Absorbing materials and structures are widely used in military and civilian fields, such as electromagnetic wave protection and stealth technology. However, current materials face challenges including high mass, limit...
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Absorbing materials and structures are widely used in military and civilian fields, such as electromagnetic wave protection and stealth technology. However, current materials face challenges including high mass, limited loadbearing capacity, and the lack of integrated material-structure designs. In this study, we combined additive manufacturing technology with a hybrid optimization algorithm, integrating large mutation genetic and ant colony algorithms, to develop a novel lightweight honeycomb structure. The optimized structure achieved broadband absorption over 91.84 % of the 2 GHz-40 GHz range and demonstrated superior mechanical performance, with a maximum deflection of 13.72 mm and a load capacity of 2.87 kN. These findings present an innovative strategy for designing lightweight structures that effectively balance electromagnetic absorption and mechanical durability.
An optimization algorithm based on SLSQP solver was created to design helical compression springs. The goal is to reach the required behavior even when the standards formula is inaccurate. Indeed, it was already shown...
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An optimization algorithm based on SLSQP solver was created to design helical compression springs. The goal is to reach the required behavior even when the standards formula is inaccurate. Indeed, it was already shown in previous works that the classic formula determining the global load-length behavior of the spring is not always accurate enough because it does not consider the effects of the spring's ends, in particular for springs with low index and low number of coils. Based on a new tri-linear model, this algorithm finds optimized spring design which respects the required operative points. In order to test the algorithm, the first tests were carried out to optimize the design of a PLA 3D-printed spring. It was shown that the initial design stiffness is overestimated by over 28% by the formula extracted from the standards. Then, the design of the spring was implemented in the algorithm to optimize the pitch and the number of active coils. The experimental curve of the optimized design spring goes through the target behavior, with a non-linearity at the beginning of the deflection. The experimental error about the final stiffness is successfully reduced to 1.1%. It is hoped that this work will provide a valuable progress to assist engineers and manufacturers in helical spring design.
In this research article, we present an optimization algorithm aimed at finding the optimal solution for nonlinear 2-dimensional fractional optimal control problems that arise from nonlinear fractional dynamical syste...
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In this research article, we present an optimization algorithm aimed at finding the optimal solution for nonlinear 2-dimensional fractional optimal control problems that arise from nonlinear fractional dynamical systems governed by Caputo derivatives under Goursat-Darboux conditions. The system dynamics are described by equations such as the Klein-Gordon, convection-diffusion, and diffusion-wave equations. Our algorithm utilizes a novel class of basis functions called generalized Laguerre polynomials (GLPs), which are an extension of the traditional Laguerre polynomials. To begin, we introduce the GLPs and their properties, and we develop several new operational matrices specifically tailored for these basis functions. Next, we expand the state and control functions using the GLPs, with the coefficients and control parameters remaining unknown. This expansion allows us to transform the original problem into an algebraic system of equations. To facilitate this transformation, we employ operational matrices of Caputo derivatives, the rule of 2D Gauss-Legendre quadrature, and the method of Lagrange multipliers.
As one representative of last-mile logistics in intelligent transportation systems, the on-demand food delivery (OFD) service has gained rapid market growth but also faces multiple challenges. One of the critical issu...
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As one representative of last-mile logistics in intelligent transportation systems, the on-demand food delivery (OFD) service has gained rapid market growth but also faces multiple challenges. One of the critical issues is the order dispatching problem (ODP) with an NP-hard nature, which refers to dispatching a large number of orders to riders reasonably in real time with very limited decision time. To address the ODP, this paper proposes an optimization algorithm based on graph neural networks (GNN) by combining the advantages of machine learning (ML) techniques and operational research (OR) methods: 1) The ML component learns to reduce the solution space by filtering out inappropriate riders for each order, handling the large-scale complexity of ODP. Specifically, we present a rider modeling approach by using GNN to better characterize rider information;besides, two attention mechanisms are designed to adaptively learn the matching relationship between riders and orders. 2) The OR component ensures the solution quality with a greedy and regret value-based dispatching heuristic. Extensive experiments are conducted on real-world datasets to evaluate the performance of the proposed method by comparing it with other existing models and algorithms. The results show that the design of our ML model is effective in yielding better prediction results, and the proposed GNN-based optimization algorithm can effectively and efficiently solve the ODP by improving delivery efficiency and customer satisfaction.
This research paper presents a novel optimization method called the Synergistic Swarm optimization algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optima...
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This research paper presents a novel optimization method called the Synergistic Swarm optimization algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search *** cooperation enhances the exploitation of promising regions in the search space while maintaining exploration ***,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and *** leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization *** effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design *** experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and *** codes of SSOA are available at:https://***/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.
The teaching-learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened t...
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The teaching-learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching-learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO's exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks, including the seven unimodal tasks (f1-f7) and six multimodal tasks (f8-f13). The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques, such as HS, PSO, MFO, GA and HHO. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article (and its supplementary information files).
With the reform of teaching methods, hybrid online and offline teaching modes have been used increasingly in college courses. In this setting, the factors affecting academic performance are more complex, making it mor...
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With the reform of teaching methods, hybrid online and offline teaching modes have been used increasingly in college courses. In this setting, the factors affecting academic performance are more complex, making it more challenging to predict students' performance. Therefore, there is an urgent need for higher- performance prediction algorithms. This study briefly analyzed college students' learning in ideological and political courses. Then, the learning features of college students in the courses were extracted using the Super Star platform and teaching system. Feature selection was carried out based on the information gain rate, while the training set was balanced using the synthetic minority oversampling technique (SMOTE). Moreover, the seagull optimization algorithm (SOA) was applied to optimize the hyperparameters of eXtreme Gradient Boosting (XGBoost) to develop the SOA-XGBoost algorithm for early warning of performance. Experiments were performed on the collected datasets. It was found that the effect of the SOA-XGBoost algorithm on the early warning of performance improved significantly following SMOTE processing. The F1- value reached 0.955 and the area under the curve value was 0.976. The SOA exhibited superior performance in hyperparameter optimization compared with other algorithms such as the grid search. The SOA-XGBoost algorithm also achieved the best results in early warning of performance. These results confirm the effectiveness of the proposed SOA-XGBoost algorithm for early warning of performance, and the method can be widely applied in practice.
Particle swarm optimization algorithm is a branch of evolutionary computing and can search for a better solution in a given feature space. This paper introduces the particle swarm optimization algorithm and greedy str...
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Particle swarm optimization algorithm is a branch of evolutionary computing and can search for a better solution in a given feature space. This paper introduces the particle swarm optimization algorithm and greedy strategy for the defect detection and location of industrial components and then proposes a greedy particle swarm optimization algorithm. This paper employs sparse random projection to map the vast high-dimensional information to the low-dimensional space while maintaining the relative distance between the data. This employment helps to accelerate the training and prediction speed of the model and remove some unimportant features or noise. This algorithm first adopts particle swarm optimization (PSO) to initialize the cluster centers in an effort to minimize the maximum distance between all unlabeled data points and these centers. The algorithm then utilizes the greedy strategy to select a batch of data points to represent the corresponding features of the normal image, thereby improving the coverage of the model to the data. Experiments have shown that the results of most categories of data sets are close to or better than the current existing methods, especially in defect detection. In terms of defect localization for object categories, our method achieves a pixel-level anomaly localization index (AUROC) of 98.3% on the MVTec AD dataset. [GRAPHICS]
The suspended sediment load transported by rivers can be estimated using various methodologies, including those based on artificial intelligence. In this study, we employed the Long Short-Term Memory (LSTM) model to e...
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The suspended sediment load transported by rivers can be estimated using various methodologies, including those based on artificial intelligence. In this study, we employed the Long Short-Term Memory (LSTM) model to estimate the suspended sediment concentration in the Mississippi River, United States of America. The input variables for the LSTM model included river discharge, water depth, suspended sediment load, and flow velocity. To enhance the model's performance, the input data and initial parameters were optimized using the Red Fox optimization (RFO) algorithm, resulting in a super-optimized LSTM model (SLSTM-RFO) developed through a two-phase optimization process. Additionally, sediment load estimations were conducted using alternative models, specifically the Artificial Neural Network (ANN) and Generalized Regression Neural Network (GRNN) models. The performance of these models was assessed using five performance indicators, the correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE), Nash and Sutcliffe efficiency (NS), and RMSE observations standard deviation ratio (RSR), demonstrating that the SLSTM-RFO model significantly outperformed the other models. Specifically, the SLSTM-RFO yielded improved estimation results, achieving reductions in error (RMSE) of 73.30%, 81.50%, and 82.56% compared to the LSTM, ANN, and GRNN models, respectively.
Nowadays,meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization *** this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Coronavirus Ma...
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Nowadays,meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization *** this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Coronavirus Mask Protection algorithm(CMPA),is proposed based on the virus transmission of *** main inspiration for the CMPA originated from human self-protection behavior against *** CMPA,the process of infection and immunity consists of three phases,including the infection stage,diffusion stage,and immune ***,wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves,which are similar to the exploration and exploitation in optimization *** study simulates the self-protection behavior mathematically and offers an optimization *** performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions,CEC2020 suite problems,and three truss design *** statistical results demonstrate that the CMPA is more competitive among these state-of-the-art ***,the CMPA is performed to identify the parameters of the main girder of a gantry *** show that the mass and deflection of the main girder can be improved by 16.44%and 7.49%,respectively.
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