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Simulation-based mineral prospectivity modeling and gray wolf optimization algorithm for delimiting exploration targets

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ORE GEOLOGY REVIEWS 2025年 177卷

作者： Mostafaei, Kamran Yousefi, Mahyar Kreuzer, Oliver Kianpour, Mohammad Nabi Univ Kurdistan Fac Engn Dept Min Engn Sanandaj Iran Malayer Univ Fac Engn Dept Min Engn Malayer Iran Corp Geosci Grp CGSG POB 5128 Rockingham Beach WA 6969 Australia James Cook Univ Coll Sci & Engn Econ Geol Res Ctr EGRU Townsville Qld 4811 Australia Univ Sains Malaysia USM Sch Mat & Mineral Resources Engn Engn Campus Gelugor Malaysia

Exploration targeting is a multi-step process concerned with delimiting progressively smaller areas that are prospective for the targeted mineral deposit type, capable of hosting a potentially economic deposit and deserving of exploration funds. In mineral prospectivity modeling (MPM), target delineation represents the final stage of a procedure designed to identify discrete, explorable areas of high discovery potential within a much larger area of interest, typically covering entire camps, districts or provinces. However, defining unbiased thresholds for discriminating between high, moderate and low priority exploration targets is not a straightforward task. To avoid human bias in this thresholding process, a more structured, automated approach is needed. This study presents a simulation-based approach to MPM that adapts the Grey wolf Optimizer (GWO) algorithm, a swarm intelligence method capable of objectively delineating exploration targets from MPM results. Our approach aims to reduce bias by applying Monte Carlo Simulation to the assignment of robust weights to the predictor maps at the core of the MPM procedure. The GWO algorithm facilitates the classification and prioritization and enhances the accuracy and reliability of the resulting targets. The proposed procedure is demonstrated here using a porphyry copper (Cu) example from the Chahargonbad district, SE Iran. The results show that the GWO-based framework not only identifies high-priority exploration zones but also reduces the uncertainty inherent in traditional manual selection methods. As such, this novel approach contributes to both theoretical and practical advancements in the field of mineral exploration, offering a scalable solution that can be adapted to various geological settings.

关键词： Mineral prospectivity mapping gray wolf optimization algorithm Mineral exploration targeting

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Human identification using palm print images based on deep learning methods and gray wolf optimization algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第1期18卷 961-973页

作者： Alshakree, Firas Akbas, Ayhan Rahebi, Javad Cankiri Karatekin Univ Dept Comp Engn Cankiri Turkiye Abdullah Gul Univ Dept Comp Engn Kayseri Turkiye Istanbul Topkapi Univ Dept Software Engn Istanbul Turkiye

Palm print identification is a biometric technique that relies on the distinctive characteristics of a person's palm print to distinguish and authenticate their identity. The unique pattern of ridges, lines, and other features present on the palm allows for the identification of an individual. The ridges and lines on the palm are formed during embryonic development and remain relatively unchanged throughout a person's lifetime, making palm prints an ideal candidate for biometric identification. Using deep learning networks, such as GoogLeNet, SqueezeNet, and AlexNet combined with gray wolf optimization, we achieved to extract and analyze the unique features of a person's palm print to create a digital representation that can be used for identification purposes with a high degree of accuracy. To this end, two well-known datasets, the Hong Kong Polytechnic University dataset and the Tongji Contactless dataset, were used for testing and evaluation. The recognition rate of the proposed method was compared with other existing methods such as principal component analysis, including local binary pattern and Laplacian of Gaussian-Gabor transform. The results demonstrate that the proposed method outperforms other methods with a recognition rate of 96.72%. These findings show that the combination of deep learning and gray wolf optimization can effectively improve the accuracy of human identification using palm print images.

关键词： Human identification Palm print images Deep learning gray wolf optimization algorithm

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A fast convergence strategy based on gray wolf optimization algorithm for co-estimation of battery state of charge and capacity

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ELECTROCHIMICA ACTA 2024年 474卷

作者： Lei, Mingdong Wu, Bin Li, Peng Yang, Wenyao Xu, Jianhua Yang, Yajie Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Chongqing Univ Arts & Sci Sch Elect Informat & Elect Engn Chongqing 402160 Peoples R China Chongqing Univ Arts & Sci Chongqing Engn Res Ctr New Energy Storage Devices Chongqing 402160 Peoples R China Univ Elect Sci & Technol China Sch Optoelect Sci & Engn Chengdu 610054 Peoples R China

Under the same initial noise covariance, the extended Kalman filter (EKF) algorithm and the adaptive extended Kalman filter (AEKF) algorithm show different convergence rates in the battery state estimation experiments. It can be concluded from the experimental results that the convergence rate of the EKF algorithm is usually faster than that of the AEKF algorithm, but the estimation accuracy of the AEKF algorithm is usually better than that of the EKF algorithm. In response to this issue, this article proposes a fast convergence strategy based on the grey wolf optimization (GWO) algorithm for the co-estimation of battery SOC and capacity. Based on battery parameter identification, the proposed algorithm utilizes the GWO to optimize the initial noise covariance of the EKF algorithm. Then, the EKF algorithm with optimized initial noise covariance is used to quickly pull the SOC estimation results into a stable region and switch to the AEKF algorithm to jointly estimate the SOC and capacity of the battery. Through data validation under different working conditions, it is shown that the proposed target algorithm has a much faster convergence ability than the comparison algorithm, and the proposed algorithm also exhibits excellent robustness under different initialization errors and temperatures.

关键词： Initial noise covariance Adaptive extended Kalman filtering Convergence speed gray wolf optimization algorithm State of charge

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Leakage Source Location of Hazardous Chemicals Based on the Improved gray wolf optimization algorithm

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2024年第3期28卷 484-493页

作者： Chen, Zeng-Qiang Wang, Yi-Meng Qi, Cong-Cong Zheng, Shao-Kun Beijing Inst Petrochem Technol Informat Engn Coll 19 Qingyuan North Rd Beijing 102600 Peoples R China

To accurately determine the leakage source location and strength during gas leakage accidents, this study compares the concentration obtained from the diffusion model with that measured by the sensor and proposes an improved gray wolf optimization algorithm for leakage source location. This algorithm introduces two improvement strategies. First, a nonlinear convergence factor is introduced to balance the global and local searches of the algorithm. Second, a reverse learning operation is performed on the three individuals with the worst fitness in the contemporary population. The results showed that the location results based on the improved gray wolf optimization algorithm exhibited high accuracy and stability, could quickly and accurately locate the leakage source, and provided data support for emergency disposal of accidents.

关键词： source inversion Gaussian diffusion model leakage source location gray wolf optimization algorithm

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Intelligent book recommendation system for libraries supported by gray wolf optimization algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Wang, Jinying Liang, Yuhua Zhao, Jialin Dezhou Univ Lib 566Univ West Rd Dezhou 253023 Peoples R China Dezhou No 1 Sr High Sch Dezhou Peoples R China

This paper presents a library book recommendation system designed to improve effectiveness, utilizing the GWO algorithm. The system architecture consists of three distinct layers: the foundational data layer, the data processing layer, and the intelligent service. The improved CGWO-KM algorithm is used to cluster project attributes, and the search and update mechanism of the gray wolf population is applied to find better initial clustering centers. Missing rating data is then filled in, and user similarity is calculated. A harmonized weighting factor is used to eliminate the correlation between ratings from different users. The weighted rating mechanism comprehensively considers both user ratings and the influence of neighboring users. The improved Pearson correlation coefficient combines the weighting factors with user similarity to obtain the final recommendation score, completing the intelligent book recommendation process for the library's books. The results show that at the 5th month time snapshot, the predicted data (6) for the library's historical borrowing dataset closely matches the actual value (3.7). The method proposed in this paper demonstrates an IGD mean close to the true optimal solution across various library datasets for literature, science popularization, history, art, and novels, with values of 0.0012, 0.0023, 0.0014, 0.0021, and 0.0020, respectively. The optimal non-dominated solutions in the three-dimensional space for resource utilization, recommendation diversity, and user engagement are close to the ideal value of 1. Moreover, the book recommendation system has a short processing time, and the recommendation accuracy ranges from 0.882 to 0.993, providing personalized, high-quality book recommendation services for readers.

关键词： gray wolf optimization algorithm library book intelligent recommendation clustering project attributes user similarity Pearson correlation coefficient

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Deep Convolutional Neural Network and gray wolf optimization algorithm for Speech Emotion Recognition

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第1期42卷 449-492页

作者： Falahzadeh, Mohammad Reza Farokhi, Fardad Harimi, Ali Sabbaghi-Nadooshan, Reza Islamic Azad Univ Dept Elect Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Biomed Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Elect Engn Shahrood Branch Shahrood Iran

Speech emotion recognition (SER), an important method of emotional human-machine interaction, has been the focus of much research in recent years. Motivated by powerful Deep Convolutional Neural Networks (DCNNs) to learn features and the landmark success of these networks in the field of image classification, the present study aimed to prepare a pre-trained DCNN model for SER and provide compatible input to these networks by converting a speech signal into a 3D tensor. First, using a reconstructed phase space, speech samples are reconstructed in a 3D phase space. Studies have shown that the patterns formed in this space contain meaningful emotional features of the speaker. To provide an input that is compatible with DCNN, a new speech signal representation called Chaogram was introduced as the projection of these patterns, and three channels similar to RGB images were obtained. In the next step, image enhancement techniques were used to highlight the details of Chaogram images. Then, the Visual Geometry Group (VGG) DCNN pre-trained on the large ImageNet dataset is utilized to learn Chaogram high-level features and corresponding emotion classes. Finally, transfer learning is performed on the proposed model, and the presented model is fine-tuned on our datasets. To optimize the hyper-parameter arrangement of architecture-determined CNNs, an innovative DCNN-GWO (gray wolf optimization) is also presented. The results of this study on two public datasets of emotions, i.e., EMO-DB and eNTERFACE05, show the promising performance of the proposed model, which can greatly improve SER applications.

关键词： Speech emotion recognition 3D tensor speech representation Chaogram Deep convolutional neural network gray wolf optimization algorithm

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optimization of LSTM based on gray wolf optimization algorithm for Part Error Compensation 5

Optimization of LSTM based on Gray Wolf Optimization Algorit...

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5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Yang, Chengju Wang, Wu Lin, Tao Zhou, Shen Zhang, Ling Huang, Junxiang MinBei Vocat & Tech Coll Dept Food Nanping Peoples R China FuZhou Univ Coll Elect Engn & Automot Fuzhou Peoples R China

ISBN: (纸本)9798350363272;9798350363265

In the field of precision manufacturing, error compensation of parts is the key to improve product quality and manufacturing efficiency. This paper presents a Long Short-Term Memory Network (LSTM) model based on the gray wolf optimization algorithm designed to optimize part error compensation. First, we introduce the sources of part errors and their impact on the manufacturing process. Then, we elaborate the application of LSTM network in predicting and compensating part errors by selecting appropriate features through correlation analysis. Through experiments, we verify the effectiveness of the gray wolf optimization-based LSTM model in part error prediction and compensation. The experimental results show that compared with the traditional method, the model in this paper has a significant improvement in both error prediction accuracy and compensation efficiency.

关键词： Part error compensation gray wolf optimization algorithm Long and short-term memory networks Error prediction

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GWO-SA: gray wolf optimization algorithm for Service Activation Management in Fog Computing

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IEEE ACCESS 2022年 10卷 107846-107863页

作者： Hashemi, Sayed Mohsen Sahafi, Amir Rahmani, Amir Masoud Bohlouli, Mahdi Islamic Azad Univ Dept Comp Engn Qeshm Branch Qeshm Iran Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu 64002 Yunlin Taiwan Inst Adv Studies Basic Sci Dept Comp Sci & Informat Technol Zanjan *** Iran Petanux GmbH Res & Innovat Dept D-53129 Bonn Germany Inst Adv Studies Basic Sci IASBS Res Ctr Basic Sci & Modern Technol REST Zanjan *** Iran

Although fog computing is a new research topic, there are robust and integrated solutions for service activation management and how to distribute IoT services over available fog computing services resources. This paper presents a multi-objective gray wolf optimization (GWO) solution for more efficient fog computing service scheduling and activation management. This solution utilizes a multi-objective function in the resource allocation process by developing and improving the proposed algorithm to check the status of resources and manage tasks. The main purpose of this study is to create a trade-off between energy consumption and task execution time. This research has presented a two-stage multi-objective approach to solve the scheduling and task offloading problem. First, the GWO algorithm is used to solve the scheduling problem, and then container migration is used to solve the task offloading problem and appropriate resource allocation. Container migration causes an idle physical server to turn off, reducing power consumption, improving imbalance, reducing latency, and improving efficiency. The proposed method has been compared with three scenarios with 700 nodes, 1000 nodes, and 5000 nodes. The proposed solution has been implemented and simulated in iFogSim and compared with five classical algorithms. The analytical results indicate the better performance of the proposed strategy with average execution time for host selection averages a reduction of 15, 20, 25, 20, and 21%, respectively, compared to Particle swarm optimization (PSO), Ant colony optimization (ACO), Grasshopper optimization algorithm (GOA), Genetic algorithm (GA), and Cuckoo optimization algorithm (COA), 12% reduction in average time required for reallocation container, and the service-level agreement (SLA) violation rate is maintained in the range of 9-10/% compared to all other solutions.

关键词： Containers Edge computing Computer architecture Task analysis Computational modeling Virtualization Delays Service activation fog computing energy consumption gray wolf optimization algorithm container

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Android Malware Classification with gray wolf optimization algorithm and Deep Neural Network Hybrid Approach 30

Android Malware Classification with Gray Wolf Optimization A...

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30th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Gullu, Merve Barisci, Necattin Gazi Univ Bilgisayar Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781665450928

With the rapid development of technology and the increase in the use of Android software, the number of malware has also increased. This study presents a classification as malware/goodware with the features of 4465 Android applications. Cost is an important problem for the increasing number of applications and the analyzes to be made on each application. This study focused on this problem with the hybrid use of gray wolf optimization algorithm (GWO) and Deep Neural Networks (DNN). With the use of GWO, both feature selection and the features of the model to be created with DNN are determined. In this way, an approximate solution proposal is presented for the most suitable features and the most suitable model design. The model, which was created with the use of GWO-DNN hybrid in this study, offers an F1 score of 99.74%.

关键词： gray wolf optimization algorithm Deep Neural Network Android malware classification

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Enhancement and Investigation of a gray wolf optimization algorithm Utilizing a Hybrid Strategy

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ENGINEERING LETTERS 2025年第5期33卷 1572-1591页

作者： Deng, Xitong Wang, Yukun Meng, Qingyao Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Liaoning Peoples R China Univ Sci & Technol Liaoning Sch Qingdao Engn Vocat Coll Qingdao 266112 Shandong Peoples R China

The gray wolf optimization (GWO) algorithm, a robust metaheuristic inspired by the hunting behavior of gray wolves, has found extensive application in diverse optimization challenges. However, despite its notable success, GWO frequently encounters limitations such as premature convergence and diminished accuracy in complex scenarios. To mitigate these issues, this paper proposes a Hybrid Strategy-based gray wolf optimization (HSGWO) algorithm. The HSGWO algorithm utilizes Tent chaotic mapping to replace conventional random initialization, thereby enhancing global exploration and expediting convergence. Additionally, it incorporates the scaling factor strategy from Differential Evolution to balance global and local search, thus improving adaptability to intricate optimization problems. A random mutation strategy is also integrated to perturb updated positions, enabling the algorithm to circumvent premature convergence and escape local optima, thereby enhancing overall optimization performance. To assess the efficacy of HSGWO, comprehensive experiments were conducted on the CEC2017 benchmark functions and four real-world engineering problems. The results demonstrate that HSGWO significantly surpasses existing algorithms across multiple metrics, including convergence speed, accuracy, precision, and global optimization capability. Statistical analyses employing Friedman's and Wilcoxon signed-rank tests further corroborate the superior performance of HSGWO, particularly in addressing complex engineering optimization challenges.

关键词： gray wolf optimization algorithm Diversity initialization Scaling factor optimization Stochastic perturbation

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