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Investigating a hybrid extreme learning machine coupled with dingo optimization algorithm for modeling liquefaction triggering in sand-silt mixtures

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SCIENTIFIC REPORTS 2024年第1期14卷 1-20页

作者： Hameed, Mohammed Majeed Masood, Adil Srivastava, Aman Rahman, Norinah Abd Razali, Siti Fatin Mohd Salem, Ali Elbeltagi, Ahmed Al Maarif Univ Coll Dept Civil Engn Ramadi Iraq Al Maarif Univ Coll Dept Comp Sci Ramadi Iraq TERI Sch Adv Studies Dept Nat & Appl Sci New Delhi India Indian Inst Technol IIT Kharagpur Dept Civil Engn Kharagpur 721302 W Bengal India Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Civil Engn Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Smart & Sustainable Township Res Ctr SUTRA Bangi 43600 Selangor Malaysia Minia Univ Fac Engn Civil Engn Dept Al Minya 61111 Egypt Univ Pecs Fac Engn & Informat Technol Struct Diagnost & Anal Res Grp Pecs Hungary Mansoura Univ Fac Agr Agr Engn Dept Mansoura 35516 Egypt

Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with dingo optimization algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with the classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.900%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored for the ELM-DOA model, to assist engineers and researchers in maximizing the utilization of this predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

关键词： Liquefaction Earthquake dingo optimization algorithm Non-linear normalization

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Enhanced dingo optimization algorithm Based on Differential Evolution and Chaotic Mapping for Engineering optimization 15th

Enhanced Dingo Optimization Algorithm Based on Differential ...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Yu, Zijing Shao, Peng Zhang, Shaoping Jiangxi Agr Univ Sch Comp & Informat Engn Nanchang 330045 Jiangxi Peoples R China

ISBN: (纸本)9789819771806;9789819771813

Aiming at the problems of lower initial population diversity and insufficient global search ability of primitive dingo optimization algorithm (DOA), a dingo optimization algorithm based on differential evolution and chaotic mapping (DCDOA) is proposed. In DCDOA, differential evolution is introduced to randomly generate a new population to increase the diversity of the dingo population;Tent chaotic map can effectively faster the convergence rate and strengthen the global search ability. Taking CEC2019 as the test function set, they are performed by DCDOA and three other algorithms. Experiments show that DCDOA has superior with convergence performance and stronger robustness. Furthermore, to verify its performance in solving engineering optimization problems (pressure vessel and car side collisions design). The experimental results demonstrate that DCDOA conserves 49.85% and 4.62% in economic costs for pressure vessels and vehicle side collisions compared to DOA, respectively, verifying the practicality and superiority of DCDOA for engineering optimization problems.

关键词： dingo optimization algorithm Differential evolution algorithm Tent chaos mapping CEC2019 Engineering optimization

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Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy dingo optimization algorithm and Binary Salp Swarm algorithm

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FUTURE INTERNET 2023年第11期15卷 357-357页

作者： Cai, Weihong Duan, Fengxi Shantou Univ Dept Comp Shantou 515063 Guangdong Peoples R China

With the development of computationally intensive applications, the demand for edge cloud computing systems has increased, creating significant challenges for edge cloud computing networks. In this paper, we consider a simple three-tier computational model for multiuser mobile edge computing (MEC) and introduce two major problems of task scheduling for federated learning in MEC environments: (1) the transmission power allocation (PA) problem, and (2) the dual decision-making problems of joint request offloading and computational resource scheduling (JRORS). At the same time, we factor in server pricing and task completion, in order to improve the user-friendliness and fairness in scheduling decisions. The solving of these problems simultaneously ensures both scheduling efficiency and system quality of service (QoS), to achieve a balance between efficiency and user satisfaction. Then, we propose an adaptive greedy dingo optimization algorithm (AGDOA) based on greedy policies and parameter adaptation to solve the PA problem and construct a binary salp swarm algorithm (BSSA) that introduces binary coding to solve the discrete JRORS problem. Finally, simulations were conducted to verify the better performance compared to the traditional algorithms. The proposed algorithm improved the convergence speed of the algorithm in terms of scheduling efficiency, improved the system response rate, and found solutions with a lower energy consumption. In addition, the search results had a higher fairness and system welfare in terms of system quality of service.

关键词： edge cloud computing Internet of things dingo optimization algorithm salp swarm algorithm federated learning

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Predicting mode I fracture toughness of CCNBD quasi-brittle specimens using hybrid gradient boosting algorithms

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THEORETICAL AND APPLIED FRACTURE MECHANICS 2025年 138卷

作者： Rouhani, Mohammad Matin Dolatshahi, Alireza Amirkabir Univ Technol Dept Min Engn Tehran Iran

This study examines the prediction of Mode I fracture toughness in Cracked Chevron Notched Brazilian Disc (CCNBD) specimens by integrating hybrid machine learning (ML) algorithms-specifically the XGBoost, the CatBoost, and the LightGBM-with metaheuristic optimization techniques This study specifically employs the Reptile Search algorithm (RSO) and the dingo optimization algorithm (DOA) for hyperparameter tuning to further optimize model performance. Using a dataset of 150 samples of rock and cement-based materials, the results show that the RSO-CatBoost model has an impressive performance both in training (R2 = 0.9825) and in testing (R2 = 0.98), while the DOA-CatBoost model also shows a good performance. Besides the ML models, novel empirical models for estimating fracture toughness are presented in this study, which overcomes the historical limitation of classical formulas using the addition of geometric and mechanical parameters. With an R2 of 0.664 versus a maximum of 0.582 for previous equations, the empirical models outperform the existing equations highlighting their increased predictive power. Combining these methods offers a considerable increase in prediction accuracy as well as valuable interpretation concerning the role of tensile strength and specimen geometry on fracture toughness. This work, therefore, offers a dual contribution: advanced predictive models via ML with refined hyperparameter tuning and improved empirical equations that better capture the complex behavior of quasi-brittle materials, reducing the need for extensive physical testing.

关键词： Quasi-brittle Materials Fracture Toughness XGBoost LightGBM CatBoost Reptile Search algorithm dingo optimization algorithm

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Adaptive grouped balanced binary tree based multi user secure data transmission on cloud

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International Journal of Information Technology (Singapore) 2025年第2期17卷 941-949页

作者： Pavithra, P. Hariharan, B. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India

Secure multi-keyword search for outsourced cloud data has gained popularity, especially for scenarios involving multiple data owners. This work proposes a method for secure multi-keyword searches across encrypted cloud data from various owners, utilizing an administrator or trustworthy third party for key management. The approach employs the TF-IDF model and Vector Space Model (VSM) to generate index and query vectors, and uses an advanced Attribute-Based Encryption (ABE) algorithm for data privacy. The Improved dingo optimization algorithm (IDOA) optimally selects the key value. Encrypted emails are sent to a cloud server, and a Grouped Balanced Binary (GBB) tree structure is used for index construction. The Greedy Depth-First Search (GDFS) method efficiently searches the index, grouping relevant documents to exclude unnecessary ones. Performance is evaluated based on memory usage, execution time, encryption and decryption times, and search times, implemented in Java and tested on a cloud simulator. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Attribute-based encryption and vector space model Cloud computing dingo optimization algorithm Multi keyword search scheme Multiple data owner

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ECG Arrhythmia Measurement and Classification for Portable Monitoring

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MEASUREMENT SCIENCE REVIEW 2024年第4期24卷 118-128页

作者： Gladis, K. P. Ajitha Ahilan, A. Muthukumaran, N. Jenifer, L. CSI Inst Technol Dept Informat Technol Thovalai 629302 Tamilnadu India PSN Coll Engn & Technol Dept Elect & Commun Engn Tirunelveli Tamil Nadu India Sri Eshwar Coll Engn Ctr Computat Imaging & Machine Vis Dept Elect & Commun Engn Coimbatore 641202 Tamil Nadu India Sathyabama Inst Sci & Technol Dept Elect & Elect Engn Chennai 600119 India

Globally, cardiovascular disease kills more than 500000 people every year, thus becoming the primary reason for death. Nevertheless, cardiovascular health monitoring is essential for accurate analysis and therapy of heart disease. In this work, a novel deep learning-based StrIppeD NAS-Network (SID-NASNet) for arrhythmia categorization into octa-classes with electrocardiogram (ECG) signals is presented. First, the ECG signals are recorded in real time using 12-lead electrodes. Then, the Discrete Wavelet Transform (DWT) is used to denoise the signals to reduce repetition and increase resilience. The noise-free ECG signals are fed into a K-means clustering algorithm to group ECG signal segments into a set number of clusters to identify patterns that may indicate heart abnormalities. Subsequently, the deep learning-based NASNet with Stripped convolutional layers is used to detect ECG irregularities of arrhythmia. Each sample point is examined for its local fractal dimension before extracting the heartbeat waveforms within a predetermined window length. A bio-inspired dingo optimization (DO) algorithm is used in the SID-NASNet to normalize the parameters to improve the efficiency of the network with low network complexity. The efficiency of the proposed SID-NASNet is assessed with specificity, accuracy, precision, F1 score and recall based on the MIT-BIH arrhythmia dataset. From the test results, the proposed SID-NASNet achieves an accuracy of 98.22% for effective categorization of ECG signals. The proposed SID-NASNet improves the overall accuracy of 1.24%, 3.76%, 1.87%, and 0.22% better than ECG-NET, Deep Learning (DL)-based GAN, 1D-CNN, and GAN-Long-Short Term Memory (LSTM), respectively.

关键词： ECG signal arrhythmia classification deep learning discrete wavelet transform stripped convolution dingo optimization algorithm

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A Frequency Support Approach for Hybrid Energy Systems Considering Energy Storage

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ENERGIES 2023年第10期16卷 4252页

作者： Li, Dahu Zhou, Hongyu Chen, Yuan Zhou, Yue Rao, Yuze Yao, Wei State Grid Hubei Elect Power Co Ltd Wuhan 430077 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China China Hubei Emiss Exchange Wuhan 430070 Peoples R China

In hybrid energy systems, the intermittent and fluctuating nature of new energy sources poses major challenges for the regulation and control of power systems. To mitigate these challenges, energy storage devices have gained attention for their ability to rapidly charge and discharge. Collaborating with wind power (WP), energy storage (ES) can participate in the frequency control of regional power grids. This approach has garnered extensive interest from scholars worldwide. This paper proposes a two-region load frequency control model that accounts for thermal power, hydropower, ES, and WP. To address complex, nonlinear optimization problems, the dingo optimization algorithm (DOA) is employed to quickly obtain optimal power dispatching commands under different power disturbances. The DOA algorithm's effectiveness is verified through the simulation of the two-region model. Furthermore, to further validate the proposed method's optimization effect, the DOA algorithm's optimization results are compared with those of the genetic algorithm (GA) and proportion method (PROP). Simulation results show that the optimization effect of DOA is more significant than the other methods.

关键词： hybrid energy system energy storage wind power support frequency dingo optimization algorithm

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Gasification process modelling and optimization using Gaussian process regression and hybrid population-based algorithms

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 4151-4171页

作者： Si, Hongying Shangqiu Normal Univ Sch Math & Stat Shangqiu 476000 Henan Peoples R China

Gasification holds a central role in the thermochemical conversion of diverse carbon-rich feedstocks into valuable syngas, making a substantial contribution to the advancement of environmentally sustainable clean energy generation. The methodical modelling and optimization of gasification processes hold paramount significance in augmenting their overall operational efficiency. As part of the ongoing research endeavour, a novel approach is introduced that combines Gaussian Process Regression (GPR) modelling with the Population-Based Vortex Search algorithm (PVSA) and the dingo optimization algorithm (DOA). The core aim of this methodology is to enhance and optimize gasification processes. GPR serves as a surrogate model used to proficiently capture the intricate relationships between input variables and gasification performance metrics. The implementation of GPR ensures predictive accuracy, facilitating a more streamlined exploration of the design space while concurrently reducing the demands on computational resources. The integration of GPR modelling in conjunction with the hybrid approach, incorporating PVSA and DOA, markedly augments both the efficiency and precision in the design and control of gasification processes. The GPPV hybrid model has achieved the most optimal result with the highest R2 value of 0.989 and 0.987 for the CH4 and C2Hn and the lowest RMSE of 0.476 and 0.164 for CH4 and C2Hn, indicating the reliability of the PVSA in optimizing the GPR model in predicting the syngas of gasification process. The framework expounded upon in this investigation provides a sturdy foundation for the progression of gasification technology, encompassing a diverse array of applications in the domains of clean energy production and sustainability endeavours.

关键词： Syngas Gasification Gaussian process regression Population-based vortex search algorithm dingo optimization algorithm

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Predicting the compressive strength of high-performance concrete employing multi-composed radial-based neural network

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 1925-1940页

作者： Wang, Honghong Chongqing Vocat Coll Transportat Sch Rd Bridge & Architecture Chongqing 402247 Peoples R China

In constructing bridges, dams, and other special structures, high-performance concrete (HPC) is a widely used material that consists of cement, water, aggregates, and some additives to improve its properties. By incorporating materials like fly ash and silica fume, the compressive strength of HPC can be increased. However, obtaining the best mixture design for HPC is complex and challenging. To save time and energy, machine learning methods like the Radial Basis Function (RBF) can be used to anticipate the crushing resilience of HPC. This study employed 344 datasets from published articles with variables of input containing natural coarse aggregate (NCA), sand (S), cement (C), water (W), reprocessed coarse aggregate (RCA), high-range water reducers, dimensions of RCA, the mass of RCA, and RCA moisture absorption. In addition, the RBF is combined with three meta-heuristic algorithms, namely, Augmented Grey Wolf Optimizer (AGWO), dingo optimization algorithm (DOA), and Wild Geese algorithm (WGA), to obtain optimal and suitable results. The algorithms were coupled with the RBF model, resulting in three different models: RBAG, RBWG, and RBDO. Criteria indicators were used to determine the most desirable hybrid model. The RBAG algorithm was identified as the most effective combination with the RBF model, producing results of RMSE = 1.5594 (MPa), R2 = 0.9985%, MSE = 2.431 (MPa), SMAPE = 0.00023 (MPa), and VAF = 99.14 (MPa). In summary, the use of the RBF model, combined with meta-heuristic algorithms, is motivated by the need for a more efficient, accurate, and systematic approach to optimize the mixture design of HPC. These models save time and resources while delivering reliable predictions, making them valuable tools in the construction industry.

关键词： High-performance concrete Compressive strength Radial Basis Function Augmented Grey Wolf Optimizer dingo optimization algorithm Wild Geese algorithm

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Toward Precise Long-Term Rockburst Forecasting: A Fusion of SVM and Cutting-Edge Meta-heuristic algorithms

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NATURAL RESOURCES RESEARCH 2024年第5期33卷 2037-2062页

作者： Armaghani, Danial Jahed Yang, Peixi He, Xuzhen Pradhan, Biswajeet Zhou, Jian Sheng, Daichao Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Univ Technol Sydney Sch Civil & Environm Engn Ultimo NSW 2007 Australia Univ Technol Sydney Fac Engn & Informat Technol Ctr Adv Modelling & Geospatial Informat Syst Sch Civil & Environm Engn Ultimo NSW Australia

Rockburst is one of the most hazardous geological disasters in underground engineering due to its complex causes and destructive nature. To address this, there is an imperative for methodologies that can predict rockbursts quickly and effectively to mitigate preemptively the risks and damages. In this study, 259 rockburst instances were analyzed, employing six rockburst feature parameters: maximum tangential stress (sigma theta), uniaxial compressive strength of rock (sigma c), uniaxial tensile strength of rock (sigma t), stress coefficient (sigma theta/sigma t), rock brittleness coefficient (sigma c/sigma t), and elastic energy index (Wet) as inputs. By integrating three novel meta-heuristic algorithms-dingo optimization algorithm (DOA), osprey optimization algorithm (OOA), and rime-ice optimization algorithm (RIME)-with support vector machine (SVM), hybrid models for long-term rockburst trend prediction were constructed. Performance evaluations through fivefold cross-validation revealed that for the no rockbursts, DOA-SVM (Pop = 200) demonstrated superior predictive performance, achieving an accuracy of 0.9808, precision of 0.9231, recall of 1, and an F1-score of 0.96. For moderate rockbursts, OOA-SVM (Pop = 100) emerged as the most effective, registering an accuracy of 0.9808, precision of 0.9545, recall of 1, and an F1-score of 0.9767. For light and severe rockbursts, DOA-SVM, OOA-SVM, and RIME-SVM showcased comparable predictive outcomes. However, these hybrid models outperformed traditional SVM models optimized with conventional algorithms in terms of accuracy across all rockburst hazard levels. Moreover, the hybrid models underwent additional validation with a new dataset of 20 rockburst instances collected globally, confirming their robust efficacy and exceptional generalization capabilities. An ensuing analysis using local interpretable model-agnostic explanations (LIME) on the six key feature parameters revealed a significant positive correlation between s

关键词： dingo optimization algorithm Osprey optimization algorithm Rime-ice optimization algorithm LIME Long-term rockburst

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