Search Results (34)

High-dimensional prediction problems with complex non-linear feature interactions present significant algorithmic challenges in machine learning, particularly when dealing with imbalanced datasets and multicollinearity issues. This study proposes an innovative Shapley Additive Explanations (SHAP)-enhanced machine learning framework that integrates SHAP with advanced ensemble methods for interpretable financialization prediction. The methodology simultaneously addresses high-dimensional feature selection using 40 independent variables (19 CSR-related and 21 financialization-related), multicollinearity issues, and model interpretability requirements. Using a comprehensive dataset of 25,642 observations from 3776 Chinese A-share companies (2011–2022), we implement nine optimized machine learning algorithms with hyperparameter tuning via the Hippopotamus Optimization algorithm and five-fold cross-validation. XGBoost demonstrates superior performance with 99.34% explained variance, achieving an RMSE of 0.082 and R² of 0.299. SHAP analysis reveals non-linear U-shaped relationships between key predictors and financialization outcomes, with critical thresholds at approximately 10 for CSR_SocR, 1.5 for CSR_S, and 5 for CSR_CV. SOE status, EPU, ownership concentration, firm size, and housing prices emerge as the most influential predictors. Notable shifts in factor importance occur during the COVID-19 pandemic period (2020–2022). This work contributes a scalable, interpretable machine learning architecture for high-dimensional financial prediction problems, with applications in risk assessment, portfolio optimization, and regulatory monitoring systems. Full article

To address the challenges posed by weak early fault signal features, strong noise interference, low diagnostic accuracy, poor reliability when using single information sources, and the limited availability of high-quality samples in practical applications for permanent magnet synchronous motor (PMSM) bearings, this paper proposes an early bearing fault diagnosis method based on Hippopotamus Optimization Variational Mode Decomposition (HO-VMD) and weighted evidence fusion of current–vibration signals. The HO algorithm is employed to optimize the parameters of VMD for adaptive modal decomposition of current and vibration signals, resulting in the generation of intrinsic mode functions (IMFs). These IMFs are then selected and reconstructed based on their kurtosis to suppress noise and harmonic interference. Subsequently, the reconstructed signals are demodulated using the Teager–Kaiser Energy Operator (TKEO), and both time-domain and energy spectrum features are extracted. The reliability of these features is utilized to adaptively weight the basic probability assignment (BPA) functions. Finally, a weighted modified Dempster–Shafer evidence theory (WMDST) is applied to fuse multi-source feature information, enabling an accurate assessment of the PMSM bearing health status. The experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio (SNR) and enables precise diagnosis of early bearing faults even in scenarios with limited sample sizes. Full article

As power grids scale and aging assets edge toward obsolescence, grounding grid corrosion has become a critical vulnerability. Conventional diagnosis must fit high-dimensional electrical data to a physical model, typically yielding a nonlinear under-determined system fraught with computational burden and uncertainty. We propose the Enhanced Biomimetic Hippopotamus Optimization (EBOHO) algorithm, which distills the river-dwelling hippo’s ecological wisdom into three synergistic strategies: a beta-function herd seeding that replicates the genetic diversity of juvenile hippos diffusing through wetlands, an elite–mean cooperative foraging rule that echoes the way dominant bulls steer the herd toward nutrient-rich pastures, and a lens imaging opposition maneuver inspired by moonlit water reflections that spawn mirror candidates to avert premature convergence. Benchmarks on the CEC 2017 suite and four classical design problems show EBOHO’s superior global search, robustness, and convergence speed over numerous state-of-the-art meta-heuristics, including prior hippo variants. An industrial case study on grounding grid corrosion further confirms that EBOHO swiftly resolves the under-determined equations and pinpoints corrosion sites with high precision, underscoring its promise as a nature-inspired diagnostic engine for aging power system infrastructure. Full article

When traditional HVAC (heating, ventilation, and air-conditioning) systems are in operation, they often run according to the designed operating conditions. In fact, they operate under part-load conditions for more than 90% of the time, resulting in energy waste. Therefore, studying the optimization and regulation of their operating conditions during operation is necessary. Given that the control set point for cooling tower outlet water temperature differentially impacts chiller and cooling tower energy consumption during system operation, optimization of this parameter becomes essential. Therefore, this study focuses on optimizing the cooling tower outlet water temperature control point in central air-conditioning systems. We propose the Hippopotamus Optimization Algorithm (HOA), a novel population-based approach, to optimize cooling tower outlet water temperature control points for energy consumption minimization. This optimization is achieved through a coupled computational methodology integrating building envelope dynamics with central air-conditioning system performance. The energy consumption of the cooling tower was analyzed for varying outlet water temperature set points, and the differences between three control strategies were compared. The results showed that the HOA strategy successfully identifies an optimized control set point, achieving the lowest combined energy consumption for both the chiller and cooling tower. The performance of HOA is better compared to other algorithms in the optimization process. The optimized fitness value is minimal, and the function converges after five iterations and completes the optimization in a single time step when run in MATLAB in only 1.96 s. Compared to conventional non-optimized operating conditions, the HOA strategy yields significant energy savings: peak daily savings reach 4.5%, with an average total daily energy reduction of 3.2%. In conclusion, this paper takes full account of the mutual coupling between the building and the air-conditioning system, providing a feasible method for the simulation and optimization of the building air-conditioning system. Full article

Bridge dynamic load test signals are readily contaminated by environmental noise. This reduces the accuracy of bridge structure state assessment. To address this issue, this research proposes a denoising method that combines the hippopotamus optimization algorithm (HOA), variational mode decomposition (VMD), and singular spectrum analysis (SSA). The methodology follows three key phases: First, the HOA optimizes the critical parameters of VMD. Then, the optimized VMD decomposes raw signals into several intrinsic mode components (IMFs). The IMFs below the threshold are removed by calculating the correlation coefficient between each IMF and the original signal. Finally, SSA is introduced for secondary denoising, which helps reorganize bridge signals and eliminate local low-frequency oscillations. The simulation results show that compared with other methods, the root mean square error (RMSE), signal-to-noise ratio (SNR), mean square error (MSE), and mean absolute error (MAE) of the denoised signals achieve on average 16.22% reduction, 2.51% improvement, 62.02% diminution, and 43.74% decrease, respectively, across varying noise levels. Practical validation reveals superior performance metrics: a mean 12.81% lower normalization Shannon entropy ratio (NSER) and a mean 8.44% higher noise suppression ratio (NSR) compared to other techniques. This comprehensive approach effectively addresses noise components in bridge dynamic load test signals. Full article

This study presents a comparative analysis of various optimization algorithms—Differential Evolution (DE), Particle Swarm Optimization (PSO), Arithmetic Optimization Algorithm (AOA), and Hippopotamus Optimization Algorithm (HOA)—for parameter identification in photovoltaic (PV) models. By utilizing RTC France solar cell data, we demonstrate that accurate parameter estimation is crucial for enhancing the efficiency of PV systems, ultimately supporting sustainable energy solutions. Our results indicate that DE achieves the lowest root mean square error (RMSE) of 0.0001 for the double-diode model (DDM), outperforming other methods in terms of accuracy and convergence speed. Both the HOA and PSO also show competitive RMSE values, underscoring their effectiveness in optimizing parameters for PV models. This research not only contributes to improved PV model precision but also aids in the broader effort to advance renewable energy technologies, thereby fostering a more sustainable future. Full article

With the rapid increase in electric vehicle (EV) ownership, the uncertainty of EV charging demand has become a significant concern, especially in distributed photovoltaic (PV) power distribution networks (DNs) with high penetration rates. This growing demand presents challenges in meeting the needs of EV owners and grid charging/discharging stations (GCDSs), jeopardizing the stability, efficiency, reliability, and sustainability of the DNs. To address these challenges, this study introduces innovative models, the anchoring effect, and regret theory for EV demand response (DR) decision-making, focusing on dual-sided demand management for GCDSs and EVs. The proposed model leverages the light spectrum optimizer–convolutional neural network to predict PV output and utilizes Monte Carlo simulation to estimate EV charging load, ensuring precise PV output prediction and effective EV distribution. To optimize DR decisions for EVs, this study employs time-of-use guidance optimization through a logistic–sine hybrid chaotic–hippopotamus optimizer (LSC-HO). By integrating the anchoring effect and regret theory model with LSC-HO, this approach enhances satisfaction levels for GCDSs by balancing DR, enhancing voltage quality within the DNs. Simulations on a modified IEEE-33 system confirm the efficacy of the proposed approach, validating the efficiency of the optimal scheduling methods and enhancing the stable operation, efficiency, reliability, and sustainability of the DNs. Full article

Due to the complex operating environment of valves, when a fault occurs inside a valve, the vibration signal generated by the fault is easily affected by the environmental noise, making the extraction of fault features difficult. To address this problem, this paper proposes a feature extraction method based on the combination of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Fuzzy Entropy (FN). Due to the slow convergence speed and the tendency to fall into local optimal solutions of the Hippopotamus Optimization Algorithm (HO), an improved Hippopotamus Optimization (IHO) algorithm-optimized Support Vector Machine (SVM) model for valve leakage diagnosis is introduced to further enhance the accuracy of valve leakage diagnosis. The improved Hippopotamus Optimization algorithm initializes the hippopotamus population with Tent chaotic mapping, designs an adaptive weight factor, and incorporates adaptive variation perturbation. Moreover, the performance of IHO was proven to be optimal compared to HO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Sparrow Search Algorithm (SSA) by calculating twelve test functions. Subsequently, the IHO-SVM classification model was established and applied to valve leakage diagnosis. The prediction effects of the seven models, IHO-SVM. HO-SVM, PSO-SVM, GWO-SVM, WOA-SVM, SSA-SVM, and SVM were compared and analyzed with actual data. As a result, the comparison indicated that IHO-SVM has desirable robustness and generalization, which successfully improves the classification efficiency and the recognition rate in fault diagnosis. Full article

This study integrates the Backpropagation (BP) Neural Network with several optimization algorithms, namely Hippopotamus Optimization (HO), Parrot Optimization (PO), Osprey Optimization Algorithm (OOA), and Goose Optimization (GO), to develop four predictive models for the adhesive strength of heat-treated wood: HO-BP, PO-BP, OOA-BP, and GO-BP. These models were used to predict the adhesive strength of the wood that was heat-treated under multiple variables such as treatment temperature, time, feed rate, cutting speed, and abrasive particle size. The efficacy of the BP neural network models was assessed utilizing the coefficient of determination (R²), error rate, and CEC test dataset. The outcomes demonstrate that, relative to the other algorithms, the Hippopotamus Optimization (HO) method shows better search efficacy and convergence velocity. Furthermore, XGBoost was used to statistically evaluate and rank input variables, revealing that cutting speed (m/s) and treatment time (hours) had the most significant impact on model predictions. Taken together, these four predictive models demonstrated effective applicability in assessing adhesive strength under various processing conditions in practical experiments. The MAE, RMSE, MAPE, and R² values of the HO-BP model reached 0.0822, 0.1024, 1.1317, and 0.9358, respectively, demonstrating superior predictive accuracy compared to other models. These findings support industrial process optimization for enhanced wood utilization. Full article

The hippopotamus optimization algorithm (HO) is a novel metaheuristic algorithm that solves optimization problems by simulating the behavior of hippopotamuses. However, the traditional HO algorithm may encounter performance degradation and fall into local optima when dealing with complex global optimization and engineering design problems. In order to solve these problems, this paper proposes a modified hippopotamus optimization algorithm (MHO) to enhance the convergence speed and solution accuracy of the HO algorithm by introducing a sine chaotic map to initialize the population, changing the convergence factor in the growth mechanism, and incorporating the small-hole imaging reverse learning strategy. The MHO algorithm is tested on 23 benchmark functions and successfully solves three engineering design problems. According to the experimental data, the MHO algorithm obtains optimal performance on 13 of these functions and three design problems, exits the local optimum faster, and has better ordering and stability than the other nine metaheuristics. This study proposes the MHO algorithm, which offers fresh insights into practical engineering problems and parameter optimization. Full article

This paper addresses the challenge of enhancing power quality in a standalone microgrid powered by wind and battery systems. Fluctuations in wind power generation and unpredictable electricity demand significantly impact power quality. To mitigate these issues, a control strategy utilizing Super Twisting Sliding Mode (STSM) controllers tuned by the Hippopotamus Optimization Algorithm (HOA) is proposed. The HOA algorithm efficiently determines optimal STSM controller parameters, leading to improved system performance and stability. A comparative study was conducted against PI, Fuzzy Logic controllers, and other metaheuristic optimization algorithms (PSO, GWO, WOA). Simulation results, obtained using MATLAB/Simulink, demonstrate the superior performance of the proposed methodology. Specifically, during a simulated abrupt load change, the system exhibited rapid recovery with frequency reaching equilibrium, significantly faster than PI and Fuzzy Logic controllers. Moreover, the DC link voltage remained stable with fluctuations of only 2%, while the three-phase RMS voltages at the Point of Load Bus (PLB) maintained balanced and stable values. These results confirm the enhanced power quality and robust operation achieved with the proposed HOA-tuned STSM control strategy, outperforming other tested methods. The methodology effectively manages both the energy management system and improves power quality in standalone wind and battery-powered microgrids. Full article

The rapid growth of the digital economy has significantly enhanced the convenience of information transmission while reducing its costs. As a result, the participation in social networks (SNs) has surged, intensifying the mutual influence among network participants. To support objective decision-making and gather public opinions within SNs, the research on the consensus-reaching process (CRP) has become increasingly important. However, CRP faces three key challenges: first, as the number of decision-makers (DMs) increases, the efficiency of reaching consensus declines; second, minority opinions and non-cooperative behaviors affect decision outcomes; and third, the relationships among DMs complicate opinion adjustments. To address these challenges, this paper introduces an enhanced CRP mechanism. Initially, the hippopotamus optimization algorithm (HOA) is applied to update the initial community division in Leiden clustering, which accelerates the clustering process, collectively referred to as HOAL. Subsequently, a two-stage opinion adjustment method is proposed, combining minority opinion handling (MOH), non-cooperative behavior management, and dual-fine tuning (DFT) management, collectively referred to as DFT-MOH. Moreover, trust relationships between DMs are directly integrated into both the clustering and opinion management processes, resulting in the HOAL-DFT-MOH framework. The proposed method proceeds by three main steps: (1) First, the HOAL clusters DMs. (2) Then, in the initial CRP stage, DFT manages subgroup opinions with a weighted average to synthesize subgroup perspectives; and in the second stage, MOH addresses minority opinions, a non-cooperative mechanism manages uncooperative behaviors, and DFT is used when negative behaviors are absent. (3) Third, the prospect-regret theory is applied to rank decision alternatives. Finally, the approach is applied to case analyses across three different scenarios, while comparative experiments with other clustering and CRP methods highlight its superior performance. Full article

This research presents an in-depth analysis of large mammal remains first discovered in 1932 in the archaeological area of ancient Rome, central Italy, during the work for the opening of Via dell’Impero (VFI). This work describes the faunal assemblage, its current preservation status, and uses tephrochronology to assess its age. Additionally, it provides paleoecological insights into the evolution of the mammalian fauna in Latium, central Italy, from MIS 13 to MIS 7. Analysis of the fossils updates the identification previously proposed by De Angelis d’Ossat, confirming the presence of Palaeoloxodon antiquus, Cervus elaphus, and Bos primigenius. However, in contrast to the previous author, the hippopotamus remains are assigned to Hippopotamus cf. antiquus, and a second deer is identified as Dama sp.. Furthermore, gnawing marks on the hippopotamus femur suggest the presence of a middle-sized carnivore. Tephrochronological investigation was conducted on pumice retrieved from the VFI fossiliferous layer and ash extracted from sediments adhering to the fossil surfaces. The major element composition of the glass from all pumice/ash samples shows a strong affinity with the Vico β unit, allowing correlation with the Fucino record and constraining the deposition of the VFI fossiliferous level between <406.5 ± 1.3 ka and >405.7 + 1.5/−1.6 ka. Radiometric dating is particularly useful for large mammal faunas of MIS 11-MIS 7, a period lacking significant faunal renewals, as Latium mammalian faunas are often dominated by species (elephants, red deer, aurochs) with broad chronological ranges. Full article

One of the most important issues that can significantly affect the electric power network’s ability to operate sustainably is the optimal power flow (OPF) problem. It involves reaching the most efficient operating conditions for the electrical networks while maintaining reliability and systems constraints. Solving the OPF problem in transmission networks lowers three critical expenses: operation costs, transmission losses, and voltage drops. The OPF is characterized by the nonlinearity and nonconvexity behavior due to the power flow equations, which define the relationship between power generation, load demand, and network component physical constraints. The solution space for OPF is massive and multimodal, making optimization a challenging concern that calls for advanced mathematics and computational methods. This paper introduces an innovative metaheuristic algorithm, the Egyptian Stray Dog Optimization (ESDO), inspired by the behavior of Egyptian stray dogs and used for solving both single and multi-objective optimal power flow problems concerning the transmission networks. The proposed technique is compared with the particle swarm optimization (PSO), multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawk optimization (HHO) and hippopotamus optimization (HO) algorithms through MATLAB simulations by applying them to the IEEE 30-bus system under various operational circumstances. The results obtained indicate that, in comparison to other used algorithms, the suggested technique gives a significantly enhanced performance in solving the OPF problem. Full article

The rise in photovoltaic (PV) energy utilization has led to increased research on its functioning, as its accurate modeling is crucial for system simulations. However, capturing nonlinear current–voltage traits is challenging due to limited data from cells’ datasheets. This paper presents a novel enhanced version of the Brown-Bear Optimization Algorithm (EBOA) for determining the ideal parameters for the circuit model. The presented EBOA incorporates several modifications aimed at improving its searching capabilities. It combines Fractional-order Chaos maps (FC maps), which support the BOA settings to be adjusted in an adaptive manner. Additionally, it integrates key mechanisms from the Hippopotamus Optimization (HO) to strengthen the algorithm’s exploitation potential by leveraging surrounding knowledge for more effective position updates while also improving the balance between global and local search processes. The EBOA was subjected to extensive mathematical validation through the application of benchmark functions to rigorously assess its performance. Also, PV parameter estimation was achieved by combining the EBOA with a Newton–Raphson approach. Numerous module and cell varieties, including RTC France, STP6-120/36, and Photowatt-PWP201, were assessed using double-diode and single-diode PV models. The higher performance of the EBOA was shown by a statistical comparison with many well-known metaheuristic techniques. To illustrate this, the root mean-squared error values achieved by our scheme using (SDM, DDM) for RTC France, STP6-120/36, and PWP201 are as follows: (8.183847 × 10⁻⁴, 7.478488 × 10⁻⁴), (1.430320 × 10⁻², 1.427010 × 10⁻²), and (2.220075 × 10⁻³, 2.061273 × 10⁻³), respectively. The experimental results show that the EBOA works better than alternative techniques in terms of accuracy, consistency, and convergence. Full article