Optimization Objective Function Research Articles

Study on SR-Crossbar RF MEMS Switch Matrix Port Configuration Scheme with Optimized Consistency.

The performance consistency of an RF MEMS switch matrix is a crucial metric that directly impacts its operational lifespan. An improved crossbar-based RF MEMS switch matrix topology, SR-Crossbar, was investigated in this article. An optimized port configuration scheme was proposed for the RF MEMS switch matrix. Both the utilization probability of individual switch nodes and the path lengths in the switch matrix achieve their best consistency simultaneously under the proposed port configuration scheme. One significant advantage of this scheme lies in that it only adjusts the positions of the input and output ports, with the topology and individual switch nodes kept unchanged. This grants it a high level of generality and feasibility and also introduces an additional degree of freedom for optimizations. In this article, a universal utilization probability function of single nodes was constructed and an optimization objective function for the SR-Crossbar RF MEMS switch matrix was formulated, which provide a convenient approach to directly solving the optimized port configuration scheme for practical applications. Simulations to demonstrate the optimized dynamic and static consistencies were conducted. For an 8 × 8 SR-Crossbar switch matrix, the standard deviations of contact resistances of 128 units and losses of all 64 paths decreased from 1.00 and 0.42 to 0.51 and 0.23, respectively. These results aligned closely with theoretical calculations derived from the proposed model.

Recovery of missing samples in Orthogonal Frequency Division Multiplexing signals with optimisation using data carriers

AbstractA method is proposed for reconstructing an Orthogonal Frequency Division Multiplexing (OFDM) signal that contains data gaps, with the aim to improve demodulation. The main objective is to use the method in a passive radar application with missing data samples and to improve target detection. The OFDM signal is assumed to comply with the Digital Video Broadcasting Terrestrial standard. The proposed recovery method is based on optimisation of a novel objective function, which consists of two parts. The first part is a function of the energy in the out‐of‐band frequencies, whereas the second, and novel part, uses the location of data carriers in the constellation diagram. The method is evaluated using both simulations and real data. The authors show that the proposed method significantly improves the OFDM signal in just a few iteration steps. The proposed method improved the condition number more than a factor ten thousand millions compared to using the least square method on the out‐of‐band frequencies only. The authors also decode the symbols with the Viterbi decoding algorithm and show how the required number of iterations with the proposed algorithm depends on the amount of missing samples and on the Signal‐to‐Noise Ratio in order to achieve a Bit Error Rate of less than one in one hundred thousand millions.

Calibration method for binocular vision system with large field of view based on small target image splicing

In vision measurement, camera calibration has a significant impact on measurement precision. The classical target-based calibration methods require the target to occupy more than one-third of the field of view. A small-size target that does not meet the requirements results in poor calibration accuracy, while an appropriate large-size target is difficult to manufacture and inconvenient to operate. In view of the above problem, we propose a flexible and accurate calibration method based on small target image splicing to calibrate the binocular vision system with a large field of view. The spliced images and virtual large targets are constructed to extend the target size, providing better flexibility for calibration. Moreover, an optimization objective function integrating two constraints in the imaging plane and measurement space is presented to improve the calibration accuracy during the parameter optimization process. The simulation experiments and actual experiments are carried out to test the performance of the proposed method. The results demonstrate that the calibration accuracy of the proposed method using a small target is equivalent to that of Zhang’s method using a large target. Additionally, when using a same-size target, the parameter error of the proposed method is less than that of Zhang’s method, and the proposed method reduces the distance measurement error from 1.169 mm to 0.208 mm compared to Zhang’s method.

Topology optimization based on combined mechanical analysis and variable density method for casting structure

In this study, a topology optimization method based on combined mechanical analysis and variable density is presented for a large-scale casting structure with complex thin-walled structures. First, the mechanical analysis model of the frame is established, the first-order bending and torsional modes and stiffness values of the all-cast aluminum frame are obtained, and the basic mechanical parameters such as bending/torsional mode and stiffness constraints needed for topology optimization are defined. Using the variable density method, unneeded material that can be removed from the casting structure is identified. According to the design requirements, the optimization objective function and constraint conditions are established, and the variable density method is used to solve the problem. Analysis of the mechanical results before and after topology optimization reveals that the modes and stiffness values are larger than the target values, and the mass of the frame is reduced by 10.2 kg (i.e. 13.9%) compared with the original design. The application of the combined mechanical analysis and variable density method to a casting structure can effectively achieve lightweight design.

Enhancing energy recovery in automotive suspension systems by utilizing time-delay

This study introduces time-delay active control technology to with the aim of enhancing the system's energy harvesting potential and ride smoothness. The time-delay active suspension represents a nonlinear, multivariable system, and stability analysis in this context is intricate. The mainly challenge lies in effectively leveraging time delay to augment the system's energy collection potential while harmonizing the vehicle's ride comfort and energy efficiency. Addressing this issue, our study proposes enhancing the suspension's energy recovery capability through time-delay control. Initially, the impact of time-delay control parameters on energy collection ability under various operational conditions was analyzed, establishing that appropriate time-delay parameters can enhance energy harvesting capacity. Subsequently, to balance the relationship between vehicle comfort and energy efficiency, the research introduces a method for constructing an optimized objective function based on linear equivalent excitation, thereby quantifying the relationship between system time-domain vibrational response, time-delay control parameters, and external excitations. This approach enables the comprehensive optimization of the suspension system's comfort, safety, and energy efficiency. The control system's stability was analyzed using cluster treatment of characteristic roots method. Finally, simulations and experiments were conducted to evaluate the effectiveness of time-delay active across different scenarios, confirming the efficacy of the proposed control methodology.

Grey-box modeling for thermal dynamics of buildings under the presence of unmeasured internal heat gains

An accurate grey-box building thermal model is an essential component for prediction-based control of split air conditioners. As the thermal performance of building envelope improves, the internal heat gains play an increasingly noteworthy influence on the building’s thermal dynamics. However, the internal heat gains are usually not measured, which makes the conventional data-dependent identification methods of building thermal models fail. To improve the accuracy of building thermal models under unmeasured internal heat gains, a two-step parameter identification framework for resistance–capacitance (RC) models is proposed in this paper. First, the time-invariant building model parameters are estimated by designing a novel optimization objective function. Next, the time-varying internal heat gains are identified based on the mismatch in predictions of the estimated building model from the first step and the measured temperature. The effectiveness of the method is evaluated using data from a simulation case and an experiment case. The results show that the proposed method outperforms the conventional identification method as well as two other identification methods that handle the unmeasured internal heat gains. Besides, the internal heat gains estimated by the proposed method can capture the trend of the actual internal loads well.

Topology optimization and bionic analysis of heat sink fin configuration based on additive manufacturing technology

Fins are extensively employed in heat sinks for high heat flux components. In this study, three distinct objective functions, namely temperature gradient minimization (TO-I type fin), average temperature minimization (TO-II type fin), and entransy dissipation minimization (TO-III type fin), are established for topology optimization design of the heat sink fin structure to effectively minimize the temperature of the heat source. By conducting a comparative analysis of the configuration features and thermal performance of optimized fins with different objective functions, it is evident that the TO-I type fin exhibits superior thermal performance. When the fin volume fraction φ is 0.3, the heat source center temperature of TO-I type fin is 2.67 K and 4.55 K lower than that of TO-II type fin and TO-III type fin, respectively. Meanwhile, the bionic analysis of the optimized fins under different objective functions using fractal theory reveals that the fractal dimension of the TO-I type fin is 1.4038, which is the closest to the average fractal dimension of many kinds of leaf veins in nature (D¯f= 1.3762), which indicates that the optimized fins have the bionic characteristics most similar to those of the leaf blade. Finally, the three topology optimization fins were fabricated using boundary-filtering optimization 3D printing technology. Comparative tests verified the advantage of minimizing the temperature gradient as the objective function of topology optimization and demonstrated its potential for thermal performance and lightweighting applications, especially under high heat source operating conditions.

Integrated design of insertions-extractions performance and contact reliability of spring-wire socket electrical connector

With the improvement of the reliability and lifespan of electrical connectors, the traditional single objective design focusing solely on performance or reliability has become inadequate to meet the requirements. This paper analyzes the failure mechanism of a specific electrical connector under the conditions of insertions-extractions. It establishes a contact degradation model and an integrated design model that considers mechanical properties and reliability. By analyzing the integrated design model, the optimization variables have been defined as the radius of the spring wire, the radius of the pin, the inner radius of the inner sleeve, the length of the inner sleeve, and the inclination of the spring wire. Additionally, the objective function of optimization has been determined. The optimal solutions that satisfy the constraints were calculated using the global optimization algorithm. The optimization results not only achieve the goal of high reliability but also the goal of extended mechanical life.

Methodic Approach for the Simulation-Based, Elastic Die Surface Compensation by Creating a FE-model of the Car Body Press Predicated on Spotting Images via Neural Networks

In recent years, the push for streamlined vehicle development and the adoption of higher-strength materials to reduce the car body weight has been prominent. However, this shift results in greater elastic deformations in press and tool components due to the elevated yield stress of these materials. The die active face is subject to the compliance of the body press and die, which influences the manufacturability and dimensional accuracy of the sheet metal part. In practice, manual die spotting is required, relying on empirical data and subjective criteria for acceptance. This paper presents a new methodological approach for the development of the car body press’s FE model in structural and forming coupled simulations. To achieve this, a series of measurements quantifies the pressure between tool and sheet metal. This information is fed into a neural network along with the die spotting images as training data. The network evaluates subsequent spotting images, assigning pressure values to distinct tool sections. Based on the die pressure distribution in the process, a virtual press model is created with the help of a newly developed objective function in topology optimization. This model aims to significantly enhance simulation-based die surface compensation, effectively reducing the die spotting process.

Fast constrained optimization for cloth simulation parameters from static drapes

AbstractWe present a cloth simulation parameter estimation method that integrates the flexibility of global optimization with the speed of neural networks. While global optimization allows for varied designs in objective functions and specifying the range of optimization variables, it requires thousands of objective function evaluations. Each evaluation, which involves a cloth simulation, is computationally demanding and impractical time‐wise. On the other hand, neural network learning methods offer quick estimation results but face challenges such as the need for data collection, re‐training when input data formats change, and difficulties in setting constraints on variable ranges. Our proposed method addresses these issues by replacing the simulation process, typically necessary for objective function evaluations in global optimization, with a neural network for inference. We demonstrate that, once an estimation model is trained, optimization for various objective functions becomes straightforward. Moreover, we illustrate that it is possible to achieve optimization results that reflect the intentions of expert users through visualization of a wide optimization space and the use of range constraints.

Optimization-based planning of speech articulation using general Tau Theory

This paper presents a model of speech articulation planning and generation based on General Tau Theory and Optimal Control Theory. Because General Tau Theory assumes that articulatory targets are always reached, the model accounts for speech variation via context-dependent articulatory targets. Targets are chosen via the optimization of a composite objective function. This function models three different task requirements: maximal intelligibility, minimal articulatory effort and minimal utterance duration. The paper shows that systematic phonetic variability can be reproduced by adjusting the weights assigned to each task requirement. Weights can be adjusted globally to simulate different speech styles, and can be adjusted locally to simulate different levels of prosodic prominence. The solution of the optimization procedure contains Tau equation parameter values for each articulatory movement, namely position of the articulator at the movement offset, movement duration, and a parameter which relates to the shape of the movement’s velocity profile. The paper presents simulations which illustrate the ability of the model to predict or reproduce several well-known characteristics of speech. These phenomena include close-to-symmetric velocity profiles for articulatory movement, variation related to speech rate, centralization of unstressed vowels, lengthening of stressed vowels, lenition of unstressed lingual stop consonants, and coarticulation of stop consonants.

Multilingual Information Retrieval Using Graph Neural Networks: Practical Applications in English Translation

Multilingual information retrieval using graph neural networks offers practical applications in English translation by leveraging advanced computational models to enhance the efficiency and accuracy of cross-lingual search and translation tasks. By representing textual data as graphs and utilizing graph neural networks (GNNs), this approach captures intricate relationships between words and phrases across different languages, enabling more effective language understanding and translation. GNNs can learn complex linguistic structures and semantic similarities from multilingual corpora, facilitating the development of more robust translation systems that are capable of handling diverse language pairs and domains. The paper introduces a novel approach termed the Multilingual Ant Bee Optimization Graph Neural Network (MABO-GNN) for addressing optimization, classification, and multilingual translation tasks. MABO-GNN integrates ant bee optimization algorithms with graph neural networks to provide a versatile framework capable of optimizing objective functions, improving classification accuracy iteratively, and facilitating high-quality translations across multiple languages. Through comprehensive experimentation, the efficacy of MABO-GNN is demonstrated across various tasks, languages, and datasets. in optimization experiments, MABO-GNN achieves objective function values of 0.012, 0.015, 0.011, and 0.013 in Experiment 1, Experiment 2, Experiment 3, and Experiment 4, respectively, with convergence times ranging from 90 to 150 seconds. In classification tasks, the model exhibits notable performance improvements over iterations, with BLEU scores reaching 0.84 and METEOR scores reaching 0.78 in the fifth iteration. The translation results showcase BLEU scores of 0.85 for English, 0.82 for French, 0.79 for German, 0.81 for Spanish, and 0.75 for Chinese, indicating the model's proficiency in generating high-quality translations across diverse languages.

Data-Driven-Based Intelligent Alarm Method of Ultra-Supercritical Thermal Power Units

In order to ensure the safe operation of the ultra-supercritical thermal power units (USCTPUs), this paper proposes an intelligent alarm method to enhance the performance of the alarm system. Firstly, addressing the issues of slow response and high missed alarm rate (MAR) in traditional alarm systems, a threshold optimization method is proposed by integrating kernel density estimation (KDE) and convolution optimization algorithm (COA). Based on the traditional approach, the expected detection delay (EDD) indicator is introduced to better evaluate the response speed of the alarm system. By considering the false alarm rate (FAR), and EDD, a threshold optimization objective function is constructed, and the COA is employed to obtain the optimal alarm threshold. Secondly, to address the problem of excessive nuisance alarms, this paper reduces the number of nuisance alarms by introducing an adaptive delay factor into the existing system. Finally, simulation results demonstrate that the proposed method significantly reduces the MAR and EDD, improves the response speed and performance of the alarm system, and effectively reduces the number of nuisance alarms, thereby enhancing the quality of the alarms.

Robust 3D coordinate transformation based on genetic algorithm intelligent weighting

In order to improve the solution quality of 3D coordinate transformation parameters, a robust optimization model is obtained by integrating the 3D coordinate transformation model within the overall framework of the genetic algorithm in this paper. The minimum of the weighted sum of squares of the point residuals of the common points is taken as the objective function for optimization, and the parallel search is started from the initial population, and the updating process of the solution set is used to generate new and better individuals, and the adaptive weight combination corresponding to the optimum of the fitness function is obtained at the end of the generation iteration. The generated simulated data and measured data were analyzed using the Hefei Light Source tunnel control network as an application scenario. The results show that the method in this paper can automatically reduce the influence of low-quality data on the model, and compared with the two robust conversion models of IGG III iterative weighting, the method in this paper is not affected by the empirical parameters in determining the weight combinations and the weighted sum of squares of the residuals of the solved points are smaller, so it can effectively improve the quality of the solved conversion parameters.

A Novel Mayfly Algorithm with Response Surface for Static Damage Identification Based on Multiple Indicators

This paper proposes a novel structural damage identification approach coupling the Mayfly algorithm (MA) with static displacement-based response surface (RS). Firstly, a hybrid optimal objective function is established that simultaneously considers the sensitivity-based residual errors of static damage identification equation and the static displacement residual. In the objective function, the static damage identification equation is addressed by the Tikhonov regularization technique. The MA is subsequently employed to conduct an optimal search and pinpoint the location and intensity of damages at the structural element level. To handle the inconformity of the static loading points and the measurement points of displacements, the model reduction and displacement extension techniques are implemented to reconstruct the static damage identification equation. Meanwhile, the static displacement-based RS is constructed to calculate the displacement residual in the hybrid objective function, thereby circumventing the time-consuming finite element calculations and improving computational efficiency. The identification results of the numerical box girder bridge demonstrate that the proposed method outperforms the particle swarm optimization, differential evolution, Jaya and whale optimization algorithms about both convergence rate in optimal searching and identification accuracy. The proposed method enables more accurate damage identification compared to methods solely based on the indicator of the residual of static damage identification equations or displacement residual. The results of identifying damage in the 21 element-truss structure and the static experiments on identifying damage in an aluminum alloy cantilever beam confirm the high efficiency of the proposed approach.

Robust two-dimensional PCANet with F-norm distance metric

The principal component analysis network (PCANet) is a simplified convolutional neural network (CNN) implemented using a deep subspace learning model. Since its typical PCA filters utilize the squared Frobenius-norm (F-norm) as the distance metric, it exhibits extreme sensitivity to outliers hidden within the data. Furthermore, PCANet faces challenges in extracting structural information in the row and column directions of images as the filters need to be pre-vectorized. To address these issues, we propose a robust bi-directional two-dimensional PCANet (RBDPCANet), wherein the filters utilize the F-norm instead of the squared F-norm to mitigate the impact of outliers. Additionally, the relationship between reconstruction errors and projection distances is simultaneously considered in the objective function. After a concise analytical description of the PCANet concept and related work, we present the methodology for generating three convolutional kernels in the form of RBDPCANet-1, RBDPCANet-2, and RBDPCANet-3 algorithms. These convolution kernels are iteratively obtained and are suitable for numerical implementation. Each of the three algorithms uses another optimization objective function. It has been shown that in image recognition tasks, the developed methods are characterized by better resistance to noise compared to the classic PCANet approach. The rotation invariance and convergence of RBDPCANet are demonstrated according to theoretical principles. The efficacy of the proposed method is validated using images from publicly available datasets. Extensive experimental results on these datasets demonstrate that the proposed RBDPCANet not only significantly boosts the efficiency of image classification but also has strong resistance to occlusions, changes in lighting, changes in orientation, etc. Our implementation code is available at https://github.com/Wesley-li1/RBDPCANet-.

Optimization Model of Emergency Evacuation Network Based on Genetic Algorithm

Aggregation and dispersion are the normal states. In economic production and social life, the evacuation of people and materials deserves extensive attention. From the perspective of mathematics and economics, arranging a thoughtful evacuation is essential for the best balance between personal satisfaction and cost. In this paper, evacuation is deeply studied by a genetic algorithm; the balance, cost, and willingness (satisfaction) priority models, which consider both satisfaction and cost, are established. In this paper, based on the field investigation and questionnaire survey conducted by Oriental Sports Center, overall satisfaction is inferred by the satisfaction of each person in the tested sample, and the time cost from the evacuation point to different reception points is inferred through actual investigation. Based on these data, a balance model is established, and the calculation is performed. The ratio of the total willingness value of the evacuated individuals to the total cost is taken as the function of the optimization objective. When the optimization objective function is complex, the genetic algorithm is used to search for the optimal solution by simulating the process of natural evolution. At the same time, the cost priority and willingness priority models are established to compare the advantages and disadvantages of different evacuation schemes and their applicable situations. The evacuation of Oriental Sports Center is studied using a genetic algorithm. The objective function value of equilibrium model optimization is 0.2155 (optimal solution), significantly different from willingness priority model optimization (0.0300). After calculating the optimization objective function value and cost value under the conditions of cost priority and willingness priority, a “People-oriented and cost-balanced” suggestion is put forward for a large crowd evacuation scheme.

Adaptive preferences: An evolutionary model of non-expected utility and ambiguity aversion

We enrich an evolutionary model with common and idiosyncratic uncertainty as in Robson (1996) by allowing for hidden actions (or phenotypic flexibility). In contexts where common uncertainty is ambiguous and idiosyncratic uncertainty is risky, the model generates both ambiguity aversion and non-expected-utility preferences for risk, thereby providing a link between the two types of behavior. While the general evolutionarily optimal objective function does not have an obvious similarity to functional forms studied in the literature, our main results show that it can be recast as a combination of familiar models of ambiguity aversion and non-expected utility. We show that particular classes of hidden actions generate the special cases of rank-dependent or divergence risk preferences embedded within a model of ambiguity aversion.

A Mesh-Based Approach for Computational Fluid Dynamics-Free Aerodynamic Optimisation of Complex Geometries Using Area Ruling

In this paper, an optimisation procedure is introduced that uses a significantly cheaper, and CFD-free, objective function for aerodynamic optimisation than conventional CFD-driven approaches. Despite the reduced computational cost, we show that this approach can still drive the optimisation scheme towards a design with a similar reduction in drag coefficient for wave drag-dominated problems. The approach used is ‘CFD-free’, i.e., it does not require any computational aerodynamic analysis. It can be applied to geometries discretised using meshes more conventionally used for ‘standard’ CFD-based optimisation approaches. The approach outlined in this paper makes use of the transonic area rule and its supersonic extension, exploiting a mesh-based parameterisation and mesh morphing methodology. The paper addresses the following question: ‘To what extent can an optimiser perform (wave) drag minimisation if using ‘area ruling’ alone as the objective (fitness) function measurement?’. A summary of the wave drag approximation in transonic and supersonic regimes is outlined along with the methodology for exploiting this theory on a typical CFD surface mesh to construct an objective function evaluation for a given geometry. The implementation is presented including notes on the considerations required to ensure stability, and error minimisation, of the numerical scheme. The paper concludes with the results from a number of (simple and complex geometry) examples of a drag-minimisation optimisation study and the results are compared with an approach using full-fidelity CFD simulation. The overall conclusions from this study suggest that the approach presented is capable of driving a geometry towards a similar shape to when using full-fidelity CFD at a significantly lower computational cost. However, it cannot account for any constraints, driven by other aerodynamic factors, that might be present within the problem.

Multiparameter optimization of the acid gas removal process using self-heat recuperation technology in IGCC system based on genetic algorithm

The acid gas removal (AGR) process in the integrated gasification combined cycle (IGCC) system consumes a large amount of heat and reduces the generation efficiency. Process modification of self-heat recuperation (SHR) technology based on vapor compression heat pumps is expected to reduce the energy consumption of the AGR process. On this basis, this paper conducted a comprehensive and parametric analysis of the AGR process integrated with a vapor compression heat pump. Wherein, two key parameters, the stripper pressure (P1) and the heat pump heat exchanger inlet pressure (P2), were analyzed in detail to understand the energy consumption characteristics of each module of the system. By establishing the optimization objective functions of minimizing system operating cost and exergy loss respectively, the system multi-parameter optimization was conducted based on the genetic algorithm by coding and solving in HYSYS and MATLAB. The corresponding parameter variables for the optimal cases of the two optimization objectives are also comparatively derived. The results showed that P1 mainly changes the SHR capacity of the system by changing the allowable flow rate of the SHR mass under different working conditions, while P2 mainly affects the SHR capacity of the system by changing the heat of the fraction at the top of the stripper. The minimum operating cost of the system decreased to 0.0655 $/kgH2S, which is 1.50 % lower compared to the original case. The minimum exergy loss of the system is reduced from 7788.89 kW to 7398.59 MW, which is about 5.01 % lower than that of the original case.