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Enhanced hippopotamus optimization algorithm and artificial neural network for mechanical component design

MATERIALS TESTING 2025年第4期67卷 655-662页

作者： Mehta, Pranav Sait, Sadiq M. Yildiz, Betul Sultan Yildiz, Ali Riza Uludag Univ Bursa Uludag Univ Dept Mech Engn Bursa Turkiye Dharmsinh Desai Univ Dept Mech Engn Nadiad Gujarat India King Fahd Univ Petr & Minerals Dhahran Saudi Arabia Bursa Uludag Univ Dept Mech Engn Bursa Turkiye

Metaheuristics have evolved as a strong family of optimization algorithms capable of handling complicated real-world problems that are frequently non-linear, non-convex, and multidimensional in character. These algorithms efficiently explore and take advantage of search areas by imitating natural processes. In addition to introducing a unique modified hippopotamus optimization algorithm (MHOA) in conjunction with artificial neural networks (ANN), this research examines the most recent developments in metaheuristics. By utilizing ANN's adaptive learning processes, MHOA improves on the original hippopotamus optimization algorithm (HOA) in terms of convergence and solution quality. The study uses MHOA to solve a number of engineering design optimization issues, such as gearbox weight reduction, robot gripper design, structural optimization, and piston lever design. When compared to more conventional algorithms, MHOA performs better in terms of accuracy, robustness, and convergence time.

关键词： robot gripper vehicle spring design hippopotamus optimization algorithm real-world engineering applications starfish optimizer ship rescue optimizer

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Application of the hippopotamus optimization algorithm for distribution network reconfiguration with distributed generation considering different load models for enhancement of power system performance

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ELECTRICAL ENGINEERING 2025年第4期107卷 3909-3946页

作者： Maurya, Priyanka Tiwari, Prabhakar Pratap, Arvind Madan Mohan Malaviya Univ Technol Dept Elect Engn Gorakhpur India

This paper presents an innovative approach to enhance the efficiency of radial distribution networks by optimizing the placement of distributed generation (DG) units and network reconfiguration simultaneously while considering voltage-dependent load models, including constant power, constant current, constant impedance, and composite load models. This study utilizes the hippopotamus optimization algorithm (HOA), a novel approach inspired by the unique behaviors of hippopotamuses, for optimal DG planning and network reconfiguration. By mimicking the hippos' strategic positioning, defense mechanisms, and evasion techniques, HOA is used to optimize the weighted sum of multiple objective functions, including active and reactive power losses and bus voltage deviation. Additionally, the study analyzes the impact of various DG planning strategies with network reconfiguration on energy loss cost savings. The effectiveness of the HOA is demonstrated on 84-node practical and 141-node radial distribution networks. The results demonstrated that combining strategic DG placement with network reconfiguration significantly improved system performance across different voltage-dependent load models. This combined approach outperformed DG planning without reconfiguration under optimal power factor conditions, improving active power loss by 55.47%, reactive power loss by 55.85%, and bus voltage deviation by 47.86% in the 84-node network and 91.20%, 91.56%, and 78.50% in the 141-node network. Additionally, the efficacy of HOA is compared with practical swarm optimization, whale optimization, grasshopper optimization, zebra optimization, coot bird optimizer, and firefly algorithms. Overall, this approach significantly enhances the efficiency and reliability of power distribution networks, especially within complex power systems.

关键词： hippopotamus optimization algorithm Optimal placement of DG Network reconfiguration Voltage-dependent load models Loss reduction Bus voltage deviation reduction

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Research on Denoising of Bridge Dynamic Load Signal Based on hippopotamus optimization algorithm-Variational Mode Decomposition-Singular Spectrum Analysis Method

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BUILDINGS 2025年第8期15卷 1390-1390页

作者： Zhong, Zhengqiang Li, Zhen Wang, Jinlong Tang, Cong Liu, Yu Guo, Kaijun Changsha Univ Sci & Technol Sch Civil & Environm Engn Changsha 410114 Peoples R China Changsha Polytech Testing & Consulting Co Ltd Changsha 410114 Peoples R China

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.

关键词： bridge detection hippopotamus optimization algorithm variational mode decomposition singular spectrum analysis signal denoising

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Research of hippopotamus optimization algorithm optimized Back Propagation neural network algorithm for distortion correction in structured light point clouds

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OPTICAL ENGINEERING 2024年第12期63卷

作者： Yang, Xiaoping Lin, Jiahao Li, Peiwu Wu, Rongyu Chen, Xinfeng Feng, Yun Han, Xingguo Guilin Univ Technol Coll Phys & Elect Informat Engn Guilin Peoples R China Guilin Univ Technol Guangxi Key Lab Embedded Technol & Intelligent Sys Guilin Peoples R China Guilin Univ Technol Coll Comp Sci & Engn Guilin Peoples R China Guilin Measuring & Cutting Tool Co Ltd Guilin Peoples R China Guilin Univ Aerosp Technol Sch Mech Engn Guilin Peoples R China

Structured light 3D reconstruction point clouds are highly susceptible to camera distortion, which hinders their ability to meet the requirements of high-precision measurements. We propose a point cloud distortion correction method based on the hippopotamus optimization algorithm optimized Back Propagation neural network (HO-BP). The method involves performing a least squares (LS) plane fitting on the actual point cloud corrected by OpenCV to obtain the Z coordinates on the same plane. Using the pinhole model, the X and Y coordinates of the fitted plane are then recovered, resulting in the target plane point cloud. The HO-BP model establishes a mapping relationship between the actual point cloud and the target point cloud, thereby achieving distortion correction. The correction was applied to standard sphere point clouds at multiple different positions. Compared with the original point clouds, the mean absolute error (MAE) and root mean square error (RMSE) of the LS radius of the sphere point clouds corrected by HO-BP decreased by 72.42% and 62.62%, respectively. This demonstrates that the corrected target point clouds are closer to the ideal point clouds. Compared with existing algorithms, HO-BP also showed superior performance in terms of MAE and RMSE.

关键词： structured light 3D reconstruction point cloud distortion plane fitting hippopotamus optimization algorithm neural network

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An Improved hippopotamus optimization algorithm Utilizing Swarm-Elite Learning Mechanism's Levy Flight and Quadratic Interpolation Operators for Optimal Parameters Extraction in Photovoltaic Models 20th

An Improved Hippopotamus Optimization Algorithm Utilizing Sw...

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20th International Conference on Intelligent Computing (ICIC)

作者： Wang, Wentao Tian, Jun Nankai Univ Coll Software Tianjin 300350 Peoples R China

ISBN: (纸本)9789819755776;9789819755783

Photovoltaic (PV) systems are among the representatives of renewable energy technologies, and their performance is influenced by parameter configurations. This paper utilizes the swarm-elite learning mechanism's Levy flight and Quadratic interpolation strategies to enhance the optimization performance of the hippopotamus optimization algorithm (HO) in both the exploration and exploitation stages. The proposed algorithm is referred to as LQHO. The aim of this paper is to utilize LQHO to provide a high-quality solution for parameter extraction problems in various types of PV models. Ten representative CEC2017 functions and three PV models are selected for designing experiments to evaluate the optimization performance and parameter extraction capability of LQHO. Five advanced metaheuristic algorithms are chosen to design control group experiments. The results indicate that LQHO exhibits superior performance over its competitors in both the parameter extraction problems of the tenCEC2017 functions and the three PV models. This superiority is reflected in terms of solution accuracy, convergence speed, and robustness.

关键词： Photovoltaic Model hippopotamus optimization algorithm Clean Energy Swarm Intelligence optimization algorithm

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Novel Hybrid optimization Technique for Solar Photovoltaic Output Prediction Using Improved hippopotamus algorithm

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7803页

作者： Wang, Hongbin Mansor, Nurulafiqah Nadzirah Binti Bin Mokhlis, Hazlie Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia

This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved hippopotamus optimization algorithm (IHO). The IHO enhances the traditional hippopotamus optimization (HO) algorithm by addressing its limitations in search efficiency, convergence speed, and global exploration. The IHO algorithm used Latin hypercube sampling (LHS) for population initialization, significantly enhancing the diversity and global search potential of the optimization process. The integration of the Jaya algorithm further refines solution quality and accelerates convergence. Additionally, a combination of unordered dimensional sampling, random crossover, and sequential mutation is employed to enhance the optimization process. The effectiveness of the proposed IHO is demonstrated through the optimization of weights and neuron thresholds in the extreme learning machine (ELM), a model known for its rapid learning capabilities but often affected by the randomness of initial parameters. The IHO-optimized ELM (IHO-ELM) is tested against benchmark algorithms, including BP, the traditional ELM, the HO-ELM, LCN, and LSTM, showing significant improvements in prediction accuracy and stability. Moreover, the IHO-ELM model is validated in a different region to assess its generalization ability for solar PV output prediction. The results confirm that the proposed hybrid approach not only improves prediction accuracy but also demonstrates robust generalization capabilities, making it a promising tool for predictive modeling in solar energy systems.

关键词： hippopotamus optimization algorithm latin hypercube sampling Jaya algorithm extreme learning machine solar photovoltaic output prediction

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Uncertainty-aware oriented lifetime prediction of proton exchange membrane fuel cells based on high-order time-frequency health indicator

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 113卷 655-668页

作者： Ma, Ruodong Li, Jisen Zhao, Dongqi Zhou, Ze Xiong, Binyu Zhang, Liyan Chen, Qihong Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China Hubei Key Lab Fuel Cell Wuhan 430070 Peoples R China

Accurate degradation prediction of proton exchange membrane fuel cells is essential for their reliability and durability. However, the sophisticated degradation mechanism introduces uncertainties that compromise the prediction accuracy of PEMFCs lifetime. To address this problem, an uncertainty-aware network is proposed for interval prediction of degradation, which leverages higher-order time-frequency health indicators. These indicators are derived from higher-order voltage polynomials, with coefficients determined by frequency features extracted from the distribution relaxation time. This approach facilitates the extraction of multi-order effective information. The uncertainty-aware network achieves interval prediction by incorporating global quantile regression layer into bidirectional long short-term memory neural network, which increases prediction accuracy and reliability. Moreover, the nature-inspired hippopotamus optimization algorithm is employed to fine-tune hyperparameters of uncertainty-aware network, reducing computational complexity. The performance of proposed method is demonstrated through experimental comparisons. The root-mean-square error of prediction was improved by more than 39.65% for both static and dynamic conditions, and the accuracy of remaining life prediction was improved by more than 32.8%. This method provides a high-order interpretable time-frequency health indicator for fuel cell degradation prediction, which provides strong support for fuel cell degradation prediction and long-time stable operation.

关键词： Proton exchange membrane fuel cells Degradation prediction High-order time-frequency health indicator QR-BiLSTM hippopotamus optimization algorithm

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An innovative data-driven AI approach for detecting and isolating faults in gas turbines at power plants

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 263卷

作者： Amiri, Mohammad Hussein Hashjin, Nastaran Mehrabi Najafabadi, Maryam Khanian Beheshti, Amin Khodadadi, Nima Shahid Beheshti Univ Fac Elect Engn Tehran Iran Univ Sydney Sch Comp Sci Sydney NSW Australia Macquarie Univ Sch Comp Sydney NSW Australia Univ Miami Dept Civil & Architectural Engn Coral Gables FL 33146 USA

This study investigated the detection and isolation of gas path faults in a power plant gas turbine using efficiency data and fundamental quantities. First, attention is given to balancing data and selecting instances. Two new neural-fuzzy networks were then designed and trained using the hippopotamus optimization algorithm. Developing these two networks aims to create a network resilient to noise with high accuracy and a low parameter count. Third, a broad spectrum of Artificial Intelligence based methods, such as shallow neural networks, machine learning models, and deep learning models, were employed to compare the proposed networks for fault detection and isolation of one power plant 163 MW gas turbine from Siemens Company. The investigation results indicate that the proposed hierarchical structure achieved an average of 99.81 % for fault detection and 99.50 % for fault isolation, consisting of only 203 learning parameters for fault detection and 335 for fault isolation, and operates better than the methods mentioned above in terms of accuracy, precision, sensitivity, and F1-Score metrics criteria.

关键词： Gas turbine Fault detection and isolation Gas path faults Machine learning hippopotamus optimization algorithm Type-3 Fuzzy

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Sign language recognition using modified deep learning network and hybrid optimization: a hybrid optimizer (HO) based optimized CNNSa-LSTM approach

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SCIENTIFIC REPORTS 2024年第1期14卷 1-22页

作者： Baihan, Abdullah Alutaibi, Ahmed I. Alshehri, Mohammed Sharma, Sunil Kumar King Saud Univ Community Coll Comp Sci Dept Riyadh 11437 Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Dept Comp Engn Majmaah 11952 Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Dept Informat Technol Majmaah 11952 Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Dept Informat Syst Majmaah 11952 Saudi Arabia

Speech impairment limits a person's capacity for oral and auditory communication. Improvements in communication between the deaf and the general public can be progressed by a real-time sign language detector. Recent studies have contributed to make progress in motion and gesture identification processes using Deep Learning (DL) methods and computer vision. But the development of static and dynamic sign language recognition (SLR) models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers' speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. This study mainly focused on SLR using a modified DL and hybrid optimization approach. Notably, spatial and geometric-based features are extracted via the Visual Geometry Group 16 (VGG16), and motion features are extracted using the optical flow approach. A new DL model, CNNSa-LSTM, is a combination of a Convolutional Neural Network (CNN), Self-Attention (SA), and Long-Short-Term Memory (LSTM) to identify sign language. This model is developed for feature extraction by combining CNNs for spatial analysis with SA mechanisms for focusing on relevant features, while LSTM effectively models temporal dependencies. The proposed CNNSa-LSTM model enhances performance in tasks involving complex, sequential data, such as sign language processing. Besides, a Hybrid Optimizer (HO) is proposed using the hippopotamus optimization algorithm (HOA) and the Pathfinder algorithm (PFA). The proposed model has been implemented in Python, and it has been evaluated over the existing models in terms of accuracy (98.7%), sensitivity (98.2%), precision (98.5%), Word Error Rate (WER) (0.131), Sign Error Rate (SER) (0.114), and Normalized Discounted Cumulative Gain (NDCG) (98%) as well. The proposed model has recorded the highest accuracy of 98.7%.

关键词： Sign language recognition (SLR) Deep learning Convolutional neural network hippopotamus optimization algorithm Pathfinder algorithm

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Hybrid Brown-Bear and hippopotamus algorithms with Fractional Order Chaos Maps for Precise Solar PV Model Parameter Estimation

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PROCESSES 2024年第12期12卷 2718-2718页

作者： Chaib, Lakhdar Tadj, Mohammed Choucha, Abdelghani El-Rifaie, Ali M. Shaheen, Abdullah M. Univ Tamanghasset Energy & Mat Lab Sersouf POB 10034 Tamanghasset 11001 Algeria Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait Suez Univ Fac Engn Dept Elect Engn POB 43221 Suez 43221 Egypt

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 x 10-4, 7.478488 x 10-4), (1.430320 x 10-2, 1.427010 x 10-2), and (2.220075 x 10-3, 2.061273 x 10-3), respectively. The experimental results show that the EBOA works better than alternative techniques in terms of accuracy, consistency, and convergence.

关键词： solar photovoltaic energy parameters extraction enhanced brown-bear optimization algorithm hippopotamus optimization algorithm

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