Article
Computer Science, Artificial Intelligence
Ranya Alwajih, Said Jadid Abdulkadir, Hitham Al Hussian, Norshakirah Aziz, Qasem Al-Tashi, Seyedali Mirjalili, Alawi Alqushaibi
Summary: This study introduces a new hybrid feature selection method, called BWOAHHO, which has been found to outperform other algorithms in terms of classification accuracy, fitness, selected attribute size, and computing time.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Mohammad Shehab, Ibrahim Mashal, Zaid Momani, Mohd Khaled Yousef Shambour, Anas AL-Badareen, Saja Al-Dabet, Norma Bataina, Anas Ratib Alsoud, Laith Abualigah
Summary: This paper introduces a new swarm intelligence optimization algorithm called Harris hawks optimization (HHO) and analyzes its major features. HHO has been recognized as one of the most effective optimization algorithms and has been successfully applied in various domains, such as energy and power flow, engineering, medical applications, networks, and image processing. The review paper provides an overview of the available related works of HHO, including its variants, modification, and hybridization, as well as its applications and a comparison with other algorithms. The conclusions focus on the existing work on HHO, highlighting its disadvantages and proposing future research directions. The paper is valuable for researchers and practitioners in optimization, engineering, medical, data mining, and clustering, offering potential future research opportunities and contributing to research on health, environment, and public safety.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2022)
Article
Engineering, Geological
Bhatawdekar Ramesh Murlidhar, Hoang Nguyen, Jamal Rostami, XuanNam Bui, Danial Jahed Armaghani, Prashanth Ragam, Edy Tonnizam Mohamad
Summary: This study examined and estimated the flyrock distance induced by blasting using five artificial intelligent algorithms, with the Harris Hawks optimization-based MLP (HHO-MLP) showing the best performance among all models. Data was collected from 152 blasting events in three open-pit granite mines in Johor, Malaysia.
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
(2021)
Article
Mathematics
Sultan Almotairi, Elsayed Badr, Mustafa Abdul Salam, Hagar Ahmed
Summary: Three contributions are proposed in this study. Firstly, a novel hybrid classifier (HHO-SVM) is introduced, which combines the Harris hawks optimization (HHO) and a support vector machine (SVM). Secondly, the performance of the HHO-SVM is improved using the conventional normalization method. Lastly, a parallel approach is adopted to enhance the efficiency of the HHO-SVM by utilizing data distribution. Evaluation on the Wisconsin Diagnosis Breast Cancer (WDBC) dataset shows that the HHO-SVM achieves high accuracy rates with different scaling techniques and the parallel version provides significant acceleration.
Article
Geosciences, Multidisciplinary
Chuanqi Li, Jian Zhou, Kun Du, Danial Jahed Armaghani, Shuai Huang
Summary: In order to mitigate the dangers of flyrock in open-pit mines, a new model called MSHHO-SVR, based on Harris hawks optimization and multi-strategies support vector regression, was developed to predict the flyrock distance (FD). The model was compared to other models such as HHO-SVR, back-propagation neural network, extreme learning machine, kernel extreme learning machine, and empirical methods. Evaluation metrics including RMSE, MAE, R2, and VAF were used to assess the model performance. The results showed that MSHHO-SVR outperformed the other models, achieving the lowest RMSE, highest R2, and MAE, and highest VAF values.
NATURAL RESOURCES RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Harun Gezici, Haydar Livatyali
Summary: In this study, Harris hawks optimization (HHO) is hybridized with 10 different chaotic maps to improve its performance. The results show that chaotic maps enhance the efficiency of HHO, with the piecewise map method being the most effective one. Comparisons with other metaheuristic algorithms demonstrate that the proposed chaotic HHO algorithm successfully solves engineering problems.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Multidisciplinary Sciences
Shangbin Jiao, Chen Wang, Rui Gao, Yuxing Li, Qing Zhang
Summary: The article discusses the limitations of the Harris Hawks optimization algorithm and proposes an improved multi-strategy search algorithm to enhance convergence speed and global search ability. Numerical experiments show that the improved algorithm outperforms traditional algorithms in terms of convergence speed, solution accuracy, and robustness.
Article
Computer Science, Interdisciplinary Applications
Yanan Zhang, Renjing Liu, Xin Wang, Huiling Chen, Chengye Li
Summary: This paper introduces an improved Harris hawks optimization (HHO) method for global optimization and feature selection tasks. By embedding the salp swarm algorithm (SSA) into the original HHO, the proposed IHHO enhances the search ability of the optimizer and broadens its application scope.
ENGINEERING WITH COMPUTERS
(2021)
Article
Automation & Control Systems
Dalia Yousri, Seyedali Mirjalili, J. A. Tenreiro Machado, Sudhakar Babu Thanikanti, Osama Elbaksawi, Ahmed Fathy
Summary: This paper proposes a novel approach to enhance the exploratory behavior of the Harris hawks optimizer based on fractional calculus memory concept, resulting in the fractional-order modified Harris hawks optimizer (FMHHO). The sensitivity of algorithm performance to FOC parameters is addressed, with the best variant recommended based on benchmarks. The proposed variant is validated using CEC2017 benchmarks and compared to other techniques through statistical measures and non-parametric tests, showing improved performance and accurate solutions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Engineering, Multidisciplinary
Xin Wang, Xiaogang Dong, Yanan Zhang, Huiling Chen
Summary: This paper proposes a variant of the Harris Hawks Optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which uses the Crisscross Optimization Algorithm (CSO) and shows improved efficiency and convergence on various optimization tasks.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Haoxuan Li, Zhenzhong Shen, Yiqing Sun, Yijun Wu, Liqun Xu, Yongkang Shu, Jiacheng Tan
Summary: Seepage is the primary cause of dam failures, and conducting regular seepage analysis can effectively prevent accidents. This study combines the Whale Optimization Algorithm (WOA) with Support Vector Regression (SVR) to invert hydraulic conductivity, and the effectiveness and practicality of the improved algorithm are evaluated through numerical experiments. The proposed inversion method is more feasible and accurate than existing hydraulic conductivity estimation methods.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Marine
Junyi Yang, Yutong Yao, Donghe Yang
Summary: Due to the complexity of the underwater environment, traditional particle filters face challenges in tracking underwater targets. This paper proposes a new tracking algorithm using Harris-hawks-optimized particle filters (HHOPF) to improve the tracking accuracy. It addresses the problem of underwater target feature construction and scale transformation, and introduces a corrected background-weighted histogram method for feature recognition and a scale filter for target scaling transformation. Additionally, a nonlinear escape energy is constructed using the Harris hawks algorithm to balance exploration and exploitation processes for faster computational speed. Experimental results show that the proposed HHOPF tracker provides better tracking results compared to other algorithms.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Inder Khatri, Arjun Choudhry, Aryaman Rao, Aryan Tyagi, Dinesh Kumar Vishwakarma, Mukesh Prasad
Summary: This study proposes an algorithm for influence maximization, which discretizes the Harris's Hawks Optimization meta-heuristic algorithm and utilizes community structures for seed node selection. The experiments show that the algorithm performs better than competing methods on multiple social networks.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Hai-Lin Zhang, Min-Rong Chen, Pei-Shan Li, Jun-Jie Huang
Summary: This paper proposes an improved hybrid algorithm of Harris Hawks optimizer and extremal optimization (IHHO-EO) to enhance the performance of HHO. Experimental results demonstrate the effectiveness of the added strategies. Furthermore, the proposed approach shows excellent performance in solving the pressure vessel design problem.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Hager Fahmy, Eman M. El-Gendy, M. A. Mohamed, Mahmoud M. Saafan
Summary: This paper proposes a hybrid Enhanced Chimp-Harris Hawks Optimization Algorithm (ECH3OA) that combines both enhanced Chimp Optimization algorithm (ChOA) and Harris Hawks optimization algorithm (HHO). The novelty of ECH3OA algorithm lies in its enhanced ChOA, updated formula for calculating escaping energy, and utilization of four cases of HHO exploitation. The algorithm achieves superior performance compared to other state-of-the-art algorithms.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Public, Environmental & Occupational Health
Jian Zhou, Yuxin Chen, Hui Chen, Manoj Khandelwal, Masoud Monjezi, Kang Peng
Summary: Pillar stability is crucial for safe work in mines. Accurate estimation of induced stresses in pillars is important for design and guaranteeing stability. Machine learning algorithms, such as back-propagation neural network (BPNN), have been successfully applied to pillar stability assessment with high accuracy.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Metallurgy & Metallurgical Engineering
Jian Zhou, Peixi Yang, Pingan Peng, Manoj Khandelwal, Yingui Qiu
Summary: Three hybrid support vector machine (SVM) models optimized by particle swarm optimization (PSO), Harris hawk optimization (HHO), and moth flame optimization (MFO) are proposed to predict rockburst hazard level (RHL), achieving better accuracy and performance than the unoptimized SVM model.
MINING METALLURGY & EXPLORATION
(2023)
Article
Chemistry, Physical
Peixi Yang, Chuanqi Li, Yingui Qiu, Shuai Huang, Jian Zhou
Summary: This study utilized three meta-heuristic optimization algorithms to select the optimal hyperparameters of the random forest model for predicting the punching shear strength of FRP-RC beams. The ALO-RF model with a population size of 100 showed the best prediction performance, and adjusting the slab's effective depth effectively controlled the punching shear strength. Furthermore, the hybrid machine learning model optimized by metaheuristic algorithms outperformed traditional models in terms of prediction accuracy and error control.
Article
Chemistry, Physical
Kun Du, Songge Yang, Jian Zhou, Lichang Wang
Summary: It is important to study the evaluation indexes and classification criteria of the bursting liability of hard rocks for the prediction and prevention of rockbursts. In this study, the rockburst tendency was evaluated using the brittleness indicator (B2) and the strength decrease rate (SDR). The measuring methods and classification criteria were analyzed, and four grades of rockburst tendency were defined based on the test results.
Article
Chemistry, Physical
Chuanqi Li, Xiancheng Mei, Daniel Dias, Zhen Cui, Jian Zhou
Summary: This paper proposes a novel hybrid artificial neural network model optimized using a reptile search algorithm with circle mapping to predict the compressive strength of rice husk ash concrete. The proposed model achieved the most satisfactory prediction accuracy regarding R-2 (0.9709), VAF (97.0911%), RMSE (3.4489), and MAE (2.6451), outperforming previously developed models.
Article
Metallurgy & Metallurgical Engineering
Chao Chen, Jian Zhou
Summary: An empirical classification model based on elastic energy index (W-et) and impact energy index (K-c) was established to analyze the risk level of coal burst. The Kriging method was used to display the classification boundaries and distribution characteristics of coal burst liabilities (CBLs) on a 2D chart. The reliability of the spatial interpolation model was further validated using 43 test samples. Results showed that the Kriging model had a classification accuracy of 91% and outperformed other uncertainty-based methods. This model can be a valuable tool for geological hazard prevention and initial design.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2023)
Article
Metallurgy & Metallurgical Engineering
Shi-ming Wang, Jia-qi Wang, Xian-rui Xiong, Zheng-hong Chen, Shi-jun Yan, Jian Zhou
Summary: This paper investigates the zonal disintegration phenomenon of surrounding rocks in deep roadway excavation. Experimental and theoretical analysis show that there is a maximum tensile strain at the elastic-plastic boundary, leading to the formation of annular cracks and zonal fractures. ABAQUS simulation results are consistent with the experiments and provide an intuitive explanation for the spalling and damage to the specimen.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2023)
Article
Computer Science, Interdisciplinary Applications
Jian Zhou, Yulin Zhang, Chuanqi Li, Weixun Yong, Yingui Qiu, Kun Du, Shiming Wang
Summary: This research introduces a groundbreaking intelligent model called the GWO-RF model for predicting water inflow during tunnel construction. By combining the capabilities of Grey Wolf Optimization (GWO) with the Random Forest (RF) algorithm, this model aims to enhance the accuracy and effectiveness of water inflow prediction, ultimately improving safety measures in tunnel construction projects. The GWO-RF model outperforms other ensemble models in terms of predictive accuracy, making it invaluable for tunneling projects.
EARTH SCIENCE INFORMATICS
(2023)
Article
Mining & Mineral Processing
Chuanqi Li, Jian Zhou, Kun Du, Daniel Dias
Summary: This paper aims to develop hybrid support vector machine (SVM) models improved by three metaheuristic algorithms known as grey wolf optimizer (GWO), whale optimization algorithm (WOA) and sparrow search algorithm (SSA) for predicting the hard rock pillar stability. The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices. However, the performance of the SSA-SVM model for predicting the unstable pillar is not as good as those for stable and failed pillars.
INTERNATIONAL JOURNAL OF MINING SCIENCE AND TECHNOLOGY
(2023)
Article
Geosciences, Multidisciplinary
Chuanqi Li, Jian Zhou, Daniel Dias, Kun Du, Manoj Khandelwal
Summary: In this study, 386 rock samples were used to predict the uniaxial compressive strength (UCS) using various empirical equations and artificial intelligence methods. The evaluation results showed that the artificial intelligence models outperformed the empirical approaches, especially the LSO-RF model. The porosity (Pn) was identified as the most important input variable for predicting UCS.
Article
Engineering, Geological
Yingui Qiu, Jian Zhou
Summary: Rockburst poses significant risks to mine workers and infrastructure. This study developed a novel hybrid model, SCSO-XGBoost, for predicting the scale of short-term rockburst damage. The model achieved high accuracy and outperformed other models, demonstrating its effectiveness in rockburst damage prediction.
ROCK MECHANICS AND ROCK ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Mingchun Lin, Guanqi Wang, Jian Zhou, Wei Zhou, Ni An, Gang Ma
Summary: This study analyzes the deformation characteristics of crushable particle materials through a series of cyclic loading tests conducted by numerical simulation. The investigation of hysteretic behavior from a particle scale reveals that an increase in particles with contacts less than two may cause residual strain, and particle breakage facilitates particle rearrangement and volume contraction. The accumulation of plastic strain and the resilient modulus are found to be influenced by confining pressures, stress levels, cyclic loading amplitudes, and the number of cycles. A function for plastic strain accumulation and an evolution function for resilient modulus are proposed.
JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A
(2023)
Article
Engineering, Civil
Jian Zhou, Shuai Huang, Ming Tao, Manoj Khandelwal, Yong Dai, Mingsheng Zhao
Summary: This study proposes a new prediction method based on machine learning to scientifically adjust the critical span graph. The prediction performance of the proposed PSO-GBDT model is the most reliable among the other eight models, with a classification accuracy of 0.93. It has great potential to provide a more scientific and accurate choice for the stability prediction of underground excavations.
Article
Engineering, Civil
Enming Li, Ning Zhang, Bin Xi, Jian Zhou, Xiaofeng Gao
Summary: This study successfully predicts the compressive strength of green concrete by combining novel algorithms with the XGB model, with the SSA-XGB model performing the best.
FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING
(2023)
Article
Construction & Building Technology
Jian Zhou, Peixi Yang, Chuanqi Li, Kun Du
Summary: In this study, predictive models for shear strength in soil-structure interactions were generated using different algorithms, and important morphological parameters were selected. The results showed that the random forest model optimized using the whale optimization algorithm performed the best in terms of predictive performance.
CONSTRUCTION AND BUILDING MATERIALS
(2023)