Search Results (36)

In order to solve the problem of false alarms and missed alarms in pipeline monitoring caused by a large amount of noise in the negative pressure wave signal collected by pressure sensors, a new pressure signal denoising method based on the black-winged kite algorithm (BWK) is proposed. First, the variational mode decomposition (VMD) parameters are optimized through BWK. Next, the effective modal components are screened by sample entropy, and the secondary noise reduction of the signal is carried out by using the wavelet thresholding (WT). Finally, the signal is reconstructed to achieve noise reduction. Simulation experiments show that, compared with WT and empirical mode decomposition (EMD), the method proposed in this paper can achieve the best noise reduction effect under both high and low signal-to-noise ratio (SNR) conditions. The method proposed in the paper can achieve the highest SNR of 14.2280 dB, compared to WT’s SNR of 12.6458 dB and EMD’s SNR of 5.5292 dB. To further validate the performance of the algorithm, an experimental platform for simulating pipeline leaks is built. Compared with WT and EMD, the method proposed in this paper also shows the best noise reduction effect. This method provides a high-precision and adaptive solution for leak detection in urban water supply pipelines and has strong engineering application value. Full article

To address the deficiencies in global search capability and population diversity decline of the black-winged kite algorithm (BKA), this paper proposes an enhanced black-winged kite algorithm integrating opposition-based learning and quasi-Newton strategy (OQBKA). The algorithm introduces a mirror imaging strategy based on convex lens imaging (MOBL) during the migration phase to enhance the population’s spatial distribution and assist individuals in escaping local optima. In later iterations, it incorporates the quasi-Newton method to enhance local optimization precision and convergence performance. Ablation studies on the CEC2017 benchmark set confirm the strong complementarity between the two integrated strategies, with OQBKA achieving an average ranking of 1.34 across all 29 test functions. Comparative experiments on the CEC2022 benchmark suite further verify its superior exploration–exploitation balance and optimization accuracy: under 10- and 20-dimensional settings, OQBKA attains the best average rankings of 2.5 and 2.17 across all 12 test functions, outperforming ten state-of-the-art metaheuristic algorithms. Moreover, evaluations on three constrained engineering design problems, including step-cone pulley optimization, corrugated bulkhead design, and reactor network design, demonstrate the practicality and robustness of the proposed approach in generating feasible solutions under complex constraints. Full article

Accurate modeling and parameter estimation of photovoltaic (PV) systems are vital for advancing energy sustainability and achieving global decarbonization goals. Reliable PV models enable better integration of solar resources into smart grids, improve system efficiency, and reduce maintenance costs. This aligns with the vision of sustainable energy systems that combine intelligent optimization with environmental responsibility. The recently introduced Black-Winged Kite Algorithm (BWKA) has shown promise by emulating the predatory and migratory behaviors of black-winged kites; however, it still suffers from issues of slow convergence, limited population diversity, and imbalance between exploration and exploitation. To address these limitations, this paper proposes an Improved Black-Winged Kite Algorithm (IBWKA) that integrates two novel strategies: (i) a Soft-Rime Search (SRS) modulation in the attacking phase, which introduces a smoothly decaying nonlinear factor to adaptively balance global exploration and local exploitation, and (ii) a Quadratic Interpolation (QI) refinement mechanism, applied to a subset of elite individuals, that accelerates local search by fitting a parabola through representative candidate solutions and guiding the search toward promising minima. These dual enhancements reinforce both global diversity and local accuracy, preventing premature convergence and improving convergence speed. The effectiveness of the proposed IBWKA in contrast to the standard BWKA is validated through a comprehensive experimental study for accurate parameter identification of PV models, including single-, double-, and three-diode equivalents, using standard datasets (RTC France and STM6_40_36). The findings show that IBWKA delivers higher accuracy and faster convergence than existing methods, with its improvements confirmed through statistical analysis. Compared to BWKA and others, it proves to be more robust, reliable, and consistent. By combining adaptive exploration, strong diversity maintenance, and refined local search, IBWKA emerges as a versatile optimization tool. Full article

Accurate environmental prediction is crucial for ecological monitoring and disaster early warnings, but it remains challenging due to the spatiotemporal complexity of dynamic wireless sensor networks (WSNs). To this end, we propose a novel hybrid model that integrates a convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and a black-winged kite algorithm (BKA). The CNN first extracts spatial features from multi-node sensor data to capture local environmental patterns. Subsequently, the BKA optimizes key CNN hyperparameters (learning rate, hidden layers, and regularization coefficients) to enhance the robustness of feature representation to noise and missing data. Subsequently, the BiLSTM processes the optimization features to model bidirectional long-term time dependencies (e.g., circadian rhythms, seasonal trends) to achieve accurate environmental predictions. Evaluation of the BKA-optimized CNN-BiLSTM model shows that our framework reduces prediction error by 19.3% to 32.7% compared to other models, achieving 89.4% accuracy in predicting extreme weather events. The synergy between BKA-driven CNN optimization and BiLSTM temporal dynamics modeling significantly improves the reliability of environmental prediction in resource-constrained sensor networks. Full article

This paper evaluates the efficacy of the Black-Winged Kite Algorithm (BKA) for parameter estimation in single-, double-, and triple-diode photovoltaic (PV) models. This study targets key electrical parameters, including photocurrent, reverse saturation current, series, and shunt resistances, and diode ideality factor(s) using experimental I-V data from an RTC France silicon cell. Performance is assessed using the root mean square error (RMSE) and convergence behavior and benchmarked against established metaheuristics including the Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), and Ant Lion Optimizer (ALO). The results show that BKA achieves competitive RMSE values with stable convergence for the investigated dataset. BKA employs coupled exploration and exploitation updates inspired by hunting and migration behaviors, and its limited number of control parameters supports straightforward deployment in nonlinear PV identification tasks. The results support BKA as a viable optimization option for PV model fitting in this setting, while also reflecting the typical trade-offs between search diversity and computational effort inherent to population-based methods. Full article

Accurate offshore wind speed forecasting is crucial for ensuring stable energy production and safe offshore operations. However, the strong nonlinearity, non-stationarity, and chaotic behavior of offshore wind speed series make precise prediction extremely difficult. To overcome these difficulties, a two-stage synergistic prediction framework is proposed. In the first stage, a multi-strategy Black-winged Kite Algorithm (MBKA) is designed, incorporating quantum population initialization, improved migration behavior, and oppositional–mutual learning to reinforce global optimization performance under complex coastal conditions. On this basis, an entropy-driven adaptive Variational Mode Decomposition (VMD) method is implemented, where MBKA optimizes decomposition parameters using envelope entropy as the objective function, thereby improving decomposition robustness and mitigating parameter sensitivity. In the second stage, the denoised intrinsic mode functions are used to train an adaptive Neuro-Fuzzy Inference System (ANFIS), whose membership function parameters are optimized by MBKA to enhance nonlinear modeling capability and prediction generalization. Finally, the proposed framework is evaluated using offshore wind speed data from two coastal regions in Shanghai and Fujian, China. Experimental comparisons with multiple state-of-the-art models demonstrate that the MBKA–VMD–ANFIS framework yields notable performance improvements, reducing RMSE by 57.14% and 30.68% for the Fujian and Shanghai datasets, respectively. These results confirm the effectiveness of the proposed method in delivering superior accuracy and robustness for offshore wind speed forecasting. Full article

With the proliferation of social media platforms, misinformation has evolved toward more diverse modalities and complex cross-semantic correlations. Accurately detecting such content, particularly under conditions of semantic inconsistency and uneven modality dependency, remains a critical challenge. To address this issue, we propose a multimodal semantic representation framework named IBKA-MSM, which integrates swarm-intelligence-based optimization with deep neural modeling. The framework first employs an Improved Black-Winged Kite Algorithm (IBKA) for discriminative feature selection, incorporating adaptive step-size control, an elite-memory mechanism enhanced by opposition perturbation, Gaussian-based local exploitation, and population diversity regulation through reinitialization. In addition, a Modality-Generated Loop Verification (MGLV) mechanism is designed to enhance semantic alignment, and a Semantic Confidence Matrix with Modality-Coupled Interaction (SCM-MCI) is introduced to achieve adaptive multimodal fusion. Experimental results demonstrate that IBKA-MSM achieves an accuracy of 95.80%, outperforming mainstream hybrid models. The F1 score is improved by approximately 2.8% compared to PSO and by 1.6% compared to BKA, validating the robustness and strong capability of the proposed framework in maintaining multimodal semantic consistency for fake news detection. Full article

Mechanical fault samples of disconnectors are scarce, the fault types vary, and the self-evidence is weak, which leads to a lack of perfect fault diagnosis methods, and hidden defects cannot be found in time. To solve this problem, a mechanical fault diagnosis method for disconnectors based on a parallel dual-channel feature fusion model is proposed. Firstly, the optimal parameters for variational mode decomposition (VMD) are obtained using the black-winged kite algorithm (BKA). After the signal decomposition, the kurtosis values of each intrinsic mode function (IMF) are calculated, screened, and reconstructed. The reconstructed signal is input into the gated recurrent unit (GRU) to capture its time-series characteristics. Then, the vibration signal is generated by the recurrence plot (RP) to generate the atlas set and input into the vision Transformer (ViT) to extract its spatial characteristics. Finally, the time-series and spatial characteristics are fused, the multi-head self-attention mechanism is used for training, and softmax is used for fault classification. The measured data results show that the diagnostic accuracy of the model for mechanical fault types reaches 97.9%, which is 3.2%, 4.3%, 1.0%, 2.4%, 2.9%, 1.8%, 2.1%, 9%, and 7.5% higher than the other nine models numbered #2–#10, respectively, verifying its effectiveness and adaptability. Full article

The nonlocality (capable of associating target dynamics across multiple time moments) and memory properties (able to retain historical trajectories) of fractional calculus serve as the core theoretical approach to resolving the “dynamic information association deficiency” in UAV mobile target search. This paper proposes the Fractional-order Black-winged Kite Algorithm (FOBKA), which transforms the search problem into an adaptability function optimization model aimed at “maximizing target capture probability” based on Bayesian theory. Addressing the limitations of the standard Black-winged Kite Algorithm (BKA), the study incorporates fractional calculus theory for enhancement: A fractional-order operator is embedded in the migration behavior phase, leveraging the memory advantage of fractional-orders to precisely capture the temporal span, spatial position, and velocity evolution of targets, thereby enhancing global detection capability and convergence accuracy. Simultaneously, population individuals are initialized using motion-encoding, and the attack behavior phase combines alternating updates with a Lévy flight mechanism to balance local exploration and global search performance. To validate FOBKA’s superiority, comparative experiments were conducted against eight newly proposed meta-heuristic algorithms across six distinct test scenarios. Experimental data demonstrate that FOBKA significantly outperforms the comparison algorithms in convergence accuracy, operational robustness, and target capture probability. Full article

In the field of industrial fault detection, accurate and timely fault identification is crucial for ensuring production safety and efficiency. Effective feature selection (FS) methods can significantly enhance detection performance in this process. However, the recently proposed Black-winged Kite Algorithm (BKA) tends to suffer from premature convergence and local optima when handling high-dimensional feature spaces. To address these limitations, this paper proposes an improved Black-winged Kite Algorithm (IBKA). This algorithm integrates two novel enhancement mechanisms: First, the Stagnation-Triggered Diversification Mechanism monitors the algorithm’s convergence state and applies mild perturbations to the worst-performing individuals upon detecting stagnation, effectively preventing traps in local optima. Second, the Adaptive Weak Guidance Mechanism employs a conditional elite guidance strategy during the late optimization phase to provide subtle directional guidance to underperforming individuals, thereby improving convergence efficiency. We comprehensively evaluated the proposed IBKA across 26 benchmark functions. Results demonstrate superior performance in solution quality, convergence speed, and robustness compared to the original BKA and other advanced meta-heuristics. Furthermore, fault detection applications on public datasets validate the practical applicability of the binary version of the IBKA (bIBKA), showcasing significant improvements in detection accuracy and reliability. Experimental results confirm that these enhancement mechanisms effectively balance exploration and exploitation capabilities while preserving algorithmic simplicity and computational efficiency. Full article

Accurate prediction of ship motion attitude remains a significant challenge due to the inherent non-stationarity and strong stochasticity of marine environmental conditions. To address this issue, this study proposes FMD-IBKA-BTGN, a hybrid model combining Feature Mode Decomposition (FMD), Improved Black-winged Kite Algorithm (IBKA), and a Bidirectional Temporal Convolutional Network with Gated Recurrent Unit (BTGN). First, FMD decomposes motion signals into intrinsic modes. Subsequently, IBKA—enhanced with chaotic mapping and Lévy flights—optimizes BTGN hyperparameters for global search efficiency. Finally, predictions from all components are ensembled for final output. Experiments on a 240 m vessel in Sea State 4 show our model outperforms six models, reducing MAPE by 20.38%, RMSE by 7.4%, MAE by 4.2%, and MSE by 0.97% versus LSTM. The model enhances both prediction accuracy and generalization. Full article

This study proposes an improved Black-winged Kite Algorithm (IBKA) for the parameter optimization of the Model Predictive Control (MPC) controller in Autonomous Underwater Vehicles (AUVs). To tackle the optimization challenges associated with the weight matrices and prediction horizon in the MPC controller, IBKA innovatively integrates the Lens Opposition-Based Learning (LOBL) strategy with the BKA. Specifically, after the migration behavior of BKA, the LOBL strategy is introduced to generate new individuals, and on this basis, the optimal individual is retained as the leader of the black-winged kite. In the experimental scenarios of AUV heading control and depth tracking, the optimization effect of the IBKA-MPC controller is evaluated. The results indicate that, in the heading control experiment, for the MPC controller optimized by IBKA, the Integral of Absolute Error (IAE) and Integral of Time-weighted Absolute Error (ITAE) of the heading angle decreased by a maximum of 6.29% and 18.24%, respectively, compared with the MPC controller under non-optimized parameters. In the depth tracking experiment, for the MPC controller optimized by IBKA, the IAE and ITAE of the depth decreased by 91.86% and 94.78%, respectively, compared with the MPC controller under non-optimized parameters. Meanwhile, through comparative experiments with four classical optimization algorithms, it is verified that the IBKA with the LOBL strategy introduced has a better optimization effect on the parameters of the MPC controller than classical optimization algorithms. Full article

The Black-winged Kite Algorithm (BKA) is a novel heuristic optimization algorithm proposed in 2024, which has demonstrated superior optimization performance on most CEC benchmark functions and several engineering problems. To further enhance its convergence accuracy and solution quality, this paper proposes a Modified Black-winged Kite Algorithm (MBKA). First, a three-phase attacking strategy is designed to replace the original BKA’s attacking mechanism, thereby enhancing population diversity and improving solution quality. Additionally, a Lévy–Cauchy migration strategy is incorporated to achieve a more effective balance between exploration and exploitation. The effectiveness of MBKA is assessed through extensive experiments on 18 classical benchmark functions, the CEC-2017 and CEC-2022 test suites, and two real-world engineering optimization problems. The results indicate that MBKA consistently outperforms the original BKA and several state-of-the-art algorithms in both convergence accuracy and convergence speed across most test cases. Full article

High energy consumption in Chinese rural residential buildings, caused by rudimentary construction methods and the poor thermal performance of building envelopes, poses a significant challenge to national sustainability and “dual carbon” goals. To address this, this study proposes a comprehensive modeling and analysis framework integrating an improved Bio-inspired Black-winged Kite Optimization Algorithm (IBKA) with Support Vector Regression (SVR). Firstly, to address the limitations of the original B-inspired BKA, such as premature convergence and low efficiency, the proposed IBKA incorporates diversification strategies, global information exchange, stochastic behavior selection, and an NGO-based random operator to enhance exploration and convergence. The improved algorithm is benchmarked against BKA and six other optimization methods. An orthogonal experimental design was employed to generate a dataset by systematically sampling combinations of influencing factors. Subsequently, the IBKA-SVR model was developed for energy consumption prediction and analysis. The model’s predictive accuracy and stability were validated by benchmarking it against six competing models, including GA-SVR, PSO-SVR, and the baseline SVR and so forth. Finally, to elucidate the model’s internal decision-making mechanism, the SHAP (SHapley Additive exPlanations) interpretability framework was employed to quantify the independent and interactive effects of each influencing factor on energy consumption. The results indicate that: (1) The IBKA demonstrates superior convergence accuracy and global search performance compared with BKA and other algorithms. (2) The proposed IBKA-SVR model exhibits exceptional predictive accuracy. Relative to the baseline SVR, the model reduces key error metrics by 37–40% and improves the R² to 0.9792. Furthermore, in a comparative analysis against models tuned by other metaheuristic algorithms such as GA and PSO, the IBKA-SVR consistently maintained optimal performance. (3) The SHAP analysis reveals a clear hierarchy in the impact of the design features. The Insulation Thickness in Outer Wall and Insulation Thickness in Roof Covering are the dominant factors, followed by the Window-wall Ratios of various orientations and the Sun space Depth. Key features predominantly exhibit a negative impact, and a significant non-linear relationship exists between the dominant factors (e.g., insulation layers) and the predicted values. (4) Interaction analysis reveals a distinct hierarchy of interaction strengths among the building design variables. Strong synergistic effects are observed among the Sun space Depth, Insulation Thickness in Roof Covering, and the Window-wall Ratios in the East, West, and North. In contrast, the interaction effects between the Window-wall Ratio in the South and other variables are generally weak, indicating that its influence is approximately independent and linear. Therefore, the proposed bio-inspired framework, integrating the improved IBKA with SVR, effectively predicts and analyzes residential building energy consumption, thereby providing a robust decision-support tool for the data-driven optimization of building design and retrofitting strategies to advance energy efficiency and sustainability in rural housing. Full article

Accurate prediction of component content in the rare-earth extraction and separation process is crucial for control system design, product quality control, and optimization of energy consumption. To improve prediction accuracy and modeling efficiency, this paper proposes a model for predicting component content based on an Improved Black-winged Kite Algorithm-Optimized Stochastic Configuration Network (IBKA-SCN). First, we develop an Improved Black-winged Kite Algorithm (IBKA), incorporating good point set initialization and Lévy random-walk strategies to enhance global optimization capability. Theoretical convergence analysis is provided to ensure the stability and effectiveness of the algorithm. Second, to address the issue that constraint parameters and weight-scaling factors in Stochastic Configuration Network (SCN) rely on manual experience and struggle to balance accuracy and efficiency, IBKA is employed to adaptively search for the optimal hyperparameter combination. The applicability of IBKA-SCN is corroborated through four real-world regression tasks. Finally, the effectiveness of the proposed method is validated through an engineering case study on predicting component content. The results show that IBKA-SCN significantly outperforms existing mainstream methods in both prediction accuracy and modeling speed. Full article