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Predictive control of microwave hot-air coupled drying model based on GWO-BP neural network

DRYING TECHNOLOGY 2023年第7期41卷 1148-1158页

作者： Bai, Haoran Chu, Ziyi Wang, Dongwei Bao, Yan Qin, Liyang Zheng, Yuhui Li, Fengmei Qingdao Agr Univ Coll Mech & Elect Engn 700 Great Wall Rd Qingdao 266109 Peoples R China China Construct Zhongxin Construct Engn Co Ltd Chengdu Peoples R China

A GB-MPC control algorithm (GWO-BP-MPC) was proposed to solve the problem of precise temperature control of fruit and vegetable coupling drying devices. Firstly, the BP (Back Propagation) neural network was improved using the grey wolf Optimizer (GWO) algorithm to increase the relevance and accuracy of the prediction model. By means of an improved neural network, we developed a high-accuracy predictive model for temperature control of drying units. Secondly, the projection conjugate gradient method was proposed for nonlinear optimization of the control system to improve the solving speed and accuracy of the optimal solution. The GB-MPC control algorithm was compared with the PID controller. The experimental results shown that the convergence speed of GB-MPC control was faster, the time took to reach a steady state in a single stage was shortened by 47 seconds compared with PID control. In the control process, the temperature change range of the GB-MPC control algorithm was smaller and there was no overshoot problem, which gave a better control effect than PID.

关键词： grey wolf optimization algorithm BP neural network predictive Control projection conjugate gradient method fruit and vegetable drying

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Group learning algorithm: a new metaheuristic algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年第19期35卷 14013-14028页

作者： Rahman, Chnoor M. Charmo Univ Coll Sci Comp Dept Sulaymaniyah Iraq Amer Univ Iraq Sulaimani Informat Technol Dept Sulaymaniyah Iraq

Metaheuristics are intelligent optimization techniques that lead the searching procedure through utilizing exploitation and exploration. Increasing the number of hard problems with big data sets has encouraged researchers to implement novel metaheuristics and hybrid the existing ones to improve their performance. Hence, in this work, a novel metaheuristic called group learning algorithm is proposed. The main inspiration of the algorithm emerged from the way individuals inside a group affect each other, and the effect of group leader on group members. The two main steps of optimization, exploration and exploitation are outlined through integrating the behaviors of group members and the group leader to complete the assigned task. The proposed work is evaluated against a number of benchmarks. The produced results of classical benchmarks are compared against PSO, GWO, TLBO, BA, ALO, and SSA. In general, compared to other participated algorithms, out of 19 classical benchmarks, the proposed work showed better results in 11. However, the second best algorithm which is SSA performed better in 4 out of 19 benchmarks. To further evaluate the ability of the algorithm to optimize large scale optimization problems CEC-C06 2019 benchmarks are utilized. In comparison to other participated algorithms, the proposed work produced better results in most of the cases. Additionally, the statistical tests confirmed the significance of the produced results. The results are evidence that the proposed algorithm has the ability to optimize various types of problems including large scale optimization problems.

关键词： optimization Single objective optimization Benchmark Particle swarm optimization grey wolf optimization algorithm Teaching learning based optimization Population based algorithm Evolutionary algorithm

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Learner Emotional Value Recognition Technology: An Artificial Neural Network Optimized by the grey wolf algorithm

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IEEE ACCESS 2023年 11卷 127689-127696页

作者： Chen, Qi Yang En Univ Law Sch Quanzhou 362014 Peoples R China

In cross-cultural communication, multimedia animation is crucial in defining a nation's image and cultural form. It is a key vehicle for cultural diffusion and a tool for film and television to convey national culture and highlight regional culture. Animation's particular charm, position, and function in cultural dissemination are further highlighted by the special cinematic language used to portray emotions, and it also reflects the medium's unique significance in the rapidly evolving modern society. To better understand the emotions generated by various multimedia animations, in-depth research is needed. To investigate these issues, this article explores the use of the Sigma-pi artificial neural network (SP-ANN) algorithm based on the grey wolf optimization algorithm (GWOA) to identify emotional states. Compared with traditional Sigma n-artificial neural network algorithms, a training process that does not require complex derivative calculations in derivative-based algorithms is performed. Sigma n-networks can benefit from the proposed learning algorithms. This algorithm has high approximation accuracy and is particularly suitable for real-time approximation of nonlinear processes. The test results indicate that the proposed algorithm can work as expected.

关键词： Animation Artificial neural networks Computational modeling Clustering algorithms Approximation algorithms Training Mathematical models Cultural aspects Multimedia systems Multimedia animation grey wolf optimization algorithm sigma pi artificial neural network algorithms

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Estimation of permeability from a hydrocarbon reservoir located in southwestern Iran using well-logging data and a new intelligent combined method

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CARBONATES AND EVAPORITES 2023年第1期38卷 1-14页

作者： Nazari, Hosnie Hajizadeh, Farnusch Urmia Univ Dept Min Engn Orumiyeh Iran

The most important tools of reservoir management are "description of reservoir properties " and "reservoir simulation ", among which permeability is the most important factor for accurate description and modeling of the reservoir. Standard method for determining permeability in oil industry is core analysis and well testing. These methods are very expensive and not all wells in a field have cores. As a result, the methods applicable to present the petrophysical properties of reservoir, including permeability and well-logging, are highly important because well logs are usually available for all wells in a field. Artificial intelligence methods are new, low-cost and accurate methods that can indirectly estimate the permeability of reservoir in the shortest possible time using well-logging data. Therefore, in this study, using different well logs and a new intelligent combined method of relevance vector regression with grey wolf optimization (RVR-GWO) algorithm, the permeability of a hydrocarbon reservoir in southwestern Iran (Azadegan oil field) was indirectly estimated. Then, the performance of this hybrid model was compared with that of relevance vector regression (RVR) method. The database consisted of 2506 well-logging data, which were divided into two categories of training data (1754 data) and test data to evaluate the models (752 data). The results showed very good performance of the combined method of RVR-GWO algorithm in estimating permeability. Therefore, this model can be used as a powerful, fast, and accurate method for indirect estimation of permeability in reservoirs where permeability through the core is not measured.

关键词： Permeability Well-logging data Relevance vector regression grey wolf optimization algorithm Hydrocarbon reservoir

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Unsupervised intelligent system based on one class support vector machine and grey wolf optimization for IoT botnet detection

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第7期11卷 2809-2825页

作者： Al Shorman, Amaal Faris, Hossam Aljarah, Ibrahim Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan

Recently, the number of Internet of Things (IoT) botnet attacks has increased tremendously due to the expansion of online IoT devices which can be easily compromised. Botnets are a common threat that takes advantage of the lack of basic security tools in IoT devices and can perform a series of Distributed Denial of Service (DDoS) attacks. Developing new methods to detect compromised IoT devices is urgent in order to mitigate the negative consequences of these IoT botnets since the existing IoT botnet detection methods still present some issues, such as, relying on labelled data, not being validated with newer botnets, and using very complex machine learning algorithms. Anomaly detection methods are promising for detecting IoT botnet attacks since the amount of available normal data is very large. One of the powerful algorithms that can be used for anomaly detection is One Class Support vector machine (OCSVM). The efficiency of the OCSVM algorithm depends on several factors that greatly affect the classification results such as the subset of features that are used for training OCSVM model, the kernel type, and its hyperparameters. In this paper, a new unsupervised evolutionary IoT botnet detection method is proposed. The main contribution of the proposed method is to detect IoT botnet attacks launched form compromised IoT devices by exploiting the efficiency of a recent swarm intelligence algorithm called grey wolf optimization algorithm (GWO) to optimize the hyperparameters of the OCSVM and at the same time to find the features that best describe the IoT botnet problem. To prove the efficiency of the proposed method, its performance is evaluated using typical anomaly detection evaluation measures over a new version of a real benchmark dataset. The experimental results show that the proposed method outperforms all other algorithms in terms of true positive rate, false positive rate, and G-mean for all IoT device types. Also, it achieves the lowest detection time, whi

关键词： Internet of Things Anomaly detection Botnets Feature selection Intrusion detection system grey wolf optimization algorithm Novelty detection One class support vector machine

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Real-time Driving Ability Prediction Model Based on GWO-LSTM 5

Real-time Driving Ability Prediction Model Based on GWO-LSTM

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

作者： Chen, Yongqing Xia, Yangyang Chen, Jiajia Chen, Jie Anhui Transportat Holding Grp Co Ltd Hefei Anhui Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9798350363272;9798350363265

With the development of intelligent transportation system, accurate prediction of driver's real-time driving ability has become the key to ensure road traffic safety and improve driving efficiency. This paper proposes a real-time driving ability prediction model based on grey wolf optimization algorithm (GWO) and long short-term memory neural network (LSTM). The model optimizes the network parameters of LSTM through GWO, improves the prediction accuracy and generalization ability, and compares it with support vector regression (SVR) and classification regression tree (CART) prediction models. The experimental results show that the average coefficient of determination of the GWO-LSTM model is 0.9949, which is greater than 0.9840 of the SVR model and 0.9602 of the CART model. The model has good performance in real-time driving ability prediction and provides strong support for the development of intelligent transportation systems.

关键词： driving behavior risk real-time driving ability prediction grey wolf optimization algorithm long short-term memory neural network

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A Hybrid Approach Based on LMP and VCG Mechanism to Create a Positive Interaction Between Distribution Companies and Owners of DG Units 12

A Hybrid Approach Based on LMP and VCG Mechanism to Create a...

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12th International Conference on Smart Grid (ICSmartGrid)

作者： Lahaghi, Seyed Amir Mohammad Zaker, Behrooz Gharehpetian, Gevork B. Nurmanova, Venera Bagheri, Mehdi Shiraz Univ Sch Elect & Comp Engn Shiraz Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran Nazarbayev Univ Dept Elect & Comp Engn Astana Kazakhstan

ISBN: (纸本)9798350361612;9798350361629

Nowadays, with the expansion of electric energy distribution networks and the increasing penetration of distributed generation (DG) units in these systems, the existence of a solution to create a positive interaction between distribution companies and owners of DGs is felt. In this paper, a hybrid locational marginal pricing (LMP)-based approach with Vickrey-Clarke-Groves (VCG) optimization is presented, focusing on the benefit of energy consumers. Initially, LMPs of DG units are optimized by the modified grey wolf optimization (MGWO) algorithm, which is combined with a decision tree (DT) model to identify the most optimal solutions in each iteration. Subsequently, the consumer benefit is calculated by comparing the initial market price and the obtained LMPs for the DGs, and ultimately, the profit of the DGs owners is determined based on the VCG mechanism. The simulation has been conducted on a California 201-node test distribution system under the MATLAB software. The simulation results showed the effectiveness of the proposed method in reducing network losses, assigning LMPs to DG units, and increasing the profits of DGs owners compared to previous methods.

关键词： Locational marginal pricing Vickrey-ClarkeGroves mechanism grey wolf optimization algorithm Decision tree Distribution systems

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A Surrogate-Based optimization Method for Solving Economic Emission Dispatch Problems with Green Certificate Trading and Wind Power 18th

A Surrogate-Based Optimization Method for Solving Economic E...

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18th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

作者： Lu, Chen Liang, Huijun Xie, Heng Lin, Chenhao Lu, Shuxin Hubei Minzu Univ Coll Intelligent Syst Sci & Engn Enshi 445000 Peoples R China Guoneng Changyuan Enshi Hydropower Dev Co Ltd Enshi 445099 Peoples R China

ISBN: (纸本)9789819722716;9789819722723

To reduce the impact of greenhouse effect, the deployment and utilization of renewable energy sources such as wind power has become an inevitable trend. Therefore, the decision of economic emission dispatch (EED) problems is particularly important. In this paper, to incentivize renewable energy generation, the green certificate trading mechanism is introduced to solve from the economic level. However, as the dimensions of the EED problems are increased, the current methods cannot make proper scheduling decisions in a short time. Therefore, the EED problems are categorized as computationally expensive EED problems. To solve the above problems, a surrogate-based multi-objective optimization method is proposed. On one hand, the artificial neural network (ANN) surrogate models are proposed to replace the traditional objective function, which greatly reduces the time to obtain feasible decisions. On the other hand, a modified multi-objective gray wolf optimizer (MOGWO) is proposed to execute EED optimization accurately and quickly. This algorithm improves the search ability and convergence of the original MOGWO algorithm through improving the position update strategy and introducing the difference algorithm. The effectiveness of the surrogate-based MOGWO is testified through simulations of benchmark functions and computationally expensive EED optimization problems within the actual Taipower 40-unit test system.

关键词： Economic emissions dispatch Random wind power grey wolf optimization algorithm Artificial neural network

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A Novel Chaotic Quantum grey wolf algorithm for Optimizing the Task Allocation of Water Quality Monitoring Sensor Networks 9

A Novel Chaotic Quantum Grey Wolf Algorithm for Optimizing t...

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9th International Conference on Automation, Control and Robotics Engineering (CACRE)

作者： Jia, Dingyi Zhu, Yuanjiao Wang, Mengfei Li, Jiahao Luo, Tao Wei, Jingyi Zhang, Baitao Zhou, Jie Shihezi Univ Coll Informat Sci & Technol Shihezi Peoples R China

ISBN: (纸本)9798350350319;9798350350302

Water quality monitoring sensor networks (WQMSNs) are crucial technological applications in environmental management, facilitating real-time monitoring and data collection of water quality. However, allocating tasks among sensor nodes in complex aquatic environments poses significant challenges to network efficiency. To tackle the task allocation problem in WQMSNs and enhance network performance, this paper introduces a novel Chaotic Quantum grey wolf algorithm (CQGWA). This algorithm incorporates a new chaotic operator and a quantum operator to optimize the task allocation of sensor nodes. The chaotic operators introduce randomness, helping to avoid local optima and enhancing the global search capability. Meanwhile, the quantum operators utilize quantum bit representation and quantum rotation gates to refresh the evolutionary search. They select predefined candidate solutions from the candidate states and seek the globally optimal predicted solution for a given objective function. Extensive simulation experiments were conducted to validate the effectiveness of CQGWA. The results demonstrate that compared to the traditional grey wolf algorithm (GWA) and Genetic algorithm (GA), the task allocation efficiency in WQMSNs optimized using CQGWA increases by at least 8.46%. This significantly improves the effectiveness of water quality monitoring and enhances the network's performance and reliability.

关键词： Wireless sensor networks target allocation model grey wolf optimization algorithm

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Research on optimization of Emergency Material Distribution in Two Stages during Sudden Public Health Emergencies 12

Research on Optimization of Emergency Material Distribution ...

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12th International Conference on Traffic and Logistic Engineering (ICTLE)

作者： Zhao YongQiang Fan Hang Xian Univ Posts & Telecommun Sch Modern Post Xian Peoples R China

ISBN: (数字)9798350362794

ISBN: (纸本)9798350362800;9798350362794

Following the outbreak of a sudden public health incident, the rational distribution of supplies is crucial for halting the spread of the event and ensuring the efficiency of the rescue efforts. This paper investigates the emergency supply distribution problem in the context of a sudden public health emergency. Initially, considering the differences among various demand points, an urgency evaluation index for demand points is constructed from four perspectives: personnel, environment, facilities, and supplies. Subsequently, a two-stage model for emergency supply allocation and route optimization is established based on four criteria: equity, time, efficiency, and economy. Furthermore, a grey wolf Optimizer is designed to solve the model, which is enhanced with strategies such as reverse learning, polynomial mutation, and simulated annealing concepts. Finally, the model and algorithm's effectiveness is validated using the COVID-19 epidemic in Wuhan as a simulation case study.

关键词： Public Health Emergencies Equity Demand Urgency Emergency Supply Distribution grey wolf optimization algorithm

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