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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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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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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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Parameter optimization of type II fuzzy sliding mode control for bridge crane systems based on improved grey wolf algorithm

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OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第5期45卷 2136-2152页

作者： Sun, Zhiqiang Sun, Zhe Xie, Xiangpeng Sun, Zhixin Nanjing Univ Posts & Telecommun Big Data Technol & Applicat Engn Res Ctr Jiangsu P Nanjing Peoples R China Nanjing Univ Posts & Telecommun Post Ind Technol Res & Dev Ctr State Posts Bur Internet Things Technol Nanjing 210023 Peoples R China

Bridge cranes are complex nonlinear dynamic systems with underactuated characteristics, making it challenging for controllers to man age the spatial swing of the load effectively. Additionally, uncertainties both within and outside the system adversely impact control performance. To address these issues, a Type-II fuzzy sliding mode controller has proven effective in enhancing the anti-swing control performance of the payload. However, due to the intricate parameter adjustment optimization problem and potential challenges in dealing with nonlinearity and uncertainty, especially in complex dynamic systems, this paper proposes a grey wolf algorithm based on a dynamic spiral hunting mechanism. This enhancement endows the algorithm with improved convergence speed and higher robustness, enabling more effective parameter tuning for the second-order fractional-order sliding mode controller (FSMC). The proposed algorithm demonstrates superior convergence speed and solution accuracy performance through testing and comparison. Finally, simulation verification under two conditions of the bridge crane system validates the effectiveness of the proposed approach. This paper proposes a grey wolf algorithm with a dynamic spiral hunting mechanism to enhance parameter tuning for the second-order fractional-order sliding mode controller (FSMC) in bridge crane systems. It addresses challenges in nonlinear dynamic systems, improving convergence speed and robustness. Testing shows superior performance in convergence speed and solution accuracy. Simulation verification confirms the effectiveness of the proposed approach in anti-swing control of bridge crane payloads, crucial for managing spatial swing under uncertainties. image

关键词： grey wolf algorithm overhead crane parameter optimization type-2 fuzzy sliding mode controller

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Virtual Inertia Optimization Allocation Based On grey wolf algorithm

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2024年第11期27卷 3453-3462页

作者： Wu, Genzhu Nan, Dongliang Duan, Yu Zhang, Lu Zhu, Ziming Chang, Xiqiang Xinjiang Univ Sch Elect Engn Urumqi 830046 Xinjiang Peoples R China State Grid Xinjiang Elect Power Co Ltd Elect Power Sci Res Inst Urumqi 831000 Xinjiang Peoples R China

To address the optimal allocation of virtual inertia as a replacement for rotating inertia in power systems, this paper proposes a virtual inertia optimal allocation method. First, the calculation method for determining the minimum inertia requirement of power systems with high penetration of renewable energy is clarified. Next, considering factors like frequency stability and virtual inertia investment costs, a virtual inertia optimization allocation model is constructed with the goals of minimizing the frequency security index and investment costs, subject to constraints such as critical inertia, rate of change of frequency, and maximum frequency deviation. And the grey wolf algorithm is utilized to solve this model. Finally, the modified WSCC 9 -bus system and IEEE 39 -bus system are simulated to validate the effectiveness and universality of the proposed model in optimally allocating virtual inertia while balancing frequency stability and investment costs.

关键词： virtual inertia critical inertia frequency stability investment cost grey wolf algorithm

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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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Joint optimization method of cell formation and layout problems with square constraints based on improved multi-objective grey wolf algorithm

Joint optimization method of cell formation and layout probl...

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2024 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2024

作者： Li, Rui Shi, Qian School of Mechanical Engineering Tongji University Shanghai China

ISBN: (纸本)9798350386097

To adapt to the real-life production environment, cell formation problems (CFPs) and cell layout problems (CLPs) with square constraints are simultaneously optimized. Firstly, CFPs and CLPs are formally described. To increase the accuracy of the inter- and intra-cell layout design, the coordinates are adopted and the material handling cost is calculated in terms of the actual position of machines within the cells and regarding the dimensions of the machines and aisle distances. To deal with the proposed problem, an improved multi-objective grey wolf algorithm with non-dominated sorting is proposed. Simulation experiments indicate that the proposed improved algorithm is feasible and effective. © 2024 IEEE.

关键词： Cell formation grey wolf algorithm layout problem coordinates

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Research on Frequency Support Technology of Offshore Wind Farm Based on Improved grey wolf algorithm

Research on Frequency Support Technology of Offshore Wind Fa...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Cai Hui Sun Wentao Peng Zhuyi Xu Sixuan Wang Quanquan Qi Wanchun State Grid Jiangsu Elect Power Co Ltd Econ & Tech Res Inst Nanjing Peoples R China

ISBN: (纸本)9798350339345

The power grid may experience a decrease in frequency response capabilities and encounter issues such as secondary frequency drops and wind turbines over-responding when large-scale offshore wind farms are integrated. This can have a serious impact on the stability of high-proportion renewable energy power systems. To address this problem, a coordinated control strategy that employs an improved droop control, power-based virtual inertia control, and the grey wolf algorithm is proposed in this paper. The strategy optimizes droop control tuning coefficients for various wind speed settings, improves control accuracy, enhances droop characteristics of wind turbines, reduces frequency overshoot during frequency regulation, and effectively tackles the issue of slow response that leads to frequency drop in the initial stages of frequency disturbance when traditional droop control is employed. This strategy serves as technical support for the development of a new power system that relies heavily on renewable energy.

关键词： large-scale offshore wind farms coordinated control strategy improved droop control power-based virtual inertia control grey wolf algorithm

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