Search Results (76)

As market competition intensifies, the survival and development of suppliers increasingly rely on research and development (R&D) investment and innovation. Due to the uncertainty of factors affecting supplier R&D investment, the risks faced by supplier R&D investment are also uncertain. Therefore, identifying and assessing risks in advance and controlling risks can provide effective support for suppliers to carry out risk management of R&D investment. This paper selects key factors through literature review and factor analysis, and establishes a risk index evaluation system for R&D investment of medical material suppliers. Seventeen indicators that affect and constrain project investment factors were identified as input variables of the back propagation (BP) neural network, the comprehensive score of the R&D investment risk assessment was used as the output variable of medical supplies suppliers, and a risk assessment model for the R&D investment of medical material suppliers was established. By leveraging the ability of particle swarm optimization (PSO), whale optimization algorithm (WOA), and genetic algorithm (GA) to search for global optimal solutions, the BP neural network is improved to avoid becoming trapped in local optimal solutions and enhance the model’s generalization ability. The improvement in accuracy and convergence speed of these three methods is compared and analyzed. The results show that the BP neural network improved by the genetic algorithm has better accuracy and faster convergence speed in predicting and assessing risks. This indicates that the BP neural network model improved by genetic algorithm is effective and feasible for predicting the risk assessment of the R&D investment of medical supplies suppliers. Full article

Robotic tack welding poses challenges in path optimization due to local optimum entrapment, limited adaptability, and high-dimensional complexity. To overcome these challenges, a Lévy flight-enhanced improved whale optimization algorithm (LF-IWOA) was developed. The algorithm combines elite opposition-based learning (EOBL), differential evolution (DE), and Lévy flight (LF) to improve global exploration capability, increase population diversity, and improve convergence. Additionally, a dynamic trajectory optimization model is designed to consider joint-level constraints, including velocity, acceleration, and jerk. The performance of LF-IWOA was evaluated using two industrial workpieces with varying welding point distributions. Comparative experiments with metaheuristic algorithms, such as the genetic algorithm (GA), WOA and other recent nature-inspired methods, show that LF-IWOA consistently achieves shorter paths and faster convergence. For Workpiece 1, the algorithm reduces the welding path by up to 25.53% compared to the genetic algorithm, with an average reduction of 14.82% across benchmarks. For Workpiece 2, the optimized path is 18.41% shorter than the baseline. Moreover, the dynamic trajectory optimization strategy decreases execution time by 26.83% and reduces mechanical energy consumption by 15.40% while maintaining smooth and stable joint motion. Experimental results demonstrated the effectiveness and practical applicability of the LF-IWOA in robotic welding tasks. Full article

Aiming at improving the prediction accuracy of the gas dissolved in transformer oil which occurs with strong nonlinearity, this paper presents a method named CEEMDAN-PWOA-VMD-BIGRU for gas content prediction. First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to decompose the original gas sequence. To solve the problem of the strong nonlinear characteristic of the decomposed high-frequency components leads to a large error in prediction, this paper uses Variational Mode Decomposition (VMD) for secondary decomposition. Though VMD can decompose high-frequency modes well, the selection of the optimal decomposition number and the quadratic penalty factors often depends on subjective judgment, which may affect the accuracy of decomposition results. Therefore, Whale Optimization Algorithm (WOA) is applied to optimize the parameter setting of VMD. However, the search of WOA in the optimization process is random, which leads to the limitations of the optimization efficiency. To solve this problem, this paper further uses Proximal Policy Optimization (PPO) to improve WOA (PWOA). With the optimized parameters of PWOA, VMD obtains more accurate secondary decomposition results. Then, the trained Bidirectional Gated Recurrent Unit (BiGRU) model is used to predict each decomposed component, and finally these predicted components are reconstructed to obtain more accurate prediction results. The experimental results demonstrate that the mean absolute error (MAE) of the proposed model is reduced by 6.88%, 7.45%, and 5.69%, compared with the traditional algorithms of Long Short-term Memory network (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolution Network (TCN), respectively. Full article

Accurate segmentation of brain tumors in magnetic resonance imaging (MRI) remains a challenging task due to heterogeneous tumor structures, varying intensities across modalities, and limited annotated data. Deep learning has significantly advanced segmentation accuracy; however, it often suffers from sensitivity to hyperparameter settings and limited generalization. To overcome these challenges, bio-inspired metaheuristic algorithms have been increasingly employed to optimize various stages of the deep learning pipeline—including hyperparameter tuning, preprocessing, architectural design, and attention modulation. This review systematically examines developments from 2015 to 2025, focusing on the integration of nature-inspired optimization methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and novel hybrids including CJHBA and BioSwarmNet into deep learning-based brain tumor segmentation frameworks. A structured multi-query search strategy was executed using Publish or Perish across Google Scholar and Scopus databases. Following PRISMA guidelines, 3895 records were screened through automated filtering and manual eligibility checks, yielding a curated set of 106 primary studies. Through bibliometric mapping, methodological synthesis, and performance analysis, we highlight trends in algorithm usage, application domains (e.g., preprocessing, architecture search), and segmentation outcomes measured by metrics such as Dice Similarity Coefficient (DSC), Jaccard Index (JI), Hausdorff Distance (HD), and ASSD. Our findings demonstrate that bio-inspired optimization significantly enhances segmentation accuracy and robustness, particularly in multimodal settings involving FLAIR and T1CE modalities. The review concludes by identifying emerging research directions in hybrid optimization, real-time clinical applicability, and explainable AI, providing a roadmap for future exploration in this interdisciplinary domain. Full article

Metaheuristic algorithms, due to their superior global exploration capabilities and applicability, have emerged as critical tools for addressing complicated optimization tasks. However, these algorithms commonly depend on expert knowledge to configure parameters and design strategies. As a result, they frequently lack appropriate automatic behavior adjustment methods for dealing with changing problem features or dynamic search phases, limiting their adaptability, search efficiency, and solution quality. To address these limitations, this paper proposes an automated hybrid metaheuristic algorithm generation method based on Learning to Rank (LTR-MHA). The LTR-MHA aims to achieve adaptive optimization of algorithm combination strategies by dynamically fusing the search behaviors of Whale Optimization (WOA), Harris Hawks Optimization (HHO), and the Genetic Algorithm (GA). At the core of the LTR-MHA is the utilization of Learning-to-Rank techniques to model the mapping between problem features and algorithmic behaviors, to assess the potential of candidate solutions in real-time, and to guide the algorithm to make better decisions in the search process, thereby achieving a well-adjusted balance between the exploration and exploitation stages. The effectiveness and efficiency of the LTR-MHA method are evaluated using the CEC2017 benchmark functions. The experiments confirm the effectiveness of the proposed method. It delivers superior results compared to individual metaheuristic algorithms and random combinatorial strategies. Notable improvements are seen in average fitness, solution precision, and overall stability. Our approach offers a promising direction for efficient search capabilities and adaptive mechanisms in automated algorithm design. Full article

As a new form of economic activity driven by data resources and digital technologies, the digital economy underscores the strategic significance of data as a core production factor. This growing importance necessitates accurate and robust valuation methods. Data valuation poses core modeling challenges due to its nonlinear nature and the instability of neural networks, including gradient vanishing, parameter sensitivity, and slow convergence. To overcome these challenges, this study proposes a genetic algorithm-optimized BP (GA-BP) model, enhancing the efficiency and accuracy of data valuation. The BP neural network employs a symmetrical architecture, with neurons organized in layers and information transmitted symmetrically during both forward and backward propagation. Similarly, the genetic algorithm maintains a symmetric evolutionary process, featuring symmetric operations in both crossover and mutation. The empirical data used in this study are sourced from the Shanghai Data Exchange, comprising 519 data samples. Based on this dataset, the model incorporates 9 primary indicators and 21 secondary indicators to comprehensively assess data value, optimizing network weights and thresholds through the genetic algorithm. Experimental results show that the GA-BP model outperforms the traditional BP network in terms of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), achieving a 47.6% improvement in prediction accuracy. Furthermore, GA-BP exhibits faster convergence and greater stability. When compared to other models such as long short-term memory (LSTM), convolutional neural networks (CNNs), and optimization-based BP variants like particle swarm optimization BP (PSO-BP) and whale optimization algorithm BP (WOA-BP), GA-BP demonstrates superior generalization and robustness. This approach provides valuable insights into the commercialization of data assets. Full article

To address the nonlinear and non-stationary characteristics of dissolved gas concentration data in transformer oil, this paper proposes a hybrid prediction model (VMD-SSA-LSTM-SE) that integrates Variational Mode Decomposition (VMD), the Whale Optimization Algorithm (WOA), the Sparrow Search Algorithm (SSA), Long Short-Term Memory (LSTM), and the Squeeze-and-Excitation (SE) attention mechanism. First, WOA dynamically optimizes VMD parameters (mode number k and penalty factor α to effectively separate noise and valid signals, avoiding modal aliasing). Then, SSA globally searches for optimal LSTM hyperparameters (hidden layer nodes, learning rate, etc.) to enhance feature mining for non-continuous data. The SE attention mechanism recalibrates channel-wise feature weights to capture critical time-series patterns. Experimental validation using real transformer oil data demonstrates that the model outperforms existing methods in prediction accuracy and computational efficiency. For instance, the CH₄ test set achieves a Mean Absolute Error (MAE) of 0.17996 μL/L, a Mean Absolute Percentage Error (MAPE) of 1.4423%, and an average runtime of 82.7 s, making it significantly faster than CEEMDAN-based models. These results provide robust technical support for transformer fault prediction and condition-based maintenance, highlighting the model’s effectiveness in handling non-stationary time-series data. Full article

Research on clean production in automotive painting processes is a core component of achieving green manufacturing, addressing environmental regulatory challenges, and advancing sustainable development in the automotive industry by reducing volatile organic compound (VOC) emissions, optimizing resource utilization, and minimizing energy consumption. To reduce pollutants generated by automotive painting processes and improve coating efficiency, this study proposes a clean production method for automotive body painting based on an improved whale optimization algorithm from the perspective of “low-carbon consumption and emission-reduced production”. A multi-level, multi-objective decision-making model is developed by integrating three dimensions of clean production: material flow (optimizing material costs), energy flow (minimizing painting energy consumption), and environmental emission flow (reducing carbon emissions and processing time). The whale optimization algorithm is enhanced through three key modifications: the incorporation of nonlinear convergence factors, elite opposition-based learning, and dynamic parameter self-adaptation, which are then applied to optimize the automotive painting model. Experimental validation using the painting processes of TJ Corporation’s New Energy Vehicles (NEVs) demonstrates the superiority of the proposed algorithm over the MHWOA, WOA-RBF, and WOA-VMD. Results show that the method achieves a 42.1% increase in coating production efficiency, over 98% exhaust gas purification rate, 18.2% average energy-saving improvement, and 17.9% reduction in manufacturing costs. This green transformation of low-carbon emission-reduction infrastructure in painting processes delivers significant economic and social benefits, positioning it as a sustainable solution for the automotive industry. Full article

The construction of a power grillage is of great significance for promoting local economic development. Identifying the characteristics of foundation damage is a prerequisite for ensuring the normal service of the power grillage. To investigate the bearing mechanism and failure mode of the grillage root foundations, a novel research method with a transparent soil material was used to conduct model tests on different types of foundations using particle image velocimetry (PIV) technology. The results indicate that, compared to traditional foundations, the uplift and horizontal bearing capacities of grillage root foundations increased by 34.35% to 38.89% and by 10.76% to 14.29%, respectively. Furthermore, increasing the base plate size and burial depth can further enhance the extent of the soil displacement field. Additionally, PIV analysis revealed that the roots improve pile–soil interactions, transferring the load to the surrounding undisturbed soil and creating a parabolic displacement field during the uplift process, which significantly suppresses foundation displacement. Lastly, based on experimental data, an Elman neural network was employed to construct a load-bearing capacity prediction model, which was optimized using genetic algorithms (GAs) and the whale optimization algorithm (WOA), maintaining a prediction error within 3%. This research demonstrates that root arrangement enhances the bearing capacity and stability of foundations, while optimized neural networks can accurately predict the bearing capacity of grillage root foundations, thus broadening the application scope of transparent soil materials and offering novel insights into the application of artificial intelligence technology in geotechnical engineering. For stakeholders in the bearing manufacturing industry, this study provides important insights on how to improve load-bearing capacity and stability through the optimization of the basic design, which can help reduce material costs and construction challenges, and enhance the reliability of power grillage infrastructure. Full article

Skin cancer is a major global health concern and one of the deadliest forms of cancer. Early and accurate detection significantly increases the chances of survival. However, traditional visual inspection methods are time-consuming and prone to errors due to artifacts and noise in dermoscopic images. To address these challenges, this paper proposes an innovative deep learning-based framework that integrates an ensemble of two pre-trained convolutional neural networks (CNNs), SqueezeNet and InceptionResNet-V2, combined with an improved Whale Optimization Algorithm (WOA) for feature selection. The deep features extracted from both models are fused to create a comprehensive feature set, which is then optimized using the proposed enhanced WOA that employs a quadratic decay function for dynamic parameter tuning and an advanced mutation mechanism to prevent premature convergence. The optimized features are fed into machine learning classifiers to achieve robust classification performance. The effectiveness of the framework is evaluated on two benchmark datasets, PH2 and Med-Node, achieving state-of-the-art classification accuracies of 95.48% and 98.59%, respectively. Comparative analysis with existing optimization algorithms and skin cancer classification approaches demonstrates the superiority of the proposed method in terms of accuracy, robustness, and computational efficiency. Our method outperforms the genetic algorithm (GA), Particle Swarm Optimization (PSO), and the slime mould algorithm (SMA), as well as deep learning-based skin cancer classification models, which have reported accuracies of 87% to 94% in previous studies. A more effective feature selection methodology improves accuracy and reduces computational overhead while maintaining robust performance. Our enhanced deep learning ensemble and feature selection technique can improve early-stage skin cancer diagnosis, as shown by these data. Full article

Accurate path loss prediction within train carriages is crucial for deploying base stations along high-speed railway lines. The field strength at receiving points inside carriages is influenced by outdoor signal transmission, penetration through window glass, and multiple reflections within the carriage, making it challenging for traditional models to predict the field strength distribution accurately. To address this issue, this paper proposes a machine learning-based path loss prediction method that incorporates ensemble techniques of multiple neural networks to enhance prediction stability and accuracy. The Whale Optimization Algorithm (WOA) is used to optimize the output weight configuration of each neural network in the ensemble model, thereby significantly improving the overall model performance. Specifically, on the test set, the WOA-optimized ensemble model reduces RMSE by 1.47 dB for CI, 0.47 dB for CNN, 0.93 dB for RNN, 1.38 dB for GNN, 0.1 dB for Transformer, 0.09 dB for AutoML, 0.33 dB for the GA-optimized ensemble model, and 0.18 dB for the PSO-optimized ensemble model. Full article

Hyperspectral technology has become increasingly important in monitoring soil heavy metal pollution, yet hyperspectral data often contain substantial band redundancy, and band selection methods are typically limited to single algorithms or simple combinations. Multi-algorithm combinations for band selection remain underutilized. To address this gap, this study, conducted in Gejiu, Yunnan Province, China, proposes a multi-algorithm band selection method to enable the rapid prediction of lead (Pb) contamination levels in soil. To construct a preliminary Pb content prediction model, the initial selection of spectral bands utilized methods including CARS (Competitive Adaptive Reweighted Sampling), GA (Genetic Algorithm), MI (mutual information), SPA (Successive Projections Algorithm), and WOA (Whale Optimization Algorithm). The results indicated that WOA achieved the highest modeling accuracy. Building on this, a combined WOA-based band selection method was developed, including combinations such as WOA-CARS, WOA-GA, WOA-MI, and WOA-SPA, with multi-level band optimization further refined by MI (e.g., WOA-GA-MI, WOA-CARS-MI, WOA-SPA-MI). The results showed that the WOA-GA-MI model exhibited optimal performance, achieving an average R² of 0.75, with improvements of 0.32, 0.11, and 0.02 over the full-spectrum model, the WOA-selected spectral model, and the WOA-GA model, respectively. Additionally, spectral response analysis identified 22 common bands essential for Pb content inversion. The proposed multi-level combined model not only significantly enhances prediction accuracy but also provides new insights into optimizing hyperspectral band selection, serving as a valuable scientific foundation for assessing soil heavy metal contamination. Full article

This research develops an equation to describe the relationship between stress (σ) and strain (ε) in concrete under different conditions. It includes important parameters from earlier studies to improve predictions of stress–strain behavior, especially for concrete with crumb rubber and stainless-steel fibers at various temperatures. The initial phase assessed three existing stress–strain formulas as a basis for optimization. Using the Genetic Algorithm (GA) and the Whale Optimization Algorithm (WOA), a new equation was created to simulate the stress–strain relationship while considering temperature changes and material additions. Results showed that Formula (1), optimized with the WOA, performed much better than other polynomial and exponential formulas, proving the WOA’s effectiveness over the traditional GA. A comparison of the mechanical properties from experiments and those predicted by the new formula showed a high level of accuracy. Key properties like the maximum stress, strain at maximum stress, modulus of elasticity, and toughness were well captured. The findings highlight how temperature and material composition significantly affect concrete’s mechanical behavior. Overall, this research offers important insights into the factors influencing concrete performance, providing a solid framework for future studies and practical applications in engineering and construction. The proposed formula is a reliable tool for predicting concrete’s mechanical properties under various conditions, which aids in better modeling and optimization in concrete design. Full article

Transmission networks face continuous electrical and mechanical stresses due to increasing system challenges and power losses. Transmission networks require special focus and detailed studies each time a load or a generator emerges to the grid. The interline power flow controller (IPFC) is a relatively new scheme that is implemented in the transmission network to improve transmission efficiency, decrease transmission losses, and enhance voltage profile. In this paper, the interline power flow controller’s impact on transmission network performance is investigated as it is implemented within the IEEE 5-bus, 14-bus, and IEEE 57-bus systems. In addition, the whale optimization algorithm (WOA) is used to optimize the interline power flow controller locations within the system to achieve optimal transmission system performance. WOA performance is also compared to genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, and the superiority of the proposed WOA-based control is proved. The robustness of the optimized system against load variations is investigated and the results introduced affirm the capability of the interline power flow controller to enhance transmission network efficiency. Full article

Intrusion detection in network systems is a critical challenge due to the ever-increasing volume and complexity of cyber-attacks. Traditional methods often struggle with high-dimensional data and the need for real-time detection. This paper proposes a comprehensive intrusion detection method utilizing a novel wrapped feature selection approach combined with a long short-term memory classifier optimized with the whale optimization algorithm to address these challenges effectively. The proposed method introduces a novel feature selection technique using a multi-layer perceptron and a hybrid genetic algorithm-particle swarm optimization algorithm to select salient features from the input dataset, significantly reducing dimensionality while retaining critical information. The selected features are then used to train a long short-term memory network, optimized by the whale optimization algorithm to enhance its classification performance. The effectiveness of the proposed method is demonstrated through extensive simulations of intrusion detection tasks. The feature selection approach effectively reduced the feature set from 78 to 68 features, maintaining diversity and relevance. The proposed method achieved a remarkable accuracy of 99.62% in DDoS attack detection and 99.40% in FTP-Patator/SSH-Patator attack detection using the CICIDS-2017 dataset and an anomaly attack detection accuracy of 99.6% using the NSL-KDD dataset. These results highlight the potential of the proposed method in achieving high detection accuracy with reduced computational complexity, making it a viable solution for real-time intrusion detection. Full article