently, significant advancements have been made in the editing methods of 3D Gaussian Splatting, a real‐time rendering technology that relies on explicit representation. However, these methods still suffer from issues including inaccurate localization and limited manipulation over editing. To tackle these challenges, we propose GSEditPro, a novel 3D scene editing framework which allows users to perform various creative and precise editing using text prompts only. Leveraging the explicit nature of the 3D Gaussian distribution, we introduce an attention‐based progressive localization module to add semantic labels to each Gaussian during rendering. This enables precise localization on editing areas by classifying Gaussians based on their relevance to the editing prompts derived from cross‐attention layers of the T2I model. Furthermore, we present an innovative editing optimization method based on 3D Gaussian Splatting, obtaining stable and refined editing results through the guidance of Score Distillation Sampling and pseudo ground truth. We prove the efficacy of our method through extensive experiments.

Optimization of a novel lateral energy dissipation system for cable-stayed bridge with short piers based on seismic vulnerability analysis

Cable-stayed bridges with short piers are susceptible to becoming the structural weak link in seismic resistance during strong earthquakes. Traditional lateral restraint systems, such as fixed and sliding bearing systems, may impose substantial lateral forces and displacement demands, affecting the entire bridge's seismic safety. A butterfly-shaped steel plate damper has been developed and integrated into a lateral energy dissipation system along with elastoplastic cables to address this. Given the significant computational challenges and the complexity of identifying the optimal parameters for the energy dissipation system, this paper combines the probabilistic seismic demand model (PSDM) with the response surface method (RSM) to propose the seismic vulnerability fitting optimization method (SVFOM). Based on SVFOM, utilizing a central composite design (CCD), 17 analysis scenarios that account for the parameter variations of the lateral energy dissipation system are established. By developing response surface functions for bearing damage (ys) and pier damage (yp) through the seismic response fitting of the structure across all scenarios, we determined the optimal parameters for the lateral energy dissipation system to minimize damage to both bearings and piers. The finite element model validation analysis, which demonstrated significant reductions in pier displacement and the probability of bearing damage, confirms the effectiveness of the SVFOM. Optimized parameters showed that the new lateral energy dissipation system can significantly enhance the lateral seismic performance of cable-stayed bridges with short piers. Compared to scenarios without energy dissipation and with lateral sliding bearings, they demonstrated that this optimized system reduces the relative displacement of the piers and girders by 56 % and the probability of complete bearing failure by 87 % while maintaining pier damage within acceptable limits. This optimized system offers a valuable reference for the seismic design of similar bridge structures.

AI-Enhanced Blockchain for Scalable IoT-Based Supply Chain

Purpose: The integration of AI with blockchain technology is investigated in this study to address challenges in IoT-based supply chains, specifically focusing on latency, scalability, and data consistency. Background: Despite the potential of blockchain technology, its application in supply chains is hindered by significant limitations such as latency and scalability, which negatively impact data consistency and system reliability. Traditional solutions such as sharding, pruning, and off-chain storage introduce technical complexities and reduce transparency. Methods: This research proposes an AI-enabled blockchain solution, ABISChain, designed to enhance the performance of supply chains. The system utilizes beliefs, desires, and intentions (BDI) agents to manage and prune blockchain data, thus optimizing the blockchain’s performance. A particle swarm optimization method is employed to determine the most efficient dataset for pruning across the network. Results: The AI-driven ABISChain platform demonstrates improved scalability, data consistency, and security, making it a viable solution for supply chain management. Conclusions: The findings provide valuable insights for supply chain managers and technology developers, offering a robust solution that combines AI and blockchain to overcome existing challenges in IoT-based supply chains.

High performance deep violet InGaN TQW laser diode through multi-layer thickness optimization using particle swarm optimization method

The paper details the optimization of efficiency parameters for a InGaN TQW laser diode using the particle swarm optimization algorithm through SILVACO software. By adjusting layer thickness based on output power, slope efficiency, and threshold current, significant enhancements were achieved. Three optimized laser diodes (LD1, LD2, LD3) were compared with a primary LD, revealing notable improvements in output power and overall performance. Analysis of carrier recombination rates in the active region highlighted increased stimulated recombination and decreased non-radiative recombination, particularly in the n-side well. The study also showed a more uniform radiative recombination in the quantum wells of the optimized LDs, especially in LD3. Investigation of electron and hole concentrations, current densities, and energy levels demonstrated higher electron and hole current density in the optimized LDs, with LD3 exhibiting substantial improvements over the primary LD. Notably, the optimized LDs displayed reduced threshold current and improved slope efficiency compared to the primary LD. The study identified optimal layer thicknesses based on various cost functions, resulting in significant enhancements in output power (0.561 W), slope efficiency (2.515 W/A), and reduced threshold current (less than 0.029 A), indicative of enhanced TQW laser diode performance. Overall, the research showcases the effectiveness of the particle swarm optimization approach in optimizing GaN-based TQW laser diodes, leading to improved efficiency characteristics and demonstrating the potential for enhanced performance in practical applications.

Fuzzy Allocation Optimization Algorithm for High-Density Storage Locations with Low Energy Consumptions

The global demand for stored and processed data has surged due to the development of IoTs and similar computational structures, which has led to further energy consumption by concentrated data storage facilities and thus the demands of global energy and environmental needs. The current paper introduces Fuzzy Allocation Optimization Algorithm to mitigate energy consumption in high storage density settings. It uses the principles of Fuzzy logic to determine the best way to assign the tasks in relation to storage density necessity, urgency and energy consumption. Thus, the proposed approach incorporates fuzzy inference systems with multi-objective optimization methods where location of storage is dynamically assessed and assigned according to energy efficiency parameters. The findings of the simulation and case study prove that the algorithm is successful in saving energy while at the same time lowering storage I/O response time, which provides a viable solution to energy issues in evolving data centres. This work satisfies the lack of energy efficient algorithms in high density storage areas and responds to the recent calls for green technology and smart utilization of resources in the energy field. The findings are used in the promotion of significant IT infrastructures towards developing the next generation of energy efficient data centers with respect to Future Internet and evolving energy web environments.

Two‐stage stochastic energy and reserve market clearing model considering real‐time offering strategy of renewable energy

AbstractWith the introduction of decarbonization policies worldwide, renewable energy, especially wind and solar power, is gradually taking a significant share in the power system. The two‐stage stochastic optimization methods are generally adopted to address the uncertainty issues and the two‐stage stochastic market clearing models are established with the participation of renewable energy. However, most existing two‐stage stochastic market clearing models usually intuitively presume the offering quantities of renewable energy are equal to their forecast values, neglecting the potential impacts of the strategic offerings on the market results and thus affecting the secure operation of power systems. To fill the gaps, this paper incorporates the strategic offering models of renewable energy into the two‐stage stochastic energy and reserve market clearing model. Within the two‐stage framework, the first day‐ahead stage serves for energy and reserve market clearing, while the second real‐time stage considers the adjustments on offering quantities of renewable energy according to the fluctuations of renewable generations. The two‐stage stochastic market clearing model is formulated as a mixed‐integer non‐linear programming problem, and an iterative algorithm is devised to obtain market clearing results after proper linearizations. The effectiveness of the proposed method is validated using the extended IEEE‐118 test system.

A hybrid GRA-PCA-TOPSIS Technique Optimization of EDD Process Parameter on Drilling of AL8011/B4C/aloevera composites

A class of cutting-edge materials called hybrid aluminum metal matrix composites has been produced for applications such as the automobile, aerospace, and electronics industry and where weight and strengths are crucial factors. In the present research work, AA8011 composite reinforced with boron carbide particles and green aloevera has been fabricated using an economical stir casting process. In this 1% to 3% of boron carbide and 1% to 2% aloevera were used to fabricate the three different combinations of composites. The electrical discharge drilling process variables such as discharge current, pulse-on time, pulse-off time, and dielectric fluid pressure are investigated using the tubular copper electrode on drilling of hybrid composites. Performance aspects including drilling rate, roundness error, taper angle, and electrode wear rate have been analyzed, and the effect of process variables with the response surface methodology–central composite experimental design. The process parameters during the drilling of composites were optimized using a novel multi-criteria decision-making technique called the hybrid grey relational analysis–pulse component analysis–technique for order of preference by similarity to ideal solution method in order to maximize the drilling rate while concurrently minimizing other aspects. The optimum conditions were reached using the hybrid optimization method and a 1.5 mm tubular copper electrode with an input current of 9 Ampere , pulse on time 25 [Formula: see text]s, pulse off time 12 [Formula: see text]s, and pressure 60 kg/cm2. In comparison to the initial settings, the optimal condition shows a 25% faster drilling rate, with a 23% better roundness value, 14% lesser taper angle, and 7% lesser electrode wear rate.

Multi-Objective Optimal Power Flow Analysis Incorporating Renewable Energy Sources and FACTS Devices Using Non-Dominated Sorting Kepler Optimization Algorithm

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability.

Optimization of three-dimensional esophageal tumor ablation by simultaneous functioning of multiple electrodes.

Radiofrequency ablation is a widely accepted minimal-invasive and effective local treatment for tumors. However, its current application in esophageal cancer treatment is limited to targeting thin and superficial lesions, such as Barrett's Esophagus. This study proposes an optimization method using multiple electrodes simultaneously to regulate the temperature field and achieve conformal ablation of tumors. A particle swarm optimization algorithm, coupled with a three-dimensional thermal ablation model, was developed to optimize the status of the functioning electrodes, the optimal voltage (Vopt), and treatment duration (ttre) for targeted esophageal tumors. This approach takes into account both the electrical and thermal interactions of the electrodes. The results indicate that for esophageal cancers at various stages, with thickness (c) ranging from 4.5mm to 10.0mm, major axis (a) ranging from 7.3mm to 27.3mm, and minor axis (b) equaling 7.3mm or 27.3mm, as well as non-symmetrical geometries, complete tumor coverage (over 99.5%) close to conformal can be achieved. This method illustrates possible precise conformal ablation of esophageal cancers and it may also be used for conformal treatments of other intraluminal lesions.

COST OPTIMIZATION METHOD FOR INFORMATIONAL INFRASTRUCTURE DEPLOYMENT IN STATIC MULTI-CLOUD ENVIRONMENT

Context. In recent years, the topic of deploying informational infrastructure in a multi-cloud environment has gained popularity. This is because a multi-cloud environment provides the ability to leverage the unique services of cloud providers without the need to deploy all infrastructure components inside them. Therefore, all available services across different cloud providers could be used to build up information infrastructure. Also, multi-cloud offers versatility in selecting different pricing policies for services across different cloud providers. However, as the number of available cloud service providers increases, the complexity of building a costoptimized deployment plan for informational infrastructure also increases. Objective. The purpose of this paper is to optimize the operating costs of information infrastructure while leveraging the service prices of multiple cloud service providers. Method. This article presents a novel cost optimization method for informational infrastructure deployment in a static multicloud environment whose goal is to minimize the hourly cost of infrastructure utilization. A genetic algorithm was used to solve this problem. Different penalty functions for the genetic algorithm were considered. Also, a novel parameter optimization method is proposed for selecting the parameters of the penalty function. Results. A series of experiments were conducted to compare the results of different penalty functions. The results demonstrated that the penalty function with the proposed parameter selection method, in comparison to other penalty functions, on average found the best solution that was 8.933% better and took 18.6% less time to find such a solution. These results showed that the proposed parameter selection method allows for efficient exploration of both feasible and infeasible regions. Conclusion. A novel cost optimization method for informational infrastructure deployment in a static multi-cloud environment is proposed. However, despite the effectiveness of the proposed method, it can be further improved. In particular, it is necessary to consider the possibility of involving scalable instances for informational infrastructure deployment.

A Comprehensive Review on the Incremental Sheet Forming of Polycarbonate

Incremental sheet forming has emerged as an excellent alternative to other material forming procedures, incrementally deforming flat metal sheets into complex three-dimensional profiles. The main characteristics of this process are its versatility and cost-effectiveness; additionally, it allows for greater formability compared to conventional sheet forming processes. Recently, its application has been extended to polymers and composites. The following review aims to present the current state of the art in the incremental sheet forming of polycarbonate, an outstanding engineering plastic, beginning with initial studies on the feasibility of this process for polymers. Attention is given to the advantages, drawbacks, and main applications of incrementally formed polycarbonate sheets, as well as the influence of process parameters and toolpath strategies on features such as formability, forming forces, deformation and failure mechanisms, geometric accuracy, surface quality, etc. Additionally, new hybrid forming methods for process optimisation are presented. Finally, a discussion is provided on the technical challenges and future research directions for incremental sheet forming of polycarbonate and, more generally, thermoplastics. Thus, this review aims to offer an extensive overview of the incremental forming of polycarbonate sheets, useful to both academic and industrial researchers working on this topic.

OPTIMIZATION OF THE PARAMETERS OF SYNTHESIZED SIGNALS USING LINEAR APPROXIMATIONS BY THE NELDER-MEAD METHOD

Context. The article presents the results of a study of the effectiveness of using the Nelder-Mead method to optimize the parameters of linear approximations of synthesized signals. Algorithms have been developed and tested that integrate spectral, temporal, and statistical analyzes and provide reasonable optimization. The effectiveness of the application of the Nelder-Mead method was proven by experiment. The obtained results substantiate the improvement of the properties of the mutual correlation of signals and the reduction of the maximum deviations of the side lobes, which opens up prospects for the further application of the method in complex scenarios of signal synthesis. Objective. The purpose of the work is to evaluate the effectiveness of the application of the Nelder-Mead method when adjusting the parameters of linear approximations to optimize the mutual correlation and minimize side deviations of complex synthesized signals. Method. The main research method is the comparison of various optimization algorithms for the selection of the most effective approaches in linear approximations of synthesized signals, taking into account such criteria as accuracy, speed and minimization of deviations. Scientific works [1, 2, 4–6, 8, 9] present algorithms, including the Nelder-Mead method and differential evolution. The effectiveness of these methods is achieved due to adaptive optimization procedures that improve the characteristics of signals. It is worth noting that the methods have disadvantages associated with high requirements for computing resources, especially when processing large data. This can be minimized using combined optimization methods that take into account the interaction of signal parameters. Another important direction of improvement is the optimization of methods for adaptation to dynamic changes in the characteristics of complex signals, which allows to achieve high adaptability and reliability of real-time systems. Results. As a result of the experiment using the Nelder-Mead method, an increase in the similarity of spectral densities was achieved from 0.52 in the first iteration to 0.90 in the fourth, with a significant decrease in the distance between the peaks of the spectrum from 1.2 to 0.4, which indicates high adaptability and the accuracy of the method in adjusting the parameters of the synthesizedsignals. Conclusions. The effectiveness of the Nelder-Mead method for adjusting the specified parameters of the synthesized signals was experimentally proven, which is confirmed by a significant improvement in the similarity of the spectra with each iteration. This opens the way for additional optimizations and application of the method in various technological areas.

Façade Design Pattern Optimization Workflow Through Visual Spatial Frequency Analysis and Structural Safety Assessment

As the demand for highly efficient yet aesthetically pleasing, complex building envelope structures is rising worldwide, computational analysis and generative design tools are becoming ever so relevant. Previous methods for achieving a natural distribution of structural or shading elements in non-uniform façades are mostly based either on computer-generated pseudo-randomness or a literal biomorphic approach where a naturally occurring pattern is directly projected on the façade surface. As an alternative, this research introduces a novel technique for optimisation that utilises a two-dimensional Power Spectrum Analysis, suitable for numerically assessing the alignment of designed geometry with natural patterns. By integrating this optimisation method into the design process, the façade pattern generation can be automated and optimal design can be selected by evaluating multiple design solutions. Instead of using repetitive geometrical patterns or generated pseudo-randomness, patterns objectively similar to those occurring in nature can be created without directly copying natural structures. The distribution of the structural and shading elements controls the way natural light permeates the building and, considering the data gathered from images of natural scenes, this method can be used to design structures not only with optimal structural and energy performance but also with visual and psychological occupant comfort in mind.

Parameter Study and Optimization of Static Performance for a Hybrid Cable-Stayed Suspension Bridge

The hybrid cable-stayed and suspension (HCSS) bridge is known for its stability and cost-effectiveness, with significant application potential. This study examined the static performance of an HCSS bridge with a 1440 m main span. A finite element model (FEM) was developed to assess key parameters, such as the span-to-rise ratio, cable-to-hanger ratio, pylon stiffness, steel–concrete interface, and cable stiffness. Through FEM analysis and parameter optimization using the zero-order and first-order optimization methods in an ANSYS module, key design variables were optimized. The results show that an inappropriate span-to-rise ratio negatively impacts mid-span girder forces, while increasing the cable-stayed area enhances the overall stiffness. Main cable stiffness plays a crucial role in load-bearing and deformation control. Significant force differences were observed between stay and hanger cables, with axial force in the main girder increasing from the side span to the pylon under dead load. Bending moments in the transition region varied widely under combined loads. Optimizing parameters, such as the span-to-rise and cable-to-hanger ratios, significantly improved the mechanical performance of HCSS bridges, offering valuable insights for future designs.

A Systematic Mapping Study and a Review of the Optimization Methods of Structures in Architectural Design

Structural optimization has gained popularity in modern structural design, helping to reduce material consumption while maintaining the structural performance of buildings. This process also significantly influences the architectural appearance, affecting various aspects such as cross-section sizing, structural forms, and the layout of structural members. Beyond minimizing materials or costs, structural optimization can serve as a powerful tool for making architecture more visually appealing. However, with the wide variety of structural optimization methods proposed, gaining a comprehensive overview has become challenging. To address this, a systematic mapping study has been conducted, focusing on methods introduced over the past decade. The relevant journal articles are categorized based on several factors, including types of optimization, materials used, structural typologies, areas of application, and optimization objectives. The results of this study provide both a broad overview of recent developments in structural optimization and valuable insights into research-rich and under-explored areas. Moreover, the paper discusses which types of structural optimization are more relevant when applied as part of the architectural design process. It is suggested that future research should focus on identifying gaps and challenges in effectively applying structural optimization to architectural design, thus enhancing both efficiency and aesthetic potential.

A comparative analysis of metaheuristic algorithms for optimizing curved roof structures

This article explores the optimal design of curved steel structures with a focus on minimizing their weight by determining the most suitable cross-sections. Customized optimization algorithms were developed to identify structural designs that meet safety and durability requirements, adhering to design constraints set by the ASD-AISC specification. To ensure reliable results, the study employs a variety of metaheuristic optimization methods: Biogeography-Based Optimization (BBO), Dynamic Harmony Search (DHS), Wolf Colony Algorithm (WCA), and Honey Badger Algorithm (HBA). The Honey Badger Algorithm, a relatively new addition to the field, was used for the first time in the context of structural optimization for curved roof systems. This unique approach aims to evaluate its performance in civil engineering problems and compare it with other established algorithms. The major challenges of this study lie in the inherent complexity of dome structures, which involve a large number of elements and nonlinear constraints. The subdivision of the structure into smaller groups was necessary to manage the computational load, although this introduced additional complexities. Despite these challenges, the metaheuristic methods demonstrated their robustness in addressing such intricate engineering problems. Additionally, data retrieval is facilitated through Open Application Interface (OAPI) functions, enabling seamless data transfer between SAP 2000 and Visual Basic. The study's final design example involves a dome model with 2556 elements, which was both modeled and optimized. The results demonstrate the effectiveness of these optimization algorithms in achieving structurally sound and efficient designs. The insights gained from this study contribute to our understanding of optimized cross-sections in curved steel structures, offering valuable guidance for improving structural performance and minimizing material usage.

Stochastic Black-Box Optimization using Multi-Fidelity Score Function Estimator

Abstract Optimizing parameters of physics-based simulators is crucial in the design pro-
cess of engineering and scientific systems. This becomes particularly challenging
when the simulator is stochastic, computationally expensive, black-box and when a
high-dimensional vector of parameters needs to be optimized, as e.g. is the case
in complex climate models that involve numerous interdependent variables and
uncertain parameters. Many traditional optimization methods rely on gradient
information, which is frequently unavailable in legacy black-box codes. To address
these challenges, we present SCOUT-Nd (Stochastic Constrained Optimization for
N dimensions), a gradient-based algorithm that can be used on non-differentiable
objectives. It can be combined with natural gradients in order to further enhance
convergence properties. and it also incorporates multi-fidelity schemes and an
adaptive selection of samples in order to minimize computational effort. We vali-
date our approach using standard, benchmark problems, demonstrating its superior
performance in parameter optimization compared to existing methods. Addition-
ally, we showcase the algorithm’s efficacy in a complex real-world application, i.e.
the optimization of a wind farm layout.