Search Results (523)

Accurate prediction of mechanical properties is essential for quality control in hot strip rolling (HSR), where the relationships among chemical composition, process parameters, and mechanical properties are highly nonlinear under industrial conditions. In this work, a data-driven framework was established for the prediction and interpretation of yield strength (YS), tensile strength (TS), and elongation (EL) of hot-rolled strips based on industrial production data. A high-quality dataset was constructed through data collection, outlier removal, and feature selection. Six machine learning (ML) models were developed and compared, and particle swarm optimization (PSO) was employed for hyperparameter tuning. The results showed that random forest (RF) achieved the best overall predictive performance, with R² values of 0.979, 0.986, and 0.959 for YS, TS, and EL, respectively. In addition, faster convergence and better optimization performance were obtained by PSO than by genetic algorithm (GA) and artificial bee colony (ABC). Shapley additive explanations (SHAP) were further introduced to reveal both global feature importance and local feature contributions. The proposed framework provides an effective approach for mechanical property prediction and alloy design in HSR. Full article

Image segmentation is the first step of image processing. It allows us to comprehend and extract information from the digital image. Multilevel thresholding is one of the most commonly used image segmentation techniques because of its simplicity and effectiveness. However, with the higher threshold level required, the more complex the process of finding the optimal threshold values becomes more complex. In this research, an effective optimization-based image segmentation technique using Otsu’s multilevel thresholding technique as the objective function to overcome the difficulties of finding the best threshold values is proposed. Instead of using the exhaustive search process, which requires more time, the best threshold values are obtained using different optimization techniques based on the nature of the image. In this study, the optimized threshold values are computed based on Otsu’s scheme, Sobel filter with Brown Bear Optimization Algorithm (BBOA), which is compared with thresholds computed based on the Artificial Bee Colony (ABC) algorithm, Jaya Algorithm (JA), Moth Flame Optimization (MFO) algorithm, Whale Optimization Algorithm (WOA) algorithm, and Particle Swarm Optimization (PSO) algorithm for segmentation. The objective function includes the sum of the variances of all four channels, namely, Red, Green, Blue, and Gray (Sobel). The superiority of the proposed method (BBOA_S) is tested on ten natural color benchmark images to verify results, and the quality of the suggested method is evaluated quantitatively by applying popular image quality assessment parameters, including PSNR, SSIM, and FSIM. The experimental results clearly show the efficiency of the suggested method of segmentation. In fact, the suggested method of segmentation has a higher PSNR value, up to 26.11, compared to other optimization methods, which have lower values. Similarly, the suggested method has a high value of structural similarity, up to 0.988, indicating that it performs a great job in terms of structural similarity. The proposed method also highly minimizes the reconstruction error, as indicated by the minimum values of the MSE, which are close to 194. This is better compared to the results of most of the other methods, which were carried out on the same images. Although the FSIM values of the proposed method are comparable to the values of the other methods, it is evident that the proposed method is good and reliable, as indicated by the overall quantitative and visual assessment. Therefore, it is clear that the use of Otsu’s multi-layer thresholding and the Sobel filter effect in combination with BBOA is effective and good. Full article

To address node redundancy and coverage holes in Wireless Sensor Network (WSN) deployment, this paper proposes an Improved Northern Goshawk Optimization (INGO) algorithm with multiple enhancements. It integrates a Diverse Chaotic Map Initialization Strategy (DCMIS) into the standard Northern Goshawk Optimization (NGO) for Diverse, uniform initial populations and improved global exploration. A Bidirectional Population Evolution Dynamics (BPED) mechanism follows the pursuit-and-evasion phase, applying asymmetric logic—elite guidance and selective replacement of weak individuals—to escape local optima and accelerate global convergence. Simulations reveal uniform grid topologies and an average coverage ratio of 91.90% with INGO, outperforming Northern Goshawk Optimization (NGO), Artificial Bee Colony (ABC), Improved Wild Horse Optimizer (IWHO), and the Firefly Algorithm (FA). INGO also achieves 100.00% connectivity, eliminating isolated nodes and ensuring reliable full-network communication. These results indicate that INGO achieves higher coverage and full connectivity under the studied simulation setting, demonstrating its effectiveness for WSN deployment optimization. Full article

Wireless Sensor Networks (WSNs) enable energy-efficient data collection in dynamic environments but continue to face the dual challenges of severely constrained node energy and the spatiotemporal heterogeneity of data traffic. Inspired by honeybee foraging behavior, this paper proposes a hybrid optimization framework that integrates mixed-integer linear programming (MILP) and Markov decision processes (MDP), utilizing Q-learning for adaptive decision-making. The proposed framework systematically maps the dual-layer decision-making mechanism of honeybee foraging onto a synergistic architecture combining MILP-based global planning and MDP-based local adaptation, offering a novel bio-inspired solution for mobile sink trajectory planning and adaptive routing. Specifically, the upper-level MILP module simulates a colony-level global assessment of distant nectar sources, generating an initial global trajectory by determining the optimal access sequence of cluster heads to minimize the movement cost of the mobile sink. The lower-level Q-learning module simulates the individual-level local adaptation, where bees adjust harvesting behavior in real-time based on nectar quality and distance. This module continuously optimizes routing parameters based on real-time network states, including residual energy, the ratio of surviving nodes, data queue lengths, and cluster head density. The algorithm employs an $ϵ$-greedy strategy to balance exploration and exploitation, while a periodic decision-update mechanism is introduced to harmonize computational efficiency with learning stability. Furthermore, a multi-objective reward function is designed to jointly optimize energy efficiency, network lifetime, end-to-end latency, and path length. Extensive simulation results demonstrate that the proposed MILP-MDP hybrid framework significantly outperforms several representative baseline algorithms in terms of network lifetime extension and energy balance. These findings validate that the integration of bio-inspired foraging strategies and reinforcement learning provides an efficient and robust solution for trajectory planning and adaptive routing in dynamic WSNs. Full article

Explainable Artificial Intelligence (XAI) has become a critical requirement in medical image analysis, where transparency and interpretability are essential for clinical trust and decision support. Melanoma is recognized as one of the most deadly types of skin cancer, with its occurrence exhibiting an increasing pattern in recent times. However, detecting this cancer in its initial stages greatly increases patients’ chances of long-term survival. Various computer-based techniques have recently been proposed to diagnose skin lesions at their early stages. Even though the machine learning community has achieved a certain degree of success, there is still an unresolved research challenge regarding high error margins and the limited interpretability of automated systems. This study focuses on addressing both segmentation and classification tasks, with particular emphasis on two key concepts: (1) improving image quality to maximize distinguishability between foreground and background regions, thereby enhancing visual interpretability and segmentation accuracy and (2) eliminating redundant and cluttered feature information to generate the most discriminative and compact feature representations. The input images are initially processed using a novel metaheuristic contrast-stretching method to estimate image-specific key parameters, thereby enhancing lesion boundary clarity in a clinically interpretable manner. Following this, the improved images are fed into selected pre-trained deep models, including DenseNet-201, Inception-ResNet v2, and NASNet-Mobile. The extracted features from all pre-trained models are fused to produce resultant vectors, which are then refined using a bio-inspired feature selection method, termed entropy-controlled whale optimization, to retain only the most informative attributes. The selected discriminative feature set is subsequently classified using multiple classifiers. The results indicate that the proposed framework achieves superior performance compared to existing methods in terms of accuracy, sensitivity, specificity, and F1-score. Additionally, it facilitates a more explainable, transparent, and structured diagnostic pipeline appropriate for medical applications. Full article

Background: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, primarily due to late diagnosis. Although machine learning (ML) techniques have been widely applied for lung cancer classification, many studies lack a fully optimized end-to-end pipeline using routine clinical data. This study proposes an optimized ML framework that integrates demographic, lifestyle, and clinical features with systematic hyperparameter tuning to improve classification performance. Methods: A dataset of 309 patient records containing demographic, lifestyle, and clinical attributes was used. The data were preprocessed and split into training and testing sets in an 80:20 ratio. Feature selection was performed using metaheuristic algorithms, including Red Deer Optimization, Binary Grasshopper Optimization, Gray Wolf Optimization, and Bee Colony Optimization. Six ML classifiers—Logistic Regression, Support Vector Classifier, Gradient Boosting, Random Forest, K-Nearest Neighbors, and Gaussian Naive Bayes—were trained with optimized hyperparameters. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC–AUC. Results: The optimized pipeline significantly improved classification performance. Logistic Regression achieved the highest accuracy of 91.07% with an AUC of 0.91, outperforming more complex ensemble models. Gradient Boosting and Random Forest both achieved an accuracy of 87.5%, while other classifiers demonstrated moderate performance. Conclusions: The proposed optimized ML pipeline enhances lung cancer classification accuracy using routine clinical data. The results highlight that simpler, well-optimized models can outperform complex approaches on structured datasets. This framework shows strong potential for early lung cancer risk screening and clinical decision support, although further validation on larger datasets is recommended. Full article

Unauthorized occupation of parking spaces designated for individuals with disabilities remains a persistent challenge in urban environments, limiting accessibility and inclusive mobility. This paper proposes an integrated UAV-assisted enforcement framework that combines drone-based imaging, onboard license plate recognition (LPR), IoT connectivity, and a staged optimization strategy for energy-aware surveillance. The framework employs a two-phase approach: first, it derives energy-efficient UAV activation patterns via sleep–active scheduling, followed by coverage maximization under energy constraints. The inherently multi-objective problem—balancing energy consumption, coverage, and redundancy—is addressed via a weighted-aggregation formulation, enabling efficient optimization with classical metaheuristic algorithms. Seven algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO), Differential Evolution (DE), Artificial Bee Colony (ABC), and a Greedy baseline—are implemented in both conventional and staged variants to enable comprehensive evaluation. Experimental results demonstrate 32–45% reductions in energy consumption, over 95% coverage effectiveness, and 50–60% faster convergence compared to single-phase approaches, with all improvements statistically significant (p < 0.001). The proposed framework provides a scalable, practically deployable solution for intelligent enforcement of disability parking regulations while also enabling energy-efficient UAV coordination in smart urban monitoring systems. Full article

This study presents a novel hybrid Bat-Artificial Bee Colony (BA-ABC) algorithm for energy-efficient optimization in Wireless Sensor Networks (WSNs), addressing the critical challenge of limited node energy and network lifetime degradation. The proposed framework integrates the rapid local convergence of the Bat Algorithm with the robust global exploration of the Artificial Bee Colony to achieve unified optimization of clustering and routing processes. An adaptive multi-objective fitness function is developed to balance energy consumption, network lifetime, and communication efficiency, enabling dynamic, efficient resource utilization across varying network conditions. Comprehensive simulations conducted in MATLAB R2024a demonstrate that the proposed BA-ABC algorithm significantly outperforms conventional and recent optimization approaches. The results show a reduction in total energy consumption of approximately 22–30%, an improvement in network lifetime of 18–25%, and a latency reduction of nearly 24% compared to baseline methods such as Ant Colony Optimization (ACO). Statistical validation, including confidence intervals and hypothesis testing, confirms the robustness, stability, and consistency of the proposed framework across multiple simulation runs. Unlike existing hybrid and machine-learning-based approaches, the BA-ABC algorithm achieves high optimization performance without introducing excessive computational overhead or complex training requirements, making it suitable for resource-constrained WSN environments. Furthermore, the proposed method demonstrates strong scalability and adaptability, positioning it as a practical solution for real-world applications, including smart cities, environmental monitoring, and healthcare systems. This work contributes to the advancement of intelligent WSN optimization by providing a scalable, adaptive, and computationally efficient hybrid framework aligned with emerging trends in next-generation IoT-enabled networks. Full article

Against the backdrop of smart cities and digital cultural tourism, the accurate prediction of urban passenger flow is of great significance for public security management and resource allocation. However, existing studies mostly rely on single data sources or only perform a simple concatenation of multi-source features, lacking systematic indicator system design. Meanwhile, weekly or monthly data are commonly used with coarse temporal granularity, making it difficult to capture short-term fluctuations and lag effects. To overcome these limitations, this paper collects the daily passenger flow data of Hangzhou from 15 March 2024 to 15 March 2025; integrates multi-dimensional factors such as keyword search trends across platforms, holidays and major events, and online public opinion; and constructs three daily characteristic indicators: online search index, humanistic–meteorological index, and textual sentiment index. The data denoising, dimensionality reduction, and sentiment quantification are realized through methods including SSA, PCA, and SnowNLP. On this basis, a hybrid CNN-GRU model integrated with the attention mechanism is proposed. An improved artificial bee colony (ABC) algorithm is adopted for global hyperparameter optimization, and a weighted hybrid loss function (JQHL) is introduced to enhance the model’s adaptability to extreme values. The results show that the ABC-CNN-GRU-Attention model, incorporating multi-dimensional indicators, outperforms traditional methods on evaluation metrics, including MAE, RMSE, MAPE, R², and RPD, demonstrating a higher prediction accuracy and robustness. Full article

The Sparrow Search Algorithm (SSA) is a novel optimization method inspired by sparrows’ foraging and anti-predator behavior. It mimics their exploration and exploitation strategies to find near-optimal solutions for various optimization problems. This paper presents the first application of SSA to the widely recognized Permutation Flowshop Scheduling Problem (PFSP) with the makespan criterion as the optimization target. Our study aims to assess the effectiveness and robustness of this cutting-edge metaheuristic through computational experiments and statistical analysis. The proposed SSA is a hybrid variant that incorporates the Variable Neighborhood Search (VNS) algorithm along with a Path Relinking Strategy. The effectiveness of the proposed method is evaluated through computational experiments on PFSP benchmark instances. The performance of the hybrid SSA is compared against several well-established swarm-intelligence metaheuristics, namely Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Tuna Swarm Optimization Algorithm (TSO), Particle Swarm Optimization Algorithm (PSO), Firefly Algorithm (FA), Bat Algorithm (BA), and the Artificial Bee Colony (ABC). To ensure fair comparison, all methods are implemented within the same computational framework as the hybrid SSA. The experimental results show that the proposed hybrid SSA achieves the lowest average mean error compared with the competing methods in solving the PFSP. The results were further validated through a comprehensive non-parametric statistical analysis using Friedman, Aligned Friedman, and Quade tests, followed by post-hoc analysis with p-adjusted values, as well as Kruskal–Wallis and Wilcoxon post-hoc tests. Full article

In real-world operation, battery electric buses often encounter conditions with significant and rapid load variations. To improve regenerative braking energy recovery efficiency under such dynamic load conditions, this paper proposes a braking control strategy based on dynamic load estimation. First, a load estimation method based on a time-varying interactive multiple-model unscented Kalman filter (TVIMM-UKF) is developed by leveraging the vehicle longitudinal dynamics model and IMU sensor data, achieving high-accuracy online load estimation. Second, a multi-objective constrained optimization model is established, and an improved artificial bee colony algorithm is introduced to realize optimal brake force distribution under time-varying loads. Based on this, a regenerative braking control strategy is designed by incorporating motor characteristics and system-level operational constraints, enabling precise adjustment of braking torque across the full load range. Finally, simulation studies are conducted under two typical driving cycles, CHTC-B and C-WTVC, to verify the effectiveness of the proposed strategy. The results show that under dynamic load conditions, the proposed strategy can effectively improve braking energy recovery efficiency in both driving cycles. Full article

The integration of photovoltaic (PV) generation with bidirectional electric vehicle (EV) fast-charging systems offers a promising pathway toward sustainable transportation and grid support. However, the dynamic coupling between maximum power point tracking (MPPT) perturbations and grid-side power quality presents a fundamental challenge in such multi-converter architectures. This paper addresses this challenge through a coordinated design and optimization framework for a grid-connected, PV-assisted bidirectional off-board EV fast charger. The system integrates a 184.695 kW PV array via a DC-DC boost converter, a common DC link, a three-phase bidirectional active front-end rectifier with an LCL filter, and a four-phase interleaved bidirectional DC-DC converter for the EV battery interface. A comparative evaluation of three MPPT algorithms establishes the Fuzzy Logic Variable Step-Size Perturb & Observe (Fuzzy VSS-P&O) as the optimal strategy, achieving 99.7% tracking efficiency with 46 μs settling time. However, initial integration of this high-performance MPPT reveals system-level harmonic distortion, with grid current total harmonic distortion (THD) reaching 4.02% during charging. To resolve this coupling, an Artificial Bee Colony (ABC) metaheuristic algorithm performs coordinated optimization of all critical PI controller gains. The optimized system reduces grid current THD to 1.40% during charging, improves DC-link transient response by 43%, and enhances Phase-Locked Loop (PLL) synchronization accuracy. Comprehensive validation confirms robust bidirectional operation with seamless mode transitions and compliant power quality. The results demonstrate that system-wide intelligent optimization is essential for reconciling advanced energy harvesting with stringent grid requirements in next-generation EV fast-charging infrastructure. Full article

The digital transformation of oil and gas pipeline networks has generated substantial volumes of unstructured maintenance documentation from communication systems, creating an urgent need for automated summarization to improve operational efficiency. However, domain-specific text summarization for pipeline communication maintenance remains challenging due to scarce labeled data and the high computational cost of fine-tuning large pretrained models. Parameter-efficient fine-tuning alleviates this issue, but its effectiveness strongly depends on appropriate hyperparameter selection. This paper proposes a unified framework that combines weight-decomposed low-rank adaptation with an enhanced Artificial Bee Colony algorithm for automated hyperparameter optimization. The enhanced algorithm addresses two specific limitations of the standard Artificial Bee Colony algorithm: uninformed random initialization that ignores promising regions, and premature abandonment of stagnated solutions that discards partially useful search directions. These two components represent principled design choices, each targeting a distinct bottleneck in applying swarm intelligence search to high-dimensional mixed-type hyperparameter spaces. The method introduces a hybrid initialization strategy to exploit prior knowledge and a stagnation-guided local search mechanism to refine stagnated solutions instead of discarding them, achieving a better balance between exploration and exploitation. Experimental results on a public Chinese summarization benchmark and an industrial oil and gas pipeline communication maintenance corpus show that the proposed approach consistently outperforms full fine-tuning, manually tuned parameter-efficient methods, and several evolutionary optimization baselines in terms of ROUGE metrics. The automated search introduces modest additional computational overhead compared to manual tuning while eliminating expert-dependent hyperparameter configuration and achieving consistent performance gains across both datasets. Overall, the proposed framework provides an efficient and robust solution for adapting large language models to specialized summarization tasks in the context of pipeline communication system maintenance. Full article

With rapid economic development in China’s coastal regions, more oil stations are being built on soft soil foundations, facing risks such as foundation settlement and pipeline failures. Mechanical vibrations of oil pumps can induce resonance in pipelines, leading to rupture, leakage, and fire or explosion, threatening both safety and sustainable operation. Traditional monitoring methods, relying on physical models or data-driven approaches alone, are limited in capturing these coupled risks. This study proposes an ABC-XGBoost hybrid risk warning model, where the artificial bee colony algorithm optimizes XGBoost hyperparameters (iteration number, tree depth, learning rate) to improve predictive accuracy. By using multidimensional data—such as internal pressure, vibration amplitude, and ground settlement—the model evaluates stress and resonance risks in real time, supporting sustainable safety management. Validation with real station data shows an accuracy of 95.22%, 2.61% higher than the unoptimized model, demonstrating effective early warning and contribution to sustainable pipeline operation. Full article

Navigation planning and execution for underwater gliders operating in thermocline-affected environments is challenging due to the coupled influence of energy constraints, spatially distributed environmental disturbances, and limited control authority. Spatially varying thermocline structures act as structured environmental disturbances that degrade motion efficiency and tracking accuracy, and therefore must be explicitly considered in both path planning and control design. This paper proposes a hierarchical control-oriented decision framework for underwater glider navigation in thermocline regions. At the planning layer, a thermocline-aware multi-objective optimization problem is formulated to regulate the trade-off between navigation efficiency and cumulative environmental disturbance, characterized by total path length and cumulative thermocline exposure, respectively. A multi-objective artificial bee colony (MOABC) algorithm is employed to generate a set of Pareto-optimal reference trajectories that explicitly reveal this trade-off. At the execution layer, pitch angle regulation is formulated as a stochastic tracking control problem under environmental uncertainty. A Markov Decision Process (MDP) is constructed to model the coupled effects of pitch control on energy consumption and trajectory deviation, and a deep deterministic policy gradient (DDPG) algorithm is adopted to synthesize a feedback control policy for adaptive pitch regulation during path execution. Simulation results demonstrate that the proposed framework effectively reduces cumulative thermocline exposure and overall energy consumption while maintaining improved trajectory consistency compared with representative benchmark methods. These results indicate that integrating multi-objective planning with learning-based control provides an effective control-oriented solution for constrained underwater glider navigation in thermally stratified environments. Full article