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Evaluation of regional green innovation performance in China using a support vector machine-based model optimized by the chaotic grey wolf algorithm

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2025年第1期27卷 293-307页

作者： Zhao, Pengyi Cai, Yuanying Chen, Liwen Li, Qing Dai, Fuqiang Chongqing Technol & Business Univ Sch Econ Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Sch Publ Adm Chongqing 400067 Peoples R China

The green innovation performance (GIP) evaluation helps to identify strengths and weaknesses in regional innovation systems and has been crucial for policymakers in developing appropriate regional policies. Recent methodology has focused on establishing an indicator framework and calculating composite scores. It is noteworthy that the non-linear relationship between evaluation scores and indicators is rarely considered. In view of this, an evaluation model was proposed in the study which combines support vector machine (SVM) and chaotic grey wolf algorithm (CGWO). Sixteen indicators from the indicator system of European Innovation Scoreboard were retained for the GIP evaluation with an initial screening of indicators using the information entropy method. Then, four different types of optimization algorithms were used to optimize the SVM to generate non-linear predictions and GIP scores. The applicability of the model was verified for the GIP evaluation of China's provinces. According to the training and test results, the SVM-CGWO model achieved significantly better performance than the other three algorithms, which has important benefits in improving the uniformity of the wolf distribution and the traversal of the wolf pack, together with enhancing operation speed and accuracy. It helps users to rank and benchmark regional GIP at the provincial level, taking into account performance improvement and accuracy of dimensions, as well as reliability issues. [GRAPHICS] .

关键词： Green innovation performance Evaluation model Support vector machine Chaotic sequence grey wolf algorithm

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A Novel Hybrid Method Using grey wolf algorithm and Genetic algorithm for IoT Botnet DDoS Attacks Detection

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-61页

作者： Maazalahi, Mahdieh Hosseini, Soodeh Shahid Bahonar Univ Kerman Fac Math & Comp Dept Comp Sci Kerman Iran

The Internet of Things (IoT) is a vast network of interconnected physical objects that has improved the conditions for a computer-based physical world and improved efficiency. With the increase in communication in an IoT system, Internet security has decreased, and the most dangerous and sophisticated attacks in the IoT have emerged, i.e., DDoS and Botnet attacks. DDoS attacks are a serious threat to the availability of Internet services, especially since botnets can now be launched by almost anyone. In this situation, the use of an intrusion detection system (IDS) is essential to detect intruders and maintain the security of IoT networks. In this paper, a new IDS is proposed to detect IoT-Botnet DDoS attacks. This IDS is a new three-phase system, the first phase is related to preprocessing on the dataset and the second phase includes a new hybrid method for feature selection using filter and wrapper methods based on the grey wolf (GW) algorithm and genetics called GW-GA. In this method, the initial population is randomly selected and then at each stage, feature selection is done by both algorithms simultaneously and the final answer is compared and the best solutions are given as a new population to both algorithms and the third phase includes the use of machine learning and metaheuristic algorithms as classifiers. In the proposed method and to verify the performance, it is evaluated using the large BOT-IoT dataset. The results show that the proposed method significantly reduces the feature and also increases the classification accuracy compared to other methods, and the RF and Bagging algorithms have achieved a maximum recognition accuracy of 0.999. The dimensions of BOT-IoT have been reduced from 46 features to 12.

关键词： Evolutionary algorithms Genetics algorithm grey wolf algorithm Botnet attack DDOS Meta-heuristic algorithm Machine learning algorithms

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Discrete Multi-Objective grey wolf algorithm Applied to Dynamic Distributed Flexible Job Shop Scheduling Problem with Variable Processing Times

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APPLIED SCIENCES-BASEL 2025年第5期15卷 2281-2281页

作者： Chen, Jiapeng Wang, Chun Xu, Binzi Liu, Sheng Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China Anhui Polytech Univ Sch Elect Engn Wuhu 241000 Peoples R China

Uncertainty in processing times is a key issue in distributed production;it severely affects scheduling accuracy. In this study, we investigate a dynamic distributed flexible job shop scheduling problem with variable processing times (DDFJSP-VPT), in which the processing time follows a normal distribution. First, the mathematical model is established by simultaneously considering the makespan, tardiness, and total factory load. Second, a chance-constrained approach is employed to predict uncertain processing times to generate a robust initial schedule. Then, a heuristic scheduling method which involves a left-shift strategy, an insertion-based local adjustment strategy, and a DMOGWO-based global rescheduling strategy is developed to dynamically adjust the scheduling plan in response to the context of uncertainty. Moreover, a hybrid initialization scheme, discrete crossover, and mutation operations are designed to generate a high-quality initial population and update the wolf pack, enabling GWO to effectively solve the distributed flexible job shop scheduling problem. Based on the parameter sensitivity study and a comparison with four algorithms, the algorithm's stability and effectiveness in both static and dynamic environments are demonstrated. Finally, the experimental results show that our method can achieve much better performance than other rules-based reactive scheduling methods and the hybrid-shift strategy. The utility of the prediction strategy is also validated.

关键词： distributed flexible job shop dynamic scheduling variable processing time grey wolf algorithm heuristic

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grey wolf algorithm-Based Clustering Technique

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JOURNAL OF INTELLIGENT SYSTEMS 2017年第1期26卷 153-168页

作者： Kumar, Vijay Chhabra, Jitender Kumar Kumar, Dinesh Thapar Univ Comp Sci & Engn Dept Patiala Punjab India Natl Inst Technol Dept Comp Engn Kurukshetra Haryana India GJUS & T Comp Sci & Engn Dept Hisar Haryana India

The main problem of classical clustering technique is that it is easily trapped in the local optima. An attempt has been made to solve this problem by proposing the grey wolf algorithm (GWA)-based clustering technique, called GWA clustering (GWAC), through this paper. The search capability of GWA is used to search the optimal cluster centers in the given feature space. The agent representation is used to encode the centers of clusters. The proposed GWAC technique is tested on both artificial and real-life data sets and compared to six well-known metaheuristic-based clustering techniques. The computational results are encouraging and demonstrate that GWAC provides better values in terms of precision, recall, G-measure, and intracluster distances. GWAC is further applied for gene expression data set and its performance is compared to other techniques. Experimental results reveal the efficiency of the GWAC over other techniques.

关键词： grey wolf algorithm data clustering K-means metaheuristics

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An Intelligent Ellipsoid Calibration Method Based on the grey wolf algorithm for Magnetic Compass

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Journal of Bionic Engineering 2021年第2期18卷 453-461页

作者： Xusheng Lei Xiaoyu Zhang Yankun Hao School of the Instrumentation Science and Opto-Electronic Engineering Beihang UniversityBeijing 100191China

With the measurement of the Earth’s magnetic field,magnetic compass can provide high frequency heading ***,it suffers from local magnetic *** intelligent ellipsoid calibration method based on the grey wolf is proposed to generate optimal parameters for magnetic compass to generate high performance heading *** the analysis of the projection relationship among the navigation coordinate frame,the body frame and the local horizontal frame,the heading ellipsoid equation is ***,an improved grey wolf algorithm is proposed to find optimization solution in a large solution *** the improvement of the convergence factor and the evolutionary mechanism,the improved grey wolf algorithm can generate optimized solution for heading ellipsoid *** effectiveness of the proposed method has been verified by a series of vehicle and flight *** experimental results show that the proposed method can eliminate errors caused by sensor defects,hard-iron interference,and soft-iron interference *** heading error generated by the magnetic compass is less than 0.2162 degree in real flight tests.

关键词： magnetic compass ellipsoid parameters grey wolf algorithm error model

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Molecular cancer classification method on microarrays gene expression data using hybrid deep neural network and grey wolf algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第5期14卷 5297-5307页

作者： Hajieskandar, AliReza Mohammadzadeh, Javad Khalilian, Majid Najafi, Ali Islamic Azad Univ Karaj Branch Dept Comp Engn Karaj Iran Baqiyatallah Univ Med Sci Syst Biol & Poisonings Inst Mol Biol Res Ctr Tehran Iran

Gene selection methods are critical in cancer classification, which depends on the expression of a small number of biomarker genes, which have been a significant issue of enormous recent studies. Microarray technology allows generating tumors gene expression datasets. Cancer classification based on these datasets commonly has a kind of small sample size against the number of genes involved and includes multiclass categories. In this paper, grey wolf algorithm was used for extracting notable features in the pre-processing stage, and deep neural network (DNN) was used as deep learning for improving the accuracy degree of cancer detection from three datasets, i.e., STAD (Stomach adenocarcinoma), LUAD (lung adenocarcinoma) and BRCA (breast invasive carcinoma). The proposed method achieved the highest accuracy for these three datasets. The proposed method was able to achieve accuracy close to 100. Furthermore, the proposed method was compared with linear support vector machine classification, RBF, the nearest neighbor, linear regression, one vs. all, Naive Bayes, and decision tree algorithms. The proposed method had 0.57 improvement on the LUAD dataset, 1.11 optimization on the STAD dataset, and 0.78 development on the BRCA dataset.

关键词： Cancer classification DNA Microarray Deep neural networks grey wolf algorithm Deep learning

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Evaluate Multi-Objective Optimization Model for Product Supply Chain Inventory Control Based on grey wolf algorithm

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JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING 2024年第1期36卷 1页

作者： Zhang, Xuan Li, Yinan Fortes, Dilma Janete Mour, Anabela Southeast Univ Sch Arts Nanjing Peoples R China Polytech Inst Viana do Castelo Viana Do Castelo Portugal Higher Sch Educ Viana do Castelo Portugal

Enterprise supply chain inventory control plays a critical role in modern enterprise management. Yet achieving multi-objective optimization remains challenging. This paper proposes a deep learning integrated model based on grey wolf optimization (GWO-DLIM) aimed at optimizing inventory holding costs, stockout costs, and order costs. The model integrates multi-layer perceptron (MLP), DeBERTa, and GWO, leveraging the powerful predictive and feature extraction capabilities of deep learning models, combined with the global optimization ability of the grey wolf algorithm, to achieve multi-objective optimization. These results demonstrate that the GWO-DLIM model significantly outperforms other comparative models across all metrics, exhibiting lower prediction errors and root mean square errors, as well as higher service levels. This study provides an effective multi-objective optimization solution for supply chain inventory management, holding significant theoretical and practical implications.

关键词： Deep Learning Enterprise Decision Optimization grey wolf algorithm Inventory Control Multi-Objective Optimization Product Supply Chain Evaluate

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Parameter Estimation for Phase and Frequency Synchronization of the Single Phase Full-Bridge Photovoltaic Grid-Connected Inverter Using New Chaotic grey wolf algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2021年 1-12页

作者： Farjami, Ali Akbar Yaghoobi, Mahdi Islamic Azad Univ Dept Elect Engn Mashhad Branch Mashhad Razavi Khorasan Iran

Photovoltaic systems are widely used due to their low maintenance cost and not polluting the environment. In this paper, parameter estimation, phase and frequency synchronization of the single phase full-bridge PV Grid-Connected inverter is studied. System identification is the first step before control and synchronization. Selecting proper parameters for the inverter is important for its stable performance. Therefore, this paper investigates system identification through estimating parameters of the inverter. Inverter is an essential part of PV systems which affects its power quality significantly. In addition, chaotic behavior can affect its performance negatively. Due to chaotic behavior of this system, phase and frequency synchronization is realized through proper selection of correlation coefficient and PV voltage. In this paper, a new adaptive chaotic grey wolf optimization algorithm is proposed for parameter estimation in order to achieve phase and frequency synchronization. The results indicate superiority of the proposed method in terms of accuracy and speed of parameter estimation, phase and frequency synchronization.

关键词： Synchronization Full-bridge Photovoltaic grey wolf algorithm Chaos theory

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Improved grey wolf algorithm based on dynamic weight and logistic mapping for safe path planning of UAV low-altitude penetration

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JOURNAL OF SUPERCOMPUTING 2024年第18期80卷 25818-25852页

作者： Wang, Siwei Zhu, Donglin Zhou, Changjun Sun, Gaoji Zhejiang Normal Univ Coll Comp Sci & Technol Jinhua 321004 Peoples R China Zhejiang Normal Univ Coll Econ & Management Jinhua 321004 Peoples R China

Unmanned aerial vehicle (UAV) has been widely used in many fields, especially in low-altitude penetration defence, which showcases superior performance. UAV requires obstacle avoidance for safe flight and must adhere to various flight constraints, such as altitude changes and turning angles, during path planning. Excellent flight paths can enhance flight efficiency and safety, saving time and energy when performing specific tasks, directly impacting mission accomplishment. To address these challenges, this paper improves the original grey wolf algorithm (GWO). In this enhanced version, the three head wolves randomly assign influence weights to execute the position updating mechanism. A dynamic weight influence strategy is designed, which accelerates convergence in the late optimization stages, aiding in finding the global optimum. Meanwhile, the logistic mapping is introduced into the convergence factor, and a micro-vibrational convergence factor is constructed. This allows the algorithm to have a better ability to find a globally optimal solution in the search space while also being able to search deeper using areas near the currently known information. In order to validate the proposed algorithm, a simulated flight environment is established, conducting simulation experiments within safe flight environments featuring 5, 10, and 15 obstacles. Comparative analysis with seven other algorithms demonstrates the superiority of the proposed algorithm. The experimental results demonstrate that the proposed algorithm has better superiority. In terms of path length on three maps, DLGWO paths are 10.3 km, 15.5 km, and 2.6 km shorter than the second-placed MEPSO, SOGWO, and WOA, respectively. Furthermore, the planned path in this study exhibits the smallest fluctuations in altitude and turning angles.

关键词： UAV path planning grey wolf algorithm Convergence factor Learning factor Logistic mapping

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Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor

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IET RENEWABLE POWER GENERATION 2022年第16期16卷 3519-3530页

作者： Zaniani, Ali Amiri Nafar, Mehdi Simab, Mohsen Islamic Azad Univ Marvdasht Branch Dept Elect Engn Marvdasht Iran

The present study tries to propose a new method which is using Central deference Kalman Filter (CDKF) as input index of deep machine learning (DML), for simulating state estimation and broken rotor bars (BRBs) diagnosis in induction motors (IMs). In addition, an advanced selective ensemble (DML) method applying the grey wolf optimization (GWO) algorithm to diagnose the BRBs in IMs is proposed in the following study. Here, in the initial step, in order to take sufficient datum within the usual efficiency, it is needed to train the DML network. The outcomes indicate that the offered scheme can more accurately and powerfully detect diverse forms of BRBs with an accuracy of more than 98%. And also, Filter precision is enhanced via changing the sigma points of the filter, however, the stability of the filter enhances more because of its utilization, and the CDKF is further stable and precise in comparison to the Unscented Kalman filter (UKF). The CDKF performance is assessed to estimate the speed and is used as input index of DML to diagnose the broken rotor bar in IMs. The obtained outcomes prove the performance of this combined scheme.

关键词： Central deference Kalman Filter CDKF Filter precision learning (artificial intelligence) broken rotor bars diagnosis deep machine learning induction motors BRBs broken rotor bar broken rotor bars detection Unscented Kalman filter grey wolf optimization algorithm rotors central deference Kalman filter induction motor Kalman filters state estimation advanced selective ensemble method IMs grey wolf algorithm fault diagnosis input index DML network nonlinear filters

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