Search Results (213)

In a digital watermarking technology system, robustness, imperceptibility and payload capacity are the three core performance indicators that restrict one another. How to achieve the optimal balance between the three is still the key scientific challenge to be solved in this field. This paper proposes a digital watermarking algorithm based on the magnitude coefficient of Non-Subsampled Shearlet Transform Fast and Accurate Polar Harmonic Fourier Moments (NSST-FAPHFMs) and the Weibull–Power Cauchy (W-PC) statistical model. The algorithm consists of two stages: watermark embedding and detection. In the embedding phase, the original image is first decomposed by NSST multi-scale decomposition, and the high-frequency subbands are divided into non-overlapping blocks and partitioned. High-energy coefficient blocks are extracted to obtain NSST-FAPHFM magnitude coefficient features, which serve as robust carriers for watermark embedding. In the detection phase, the W-PC distribution is used to accurately statistically model the above magnitude coefficients to characterize their heavy-tailed characteristics and strong correlation structure. Maximum likelihood estimation (MLE) is employed to estimate the model parameters, and a blind watermark detection mechanism is further constructed by integrating the W-PC model with the Local Optimal Detector (LOD) under the Neyman–Pearson (N-P) criterion. Experimental results show that the proposed algorithm has good imperceptibility, and the area under the receiver operating characteristic curve (AUROC) can reach 0.9991 without attack. The algorithm maintains strong robustness against various attacks and can effectively realize the joint optimization of the three core performance indicators of the watermarking system. Full article

Accurate boxing action recognition is critical for intelligent combat training, action quality assessment, and sports injury prevention. However, existing deep learning approaches face three key challenges: limited feature extraction for high-speed non-rigid boxing motions, weak robustness against background interference and occlusion, and performance instability from labor-intensive manual hyperparameter tuning. Furthermore, the original Centered Collision Optimizer (CCO), a biomimetic algorithm inspired by celestial collision dynamics, suffers from insufficient population diversity, poor adaptive regulation, and premature convergence in high-dimensional hyperparameter optimization tasks. To address these issues, this paper proposes a novel biomimetic optimization-driven boxing action recognition framework, where an Enhanced Adaptive Centered Collision Optimizer (EACCO) automatically optimizes the hyperparameters of a Multi-Scale Spatio-Temporal Adaptive ConvNeXt (MSTA-ConvNeXt) network. First, the MSTA-ConvNeXt backbone integrates multi-scale dynamic deformable convolution, a Bi-GRU spatio-temporal fusion module, and a dual-channel attention mechanism to enhance fine-grained feature extraction and temporal modeling. Second, three biomimetic improvements are introduced to CCO: Tent chaotic elite opposition-based initialization, adaptive nonlinear convergence factor with dynamic weight guidance, and adaptive Gaussian-Cauchy hybrid mutation, which balance exploration and exploitation and avoid local optima. Experiments on two public benchmark datasets show that the proposed framework achieves 96.1% accuracy, 95.9% precision, 95.7% recall, and 95.8% F1-score on the Boxing Jab Skeleton Dataset, and 95.4% accuracy, 95.2% precision, 94.9% recall, and 95.0% F1-score on the Olympic Boxing dataset, outperforming all state-of-the-art methods. Ablation studies validate the effectiveness of each EACCO component and confirm that this biomimetic hyperparameter optimization approach outperforms manual tuning and other popular optimizers. This work provides an effective biomimetic optimization solution for intelligent sports action recognition. Full article

To address the difficulty of accurately capturing the dynamic patterns of carbon emission time series—characterized by nonlinearity, non-stationarity, and complex fluctuations—this paper proposes a carbon emission prediction model based on an elite-guided collaborative social spider learning optimization algorithm (EGC-SSLO) integrated with a Long short-term memory (LSTM) network. First, considering the limitations of the standard stochastic social learning optimization (SSLO) algorithm in complex high-dimensional optimization problems, such as insufficient elite information guidance, weak local exploitation in the later stages, and a tendency to become trapped in local optima, three complementary improvement strategies are introduced. The adaptive elite mean-guided search strategy enhances the search directionality by incorporating the cooperative information of the best individual and the elite mean. The worst-individual hybrid Cauchy–Lévy search mechanism achieves a dynamic balance between early-stage global exploration and late-stage local exploitation through long-range Lévy flights and fine-grained Cauchy perturbations. The quadratic directional exploitation strategy further refines the search trajectory of candidate solutions, thereby improving convergence accuracy. These three strategies significantly enhance the optimization performance without increasing the time complexity order of the algorithm. Experimental results on the CEC2017 (30-dimensional), CEC2020 (20-dimensional), and CEC2022 (20-dimensional) benchmark suites demonstrate that EGC-SSLO consistently outperforms classical algorithms such as PSO, GWO, and HHO, as well as their improved variants, in terms of convergence accuracy, convergence speed, and robustness. Furthermore, the Wilcoxon rank-sum test and Friedman test confirm that the observed improvements are statistically significant. Finally, an EGC-SSLO-LSTM carbon emission prediction model is constructed and applied to daily carbon emission data in China from 2019 to 2025 for empirical analysis. The experimental findings show that the EGC-SSLO-LSTM model markedly outperforms both the standard LSTM and SSLO-LSTM approaches across key evaluation metrics, including mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R²). In particular, the MAE is decreased by 39.9% and 4.64% compared with the two benchmark models, respectively, which highlights the strong effectiveness and practical potential of the proposed method in real-world carbon emission forecasting applications. Full article

To tackle the drawbacks inherent in the Chinese Pangolin Optimization (CPO) algorithm, such as uneven population initialization distribution and a tendency to fall into local optimal solutions, this paper proposes an ACDCPO algorithm that integrates boundary-adaptive contraction initialization, Cauchy inverse cumulative distribution mutation, and dynamic opposition-based learning strategies, which effectively enhances the uniformity of population distribution, improves the ability to jump out of local optimum, and strengthens the adaptive coordination between exploration and exploitation. To validate its performance, the proposed ACDCPO is compared with nine representative algorithms using the CEC2017 and CEC2022 test functions. The results verify that ACDCPO achieves remarkably higher convergence precision and stability than the comparative algorithms. In four typical engineering optimization tasks, ACDCPO shows strong constraint handling ability and engineering adaptability. In addition, based on near-infrared spectrum data, the ACDCPO algorithm optimized the BP network model for the moisture content prediction of Dendrobium huoshanense, and the coefficient of determination (R²) reached 91.211%, which verified the effectiveness of the method in practical applications. Full article

The Ivy Optimization Algorithm (IVY) is a novel swarm intelligence optimization algorithm that simulates the phototropic growth mechanism of plants. To comprehensively improve the overall optimization performance, this paper proposes an enhanced Ivy Optimization Algorithm (LSIVY) integrating improved Logistics chaotic mapping, sine–cosine operator, and adaptive t-distribution mutation strategy. Firstly, an improved cascaded Logistics chaotic mapping is used for population initialization. The double arcsine transformation improves the ergodicity and uniformity of chaotic sequences, so that initial solutions are distributed more evenly in the search space, population diversity is enhanced, and premature convergence is suppressed. Secondly, the sine–cosine operator is embedded into the position update mechanisms of IVY growth, climbing, and propagation evolution. Nonlinearly decreasing control parameters realize adaptive switching between global exploration and local exploitation and accelerate convergence. Thirdly, an adaptive t-distribution mutation strategy is designed to dynamically adjust mutation intensity according to the iteration cycle and implement directional perturbation at the optimal solution position. It combines the large-scale exploration advantage of the Cauchy distribution and the local fine search merit of the Gaussian distribution, which significantly improves the ability to escape from local optima. Comparative experiments with eight mainstream metaheuristics (DE, WOA, GWO, HHO, DBO, MBWO, AOO, native IVY) are conducted with 30 independent runs on 30-dimensional CEC 2014 (30 test functions) and CEC 2020 (10 composite functions). Quantitatively, LSIVY achieves 20~30 orders of magnitude higher optimization accuracy than standard IVY on unimodal functions, and its average standard deviation across all benchmarks drops by 4–6 orders of magnitude. LSIVY ranks first on all CEC 2020 composite functions, reducing over 30% of iterations compared with native IVY. Three classical constrained mechanical design problems (three-bar truss, cantilever beam, pressure vessel) are adopted for engineering verification. In the pressure vessel case, the average manufacturing cost of LSIVY is reduced by 9.2% against standard IVY, and the standard deviation of three engineering cases decreases by 2–3 orders on average, demonstrating remarkable robustness. The proposed algorithm not only improves the theoretical system of plant-inspired swarm intelligence algorithms but also has great application prospects in mechanical structure lightweight design, industrial equipment cost optimization, and other practical engineering fields. Full article

Background/Objectives: Oral cancer is one of the most common types of cancer, with high mortality rates if not detected early. Traditional diagnostic methods based on clinical examination rely on experience, leading to delays in early and reliable diagnosis. In recent years, medical imaging and AI-based computer-aided diagnostic systems have shown promising results in the automated identification of oral cancer. In particular, the efficient management of high-dimensional feature spaces in machine learning and deep learning approaches directly impacts classification performance. In this context, metaheuristic-based feature selection technics is a critical component because of eliminating redundant and irrelevant features. To address these challenges, this study proposes a metaheuristic-based feature selection method to reduce feature dimensionality and enhance the classification performance of oral cancer detection. Methods: This study proposes an improved Honey Badger Algorithm-based feature selection approach for the automated detection of oral cancer. In the proposed method, the distance vector used in the HBA method has been redefined to improve the balance between exploration and exploitation. Additionally, a new Cauchy mutation-based migration strategy was integrated into the proposed method to increase diversity in the search space and avoid getting stuck in local minima. The continuous-valued iHBA method was discretized with a modified sin–cos transfer function for feature selection. Oral cancer images were filtered using the CLAHE method, and after extracting deep features with the ResNet50 architecture, the proposed metaheuristic-based method was used to select discriminative features. Results: The proposed method was first tested for reliability and limitations through repeated runs on problems with different characteristics, such as unimodal and multimodal classical test functions. Then, the method was applied to extract significant features for oral cancer detection using a Histopathological Imaging Database containing 1224 histopathological oral tissue images at 100× and 400× magnification levels from 230 patients. The proposed approach was assessed in terms of accuracy, precision, recall, F1-score, and convergence curves in comparison with various classical feature selection techniques, such as wrapper-based, filter-based, and embedded-based methods, as well as other metaheuristic-based methods. The experimental results demonstrated that the suggested strategy outperformed both traditional feature selection techniques and alternative metaheuristic approaches. Conclusions: The effectiveness of the proposed method in improving diagnostic accuracy was evaluated through comprehensive experimental analyses. The obtained findings show that the proposed iHBA-based feature selection approach can reduce feature dimensionality, eliminate redundant and irrelevant features, and improve the classification performance of oral cancer detection. Therefore, the proposed method provides an effective and competitive computer-aided diagnostic framework for the automated classification of histopathological oral cancer images. Full article

Photoacoustic spectroscopy (PAS) is a highly sensitive and non-destructive technique widely used for trace gas detection; however, the simultaneous quantification of methane (CH₄), ethane (C₂H₆), and ethylene (C₂H₄) remains challenging due to severe spectral cross-interference and non-linear responses across broad concentration ranges. In this work, we propose a high-precision, end-to-end detection framework based on a Deep Kernel Extreme Learning Machine (DKELM) optimized using a Mutation–Chaotic Particle Swarm Optimization (MCPSO) algorithm. To enhance diagnostic information in the photoacoustic signals, a multi-scale wavelet transform based on a db4 wavelet basis with 5-layer decomposition and a Heursure soft threshold strategy is first employed for denoising and enhancing absorption features. To address the hyperparameter sensitivity and local-optimum trapping inherent in deep models, the MCPSO algorithm integrates hybrid chaotic initialization, adaptive mutation probability control, Cauchy-based perturbation, temperature-controlled mutation amplitude, and elite-guided population updating. The proposed MCPSO-DKELM model is evaluated on an expanded dataset of 470 mixed-gas spectra and benchmarked against other frameworks, including the previously reported SVM-CPSO-KELM architecture. The experimental results demonstrate that MCPSO-DKELM achieves stable, segmentation-free quantification across the full dynamic range, with an average detection error below 3.5% and the maximum relative error constrained to under 15%, which represents a substantial improvement over existing approaches. Thus, the combination of deep kernel feature extraction and mutation–chaotic global optimization provides a robust and reliable solution for simultaneous multi-component hydrocarbon gas analysis in complex industrial environments. Full article

In this paper, the Tricomi problem for a second-kind mixed-type equation with a lower-order term is studied in an unbounded domain. The elliptic part of the domain is a vertical half-strip, while the hyperbolic part is bounded by characteristics. Homogeneous Dirichlet conditions are imposed on the walls of the half-strip, gluing conditions are given on the parabolic degeneracy line, and the trace of the desired solution is prescribed on one of the characteristics. The uniqueness of the solution is proved using the extremum principle and the Zaremba–Giraud principle. The existence of the solution is established by Green’s function method: in the elliptic part, Green’s function of the mixed problem is constructed in the form of a rapidly convergent series; in the hyperbolic part, a generalized solution of the Cauchy problem of a special class is used. The functional relations on the degeneracy line lead to a singular integral equation, which is regularized by the Carleman–Vekua method into a Fredholm integral equation of the second kind with a weak singularity. Explicit formulas for the trace of the solution and its normal derivative are obtained. For a specific set of parameters, a numerical visualization of the solution is performed, the gluing conditions are verified, and a physical interpretation of the obtained graphs is given in the context of transonic gas dynamics. The results can be useful for mathematical modeling of flows in Laval nozzles and other problems of mechanics. Full article

To improve the reliability and path quality of three-dimensional UAV path planning in complex mountainous environments, this paper proposes an Adaptive Elite Differential Gold Rush Optimizer (AEDGRO). The main novelty of AEDGRO lies in the coordinated integration of three enhancement mechanisms into the original Gold Rush Optimizer: chaotic good-point initialization for improving initial population coverage, adaptive elite differential mining for strengthening exploitation around promising regions, and stagnation-aware Gaussian–Cauchy mutation for escaping local optima. A UAV path-planning model is constructed by considering path length, altitude fluctuation, trajectory smoothness, terrain collision avoidance, threat-region avoidance, and UAV safety clearance. The experimental results on the IEEE CEC2017 benchmark suite show that AEDGRO obtains the best Friedman average ranking of 1.63, outperforming the original GRO with a ranking of 4.80. In the UAV path-planning experiments, AEDGRO achieves the lowest mean fitness value of 235.69 and the smallest standard deviation of 7.55, indicating better path quality and stronger robustness than the compared algorithms. The generated trajectories are smoother and can effectively avoid mountainous terrain and threat regions. These results demonstrate that AEDGRO has clear advantages in global optimization accuracy, convergence stability, and UAV path-planning applicability. Full article

In this paper, we investigate the generalized integral-type operator acting between the fractional Cauchy transform space and two classical analytic function spaces: the weighted Bloch space and the weighted Dirichlet space. For the operator acting into the weighted Bloch space, we obtain two equivalent exact formulas for its operator norm. Furthermore, an estimate for its essential norm is provided, which leads to a necessary and sufficient condition for compactness. For the operator acting into the weighted Dirichlet space, we derive the exact operator norm and fully characterize its compactness. Full article

To address the imbalance between global exploration and local exploitation in the secretary bird optimization algorithm (SBOA), this paper presents a multi-strategy improved version termed MSISBOA. The proposed approach incorporates optimal Latin hypercube sampling during initialization to achieve a more uniform distribution of initial solutions. In the hunting phase, an adaptive Cauchy mutation factor and a boundary strategy are integrated to refine local search precision. To reduce the risk of stagnation in local optima during later iterations, a triangular walk strategy is utilized for mutation perturbation. Furthermore, the escape phase employs a combined Tent chaotic-Gaussian mutation factor and an elite retention strategy to maintain high-quality solutions while diversifying the population. The performance of MSISBOA was evaluated using the benchmark suites released for the IEEE Congress on Evolutionary Computation (CEC), including CEC-2017 and CEC-2022, against nine other swarm intelligence algorithms, with statistical results showing that MSISBOA achieved the highest average rank. Additionally, the algorithm was applied to 18 engineering optimization problems to assess its capability in solving practical constrained tasks. Experimental results indicate that MSISBOA provides competitive convergence characteristics and solution quality across the tested scenarios. Full article

Complex engineering optimization problems often exhibit high-dimensional, multi-constraint, and nonlinear characteristics. Traditional deterministic optimization methods rely on gradient information and have limited optimization ranges, making it difficult to meet the requirements of efficient and accurate solutions. Intelligent optimization algorithms have become the core means of solving such problems. Aiming at the limitations of the standard artificial lemming algorithm (ALA), such as insufficient population diversity, premature convergence, weak local exploitation ability, and slow convergence speed, which make it difficult to meet the requirements of solving complex engineering optimization problems, this paper proposes a reverse mutation for optimization learning artificial lemming algorithm (RMALA). Based on the ALA algorithm, the algorithm integrates three strategies: Cauchy mutation, the improved salp swarm algorithm (ISSA), and reverse mutation for optimization learning. The Cauchy mutation is used to maintain population diversity and avoid premature convergence of the algorithm. The improved salp swarm algorithm enhances the local exploitation ability of the algorithm and improves the optimization accuracy. Reverse mutation for optimization learning guides the population toward the global optimal solution region and accelerates the convergence speed. The significant experimental results show that in the CEC2017 and CEC2022 standard test sets, as well as the three classic engineering constrained optimization problems of welded beams, cantilever beams, and pressure vessels, RMALA’s optimization accuracy is improved by more than 30% compared to the original ALA, and its convergence speed is improved by more than 25%. Its stability and robustness are better than those of five new swarm intelligence algorithms proposed in recent years. It can efficiently solve complex high-dimensional, nonlinear constrained optimization problems and has high significant engineering application value and academic innovation. Full article

As a core crushing equipment in mining, building materials, and related industries, the cone crusher relies heavily on the optimal design and health state prediction of its mantle liner to enhance equipment reliability and reduce maintenance costs. This paper proposes a comprehensive approach integrating dynamic modeling, intelligent optimization, and health prognosis. First, a virtual prototype model is established based on laminated crushing theory and multibody dynamics simulation to analyze the motion and force characteristics of the mantle liner. Second, for the two key parameters—counterweight mass and motor speed—an improved butterfly optimization algorithm (IBOA) incorporating Cauchy mutation and an adaptive weight is proposed to achieve efficient global optimization. Furthermore, vibration signal features are extracted at different wear stages; a comprehensive health indicator curve is constructed by combining PCA dimensionality reduction with adaptive feature fusion (ASFF), and the Weibull degradation model is employed for life extrapolation prediction. Finally, fuzzy C-means (FCM) clustering is applied to autonomously partition the health states. Parameter optimization reduces the standard deviation of the force acting on the mantle liner by approximately 15.4%, markedly improving system operational stability. Health prognosis reveals that the liner enters a faulty state after 785 h, and the health condition is effectively classified into four stages: healthy, good, degraded, and faulty. The results demonstrate that the proposed optimization and health prognosis methods can effectively improve the operational efficiency and reliability of cone crushers, exhibit favorable engineering applicability, and provide a quantitative basis for condition monitoring and maintenance decision-making. Full article

Efficient exploitation of evolutionary knowledge while preserving population diversity remains a central challenge in optimization. Existing knowledge-learning evolutionary algorithms primarily rely on successful experiences, overlooking structural information embedded in failed search attempts. This asymmetric learning limits adaptability and may cause premature convergence in high-dimensional landscapes. To address this issue, a failure-aware bidirectional evolutionary knowledge assimilation framework is developed within the honey badger optimization algorithm. Unsuccessful offspring are treated as negative knowledge carriers and transformed through symmetric adversarial reflection, enabling simultaneous extraction of positive and negative structural information. A time-dependent regulation mechanism dynamically adjusts knowledge assimilation intensity across evolutionary phases to balance exploration and exploitation. In addition, a continuous mutation spectrum transition strategy adaptively integrates Cauchy and Gaussian perturbations, facilitating smooth migration from global exploration to local refinement. Comprehensive experiments conducted on the CEC 2017 benchmark suite across 10, 30, and 50 dimensions validate the proposed framework, establishing a novel failure-aware bidirectional evolutionary learning paradigm for knowledge-driven optimization. The results demonstrate that our method achieves statistically significant and consistent performance improvements over classical baseline algorithms. Furthermore, its robustness and cross-domain adaptability are corroborated through successful application to a real-world constrained engineering problem: welded beam design. Full article

Ketosis is a prevalent metabolic disorder in early-lactation dairy cows, significantly affecting animal health, milk production, and farm profitability. Developing accurate and non-invasive methods for early risk detection is therefore of critical importance. In this study, a hybrid optimization framework integrating an Improved Whale Migration Algorithm (IWMA) with a Light Gradient Boosting Machine (LightGBM) is proposed to predict ketosis risk based on the milk fat-to-protein ratio (F/P) using Dairy Herd Improvement (DHI) records. The proposed IWMA enhances optimization performance through cubic chaotic initialization, elite opposition-based learning, and a Cauchy–Gaussian hybrid mutation strategy, enabling improved global exploration and convergence stability. A dataset comprising 25,155 DHI records collected from multiple commercial dairy farms over seven months was used for model development and evaluation. Experimental results demonstrate that the IWMA–LightGBM model achieves a classification accuracy of 0.8997 and a mean squared error of 0.289, consistently outperforming six benchmark optimization methods. Feature analysis identifies Herd Within Index (WHI), Energy Corrected Milk (ECM), Days in Milk (DIM), Milk Urea Nitrogen, and Foremilk as key predictors associated with metabolic risk. Overall, the proposed approach provides a robust and effective non-invasive solution for early-stage metabolic risk screening at the herd level, offering practical value for precision dairy management. It should be noted that the model is intended for risk assessment rather than clinical diagnosis of ketosis. Full article