Inverse identification of material properties using artificial neural network - particle swarm optimization (ANN-PSO) in Taylor impact testing

Black-box physical adversarial stripes for hiding from infrared detectors at multiple views.

Enhanced pneumonia prognosis via a hybrid deep learning ensemble: Dense Net, Efficient Net, and VGG16 integration.

Integrating smart glasses and smart gloves in hybrid assembly/disassembly systems: an STPA-driven semi-automated risk management tool

Research on the method of guided wave mode excitation in rails based on mathematical model.

Optimizing fractal geometries for multifrequency antennas in industrial, scientific, and medical bands using evolutionary particle swarm optimization: Modeling and design considerations

An adaptive rank-based coevolutionary learning particle swarm optimization algorithm for server placement in edge computing

A co-evolutionary particle swarm optimizer with synergistic search for enhancing pattern fidelity in DMD lithography

Fly ash yield prediction-enabled optimization of municipal solid waste incineration: Reducing fly ash generation, disposal costs, and carbon emissions.

ABSTRACT The physical properties of concrete depend on the type of supplementary cementitious materials (SCMs); thus, its strength must be evaluated for specific purposes. In this study, the Random Forest regression (RFR) machine learning (ML) algorithm was used to estimate the compressive strength (CS) of blended concrete (BC) with fly ash (F_Ash). The hyperparameters of the RFR model were optimized using Particle Swarm Optimization (PSO) to enhance predictive performance. The optimized RFR model served as a surrogate model, and PSO found optimal input parameters for the best response. Key inputs included cement, GGBS, fine aggregate (FA), coarse aggregates (CA), fly ash (F_Ash), water content, superplasticizer and curing days, with CS as the output. Performance evaluation used indices, such as MAE, MAPE, MSE, RMSE, MBE, R 2, a20-index to assess accuracy. Sensitivity analysis showed the relationship between inputs and CS, highlighting the impact of F_Ash and other parameters on CS prediction. The model achieved an RMSE of 2.005 and an R 2 of 0.9858 for CS. The optimized response was confirmed at 71.42 MPa, with optimized input parameters: Cement = 365.94 kg/m3, GGBS = 263.184 kg/m3, F_Ash = 148.610 kg/m3, W = 123.907 litres, SP = 23.58 kg/m3, CA = 820.788 kg/m3, FA = 734.118 kg/m3, and days = 166.710.

ABSTRACT This study investigates the shear performance of ultra-high-performance concrete deep beams (UHPC-DBs) reinforced with hybrid fibers comprising 5D steel fibers and Forta-Ferro (FF) synthetic fibers. UHPC-DBs are widely used in bridges, piles, and transfer girders because of their high load-bearing capacity. Although fiber content strongly influences shear behavior, the contribution of synthetic fibers in hybrid systems remains insufficiently understood. To address this gap, finite element analysis based on the concrete damage plasticity (CDP) model was developed in ABAQUS and validated using experimental results from five previously tested UHPC-DBs with different fiber contents. Load – deflection response, crack patterns, shear capacity, mid-span deflection, fiber stress, and ductility were evaluated. Particle swarm optimization (PSO) was employed to identify the optimal hybrid fiber combination considering both shear performance and cost efficiency. Results indicate that a hybrid system containing 1.0% 5D steel fibers and 0.11% FF synthetic fibers provides the best performance-to-cost ratio, improving shear strength and ductility. Furthermore, UHPC-DBs reinforced with 1.0–2.5% steel fibers combined with 0.55% FF synthetic fibers achieved 92.83–99.19% of the shear capacity of beams reinforced solely with 1.5–3.0% steel fibers. These findings demonstrate the effectiveness of hybrid fiber reinforcement and support updating ACI 318–19 to account for synthetic fiber contributions practice.

ABSTRACT This study develops supervised machine learning models based on least squares support vector machine (LSSVM) and its hybrid variants integrated with particle swarm optimization (PSO), ant colony optimization (ACO) and grey wolf optimization (GWO) to predict CS of red-mud-based concrete. A comprehensive database of 198 experimental datasets including eight input variables and one output variable employed for model development and validation of models. Model performance was evaluated using statistical and error indices. The results indicate that hybrid optimization significantly enhances prediction accuracy, with the PSO-LSSVM model achieving the best performance (R2 = 0.944, RMSE = 0.047, MAE = 0.034), outperforming the standalone LSSVM model (R2 = 0.909, RMSE = 0.058, MAE = 0.045). Sobol sensitivity analysis reveals that fly ash content is the most influential parameter (0.583), followed by water (0.352), superplasticizer (0.277) and red mud (0.259). The findings demonstrate that hybrid LSSVM models provide a reliable and efficient approach for accurate CS prediction and mix optimization in sustainable red-mud-based concrete. The developed models were further integrated into a user-friendly web-based application to facilitate rapid and practical compressive strength prediction for engineers and researchers.

AI-driven digital twin and delay-aware surrogate MPC framework for biogas production

Hybrid fertilized particle swarm optimization for engineering design with application to vibration control

Towards intelligent mine ventilation: An inverse optimization approach for fan efficiency maximization via surrogate modeling and adaptive particle swarm optimization

GPU-enabled Decentralized, multi-robot path planning based on global evolutionary dynamic programming and local particle swarm optimization

Particle swarm optimization algorithm-optimized Si3N4 Raman metalens for 457 nm laser: Chromatic aberration control and dual polarization compatibility

Nowadays, fossil fuels, such as petrol, diesel, and coal, are being replaced by non-conventional energy resources (NCERs) because NCERs are free from CO2 emissions, and their availability is surplus in the environment. Because of these major advantages, NCERs are the main focus for researchers. The hydro energy-based power system (HEPS) is one of the important NCERs. The modelling of HEPS is complicated by their higher-order nature. Consequently, the literature is progressively proposing reduction approaches. This study proposes a novel model order reduction approach to approximate the higher-order HEPS, based on the particle swarm optimisation (PSO) algorithm and the factor division technique (FDT). The PSO algorithm is used to estimate the denominator polynomial, while the FDT approach is used to generate the numerator polynomial of the desired reduced order model (ROM). A sixth-order HEPS model is considered as a test case to demonstrate the superiority of the suggested approach. The outcomes are contrasted with established techniques found in the literature.

Beamforming has emerged as an essential enabling technique for beyond 5G and future 6G systems because it improves spectral efficiency. However, optimizing antenna weights in beamforming is a highly nonlinear and multidimensional problem that traditional approaches struggle to solve. To address this, we offer a unique beamforming strategy based on the Caterpillar Fungus Optimization (CFO) algorithm, which strikes an optimal balance between exploration and exploitation, making it ideal for large-scale antenna systems. The CFO is inspired by the rare lifecycle of caterpillar fungus considering its soil exploration and parasitic behaviors. Its unique blend of wave-like and spiral search strategies, dual parasitism operators, and hybrid noise-handling makes it stand out among bio-inspired algorithms, enabling high accuracy and robustness in complex engineering optimization problems. The proposed scheme has two goals: first, to reduce the number of active antenna elements, thereby improving energy efficiency and reducing system complexity; and second, to suppress side lobe levels (SLL), which mitigate interference and improve communication performance. To assess its efficacy, the CFO-based method is compared to five established algorithms: Artificial Rabbits Optimizer (ARO), Whale Shark Optimization (WSO), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Boomerang Aerodynamic Ellipse (BAE). According to simulation data, CFO maintains beamwidth deviations within 1% to 2% of the standard reference while achieving an average error reduction of up to 99.7% when compared to PSO and WSO. Furthermore, CFO outperforms all benchmark algorithms in terms of accuracy, and computing efficiency, delivering the lowest SLL deviations and the most steady convergence behavior. This paper provides a simulation-based beamforming optimization framework that employs the metaheuristic Caterpillar Fungus Optimization (CFO) algorithm for antenna array synthesis in beyond 5G and future 6G wireless systems.

Accurate characterization of the initial in-situ stress field is essential for stability assessment, support design, and surrounding rock control in deep underground engineering, yet field measurements are often highly scattered and three-dimensional inversion is computationally expensive. This study develops a robust and efficient inversion framework for a deeply buried underground powerhouse in the southeastern Tibetan Plateau. First, a three-dimensional borehole stress synthesis method is established by combining particle swarm optimization, the Huber loss, Levenberg-Marquardt iteration, and regularization to denoise multi-source measurements, suppress local outliers, and alleviate ill-conditioning in stress-tensor reconstruction. Second, a surrogate-assisted differential evolution workflow is constructed using a radial basis function network within a prediction-verification-correction active-learning loop to reduce the cost of repeated forward simulations while preserving global optimization capability. Application to the powerhouse shows that the mean relative error decreases from 14.12 to 9.34%, and the deviation variance decreases from 2.98 to 1.33 after optimization. The proposed framework improves both the reliability of inversion input data and the efficiency of field-scale stress reconstruction, providing practical support for stability evaluation and surrounding rock control in deep, geologically complex underground caverns.