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Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems

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KNOWLEDGE-BASED SYSTEMS 2021年 223卷 107044-107044页

作者： Seyyedabbasi, Amir Aliyev, Royal Kiani, Farzad Gulle, Murat Ugur Basyildiz, Hasan Shah, Mohammed Ahmed Beykent Univ Fac Engn & Architecture Dept Comp Engn Istanbul Turkey Istanbul Aydin Univ Fac Engn Dept Comp Engn Istanbul Turkey Istanbul Arel Univ Fac Engn & Architecture Dept Comp Engn Istanbul Turkey Istanbul Ayvansaray Univ Fac Engn Dept Comp Engn Istanbul Turkey Istinye Univ Fac Engn Software Engn Dept Istanbul Turkey

This paper introduces three hybrid algorithms that help in solving global optimization problems using reinforcement learning along with metaheuristic methods. Using the algorithms presented, the search agents try to find a global optimum avoiding the local optima trap. Compared to the classical metaheuristic approaches, the proposed algorithms display higher success in finding new areas as well as exhibiting a more balanced performance while in the exploration and exploitation phases. The algorithms employ reinforcement agents to select an environment based on predefined actions and tasks. A reward and penalty system is used by the agents to discover the environment, done dynamically without following a predetermined model or method. The study makes use of Q-Learning method in all three metaheuristic algorithms, so-called RLI-GWO, RLEx-GWO, and RLWOA algorithms, so as to check and control exploration and exploitation with Q-Table. The Q-Table values guide the search agents of the metaheuristic algorithms to select between the exploration and exploitation phases. A control mechanism is used to get the reward and penalty values for each action. The algorithms presented in this paper are simulated over 30 benchmark functions from CEC 2014, 2015 and the results obtained are compared with well-known metaheuristic and hybrid algorithms (GWO, RLGWO, I-GWO, Ex-GWO, and WOA). The proposed methods have also been applied to the inverse kinematics of the robot arms problem. The results of the used algorithms demonstrate that RLWOA provides better solutions for relevant problems. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Metaheuristic algorithm Reinforcement learning algorithm grey wolf optimization algorithm Whale optimization algorithm Q-learning

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An Optimized Self-Compensated Solution for Temperature and Strain Cross-Sensitivity in FBG Interrogators Based on Edge Filter

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SENSORS 2021年第17期21卷 5828-5828页

作者： Silveira, Mariana L. Rocha, Helder R. O. Antunes, Paulo F. C. Andre, Paulo S. B. Segatto, Marcelo E., V Frizera, Anselmo Diaz, Camilo A. R. Univ Fed Espirito Santo Elect Engn Dept Telecommun Lab LABTEL BR-29075910 Vitoria ES Brazil Univ Aveiro I3N P-3810193 Aveiro Portugal Univ Aveiro Phys Dept P-3810193 Aveiro Portugal Inst Telecomunicacoes Campus Univ Santiago P-3810193 Aveiro Portugal Univ Lisbon Inst Telecomunicacoes Inst Super Tecn Dept Elect & Comp Engn P-1049001 Lisbon Portugal

Optical fiber sensors based on fiber Bragg gratings (FBGs) are prone to measurement errors if the cross-sensitivity between temperature and strain is not properly considered. This paper describes a self-compensated technique for canceling the undesired influence of temperature in strain measurement. An edge-filter-based interrogator is proposed and the central peaks of two FBGs (sensor and reference) are matched with the positive and negative slopes of a Fabry-Perot interferometer that acts as an optical filter. A tuning process performed by the grey wolf optimizer (GWO) algorithm is required to determine the optimal spectral characteristics of each FBG. The interrogation range is not compromised by the proposed technique, being determined by the spectral characteristics of the optical filter in accordance with the traditional edge-filtering interrogation. Simulations show that, by employing FBGs with optimal characteristics, temperature variations of 30 degrees C led to an average relative error of 3.4% for strain measurements up to 700 mu epsilon. The proposed technique was experimentally tested under non-ideal conditions: two FBGs with spectral characteristics different from the optimized results were used. The temperature sensibility decreased by 50.8% as compared to a temperature uncompensated interrogation system based on an edge filter. The non-ideal experimental conditions were simulated and the maximum error between theoretical and experimental data was 5.79%, proving that the results from simulation and experimentation are compatible.

关键词： fiber optical sensors fiber Bragg gratings edge filter temperature cross-sensitivity grey wolf optimization algorithm

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Guided joint spectrum sensing and resource allocation using a novel random walk grey wolf optimization for frequency hopping cognitive radio networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2019年第13期32卷 e4032-e4032页

作者： Karthikeyan, Anbalagan Srividhya, Venkatasubramanian Kundu, Souvik Vellore Inst Technol Sch Elect & Commun Engn Vellore Tamil Nadu India Vellore Inst Technol Vellore 632014 Tamil Nadu India

Effective use of unused licensed spectrum by secondary nodes without causing any harmful interference to the primary users is one of the challenging task, and in this paper, a detailed description of joint spectrum sensing and resource allocation in cognitive radio (CR) networks is discussed. The CR system utility is maximized by joint spectrum sensing and channel resource allocation scheme based on random walk grey wolf optimization (RW-GWO) algorithm for frequency hopping cognitive radio-based networks. In this paper, the nodes will sense the presence of primary users in the same channel and move to another channel in the guidance of a guide node (GN) if primary signal is detected in the channel. In order to achieve collision-free communication, primary signals and secondary signals use the channels strategically and the mechanism is described in this paper. RW-GWO is used to derive the optimum sensing and data transmission schedules. It selects the sensing nodes to sense the spectrum and other nodes take part in transmitting the data. GNs not only guide the nodes to hop to the next channel if the primary channel is detected, but also distribute the nodes in different available secondary hopping channels. Simulation results show that reliable spectrum sensing and efficient channel allocation can be achieved in our proposed algorithm.

关键词： channel allocation cognitive radio grey wolf optimization algorithm optimization random walk

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Type recognition of partial discharge source based on PCA and GWO-SVM

Type recognition of partial discharge source based on PCA an...

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第33届中国控制与决策会议

作者： Li Li Chen Yuepeng Yang Guang Mao Cuimin Zhang Huajun Wuhan University of Technology School of Automation Hubei Electric Power Survey and Design Institute Co.LTD

As an important indication and manifestation of insulation failures in high-voltage equipment,the type identification of partial discharge（PD） is important for the assessment of the insulation state of electrical *** order to identify the type of partial discharge source accurately,this paper presents a method for partial discharge source type identification based on principal component analysis（PCA） and support vector machine（SVM） with grey wolf optimization algorithm（GWO）.The PCA method is used to select the three features out of 10 that best represent the original data,namely the phase,E/I and I/*** this basis,the SVM kernel function g and the non-negative penalty factor c are optimized by GWO to establish a support vector machine classification model based on the grey wolf optimization algorithm（GWO-S VM）.The results show that PCA is able to extract the main features well and that GWO-SVM can identify partial discharge source more accurately than the genetic algorithm-based support vector machine（GA-SVM）,multilayer perceptron（MLP） and k-nearest neighbor（kNN） classifier.

关键词： Principal component analysis support vector machines identification of partial discharge types grey wolf optimization algorithm k-Nearest neighbors multilayer perceptron

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Bearing Fault Prediction Based on Mixed Domain Features and GWO-SVM

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JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2024年第1期2024卷

作者： Zhou, Xuan Xia, Ruiyang Zhang, Zhaodong Duan, Sasa Cheng, Mao Zhou, Chengjiang Mao, Min Yunnan Normal Univ Sch Informat Sci & Technol Kunming 650500 Peoples R China Quzhou Coll Technol Fac Informat Engn Quzhou 324000 Peoples R China

The rotating machinery is composed of rolling bearing connection, so the fault identification of rolling bearing is a very critical task. We propose a bearing fault identification algorithm based on grey wolf optimizer (GWO) to address the common problems of high signal noise, inability of a single indicator to accurately reflect the true state of bearings, and optimization of support vector machine (SVM) prediction model parameters in bearing fault identification. First, the wavelet soft threshold is used to remove the noise of the original signal and then the empirical Fourier decomposition (EFD) is used in the decomposition and reconstruct signals. Second, in the aspect of feature extraction, the time and frequency domain features of the bearing data are selected to form the mixed domain features of the bearing signal. Finally, aiming at improving the bearing fault prediction accuracy, the GWO algorithm is used to optimize the parameters. Achievements: the signal-to-noise ratio can be effectively improved to 77.8 by using the wavelet denoising, and the parameter modeling optimized by the GWO algorithm can significantly improve the prediction accuracy, with an increase of about 3%-5%. It provides theoretical support for the optimization of bearing fault identification with this technology in the industrial field.

关键词： bearing fault identification empirical Fourier decomposition grey wolf optimization algorithm mixed domain characteristics support vector machine

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Virtual power plant capacity tariff pricing method based on master–slave game

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International Journal of Electrical Power & Energy Systems 2025年 169卷

作者： Shaobo Yin Weiqing Sun Haibing Wang School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai 200093 China

The large-scale construction of virtual power plants (VPPs) is a crucial method for alleviating the tight supply–demand balance during peak periods, necessitating capacity tariff incentives to encourage their development. Consequently, a VPP capacity tariff pricing method based on a master–slave game is proposed, considering the incentive effect of the capacity tariff on VPP construction. The grid is dominant within this framework, determining the capacity tariff based on construction capacity signals. VPP, as the follower, responds optimally to the leader’s pricing strategy to decide on construction capacity, with both parties influencing each other until equilibrium is reached. Based on this, the upper-level grid minimizes the operational cost of the power system by optimizing the values of capacity tariff and VPP output. The lower-level VPP aims for an annual return on investment (ROI) of 20%, thus determining the optimal construction capacity. Furthermore, the grey wolf optimization (GWO) algorithm is employed to solve the master–slave game problem. The proposed method’s effectiveness and rationality are verified through a real-world scenario of a regional grid. The results indicate that the master–slave game pricing method effectively simulates the interactive decision-making relationship between the grid and VPP sides, ensuring the rationality of VPP capacity tariff and construction capacity decisions, thereby providing valuable references for managers.

关键词： Virtual power plant Capacity tariff Master-slave game Peak-shaving demand response grey wolf optimization algorithm

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An adaptive deep transfer learning method for bearing fault diagnosis

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MEASUREMENT 2020年 151卷 107227-000页

作者： Wu, Zhenghong Jiang, Hongkai Zhao, Ke Li, Xingqiu Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

Bearing fault diagnosis has made some achievements based on massive labeled fault data. In practical engineering, machines are mostly in healthy and faults seldom happen, it's difficult or expensive to collect massive labeled fault data. To solve the problem, an adaptive deep transfer learning method for bearing fault diagnosis is proposed in this paper. Firstly, a long-short term memory recurrent neural network model based on instance-transfer learning is constructed to generate some auxiliary datasets. Secondly, joint distribution adaptation, a feature-transfer learning method, which is used to reduce the differences in probability distributions between an auxiliary dataset and target domain dataset. Finally, grey wolf optimization algorithm is introduced to adaptively learn key parameters of joint distribution adaptation. The proposed method is verified with two kinds of datasets, and the results demonstrate the effectiveness and robustness of the proposed method when the labeled fault data are scarce. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Feature-transfer learning Instance-transfer learning Long-short term memory recurrent neural network Joint distribution adaptation grey wolf optimization algorithm

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A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 139卷 112824-000页

作者： Abdel-Basset, Mohamed El-Shahat, Doaa El-henawy, Ibrahim de Albuquerque, Victor Hugo C. Mirjalili, Seyedali Zagazig Univ Fac Comp & Informat Dept Operat Res Zagazig Egypt Zagazig Univ Fac Comp & Informat Comp Sci Dept Zagazig Egypt Univ Fortaleza Fortaleza Ceara Brazil Torrens Univ Australia 90 Bowen Terrace Fortitude Valley Qld 4006 Australia

Because of their high dimensionality, dealing with large datasets can hinder the data mining process. Thus, the feature selection is a pre-process mandatory phase for reducing the dimensionality of datasets through using the most informative features and at the same time maximizing the classification accuracy. This paper proposes a new grey wolf Optimizer algorithm integrated with a Two-phase Mutation to solve the feature selection for classification problems based on the wrapper methods. The sigmoid function is used to transform the continuous search space to the binary one in order to match the binary nature of the feature selection problem. The two-phase mutation enhances the exploitation capability of the algorithm. The purpose of the first mutation phase is to reduce the number of selected features while preserving high classification accuracy. The purpose of the second mutation phase is to attempt to add more informative features that increase the classification accuracy. As the mutation phase can be time-consuming, the two-phase mutation can be done with a small probability. The wrapper methods can give high-quality solutions so we use one of the most famous wrapper methods which called k-Nearest Neighbor (k-NN) classifier. The Euclidean distance is computed to search for the k-NN. Each dataset is split into training and testing data using K-fold cross-validation to overcome the overfitting problem. Several comparisons with the most famous and modern algorithms such as flower algorithm, particle swarm optimization algorithm, multi-verse optimizer algorithm, whale optimization algorithm, and bat algorithm are done. The experiments are done using 35 datasets. Statistical analyses are made to prove the effectiveness of the proposed algorithm and its outperformance. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Feature selection grey wolf optimization algorithm Wrapper method Classifier, accuracy Cross-validation Mutation

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Point Cloud Registration algorithm Based on the grey wolf Optimizer

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IEEE ACCESS 2020年 8卷 143375-143382页

作者： Feng, Yaqing Tang, Jialin Su, Binghua Su, Qinglang Zhou, Zhuang Beijing Inst Technol Opt Engn Zhuhai 519088 Peoples R China Beijing Inst Technol Minist Educ China Key Lab Photoelect Imaging Technol & Syst Zhuhai 519088 Peoples R China Beijing Inst Technol Fac Informat Technol Zhuhai 519088 Peoples R China Beijing Inst Technol Beijing 100081 Peoples R China City Univ Macau Res Inst Data Sci Macau Peoples R China

In view of the long computation time and low registration accuracy of the current point cloud registration algorithm, a point cloud registration algorithm based on the grey wolf optimizer (GWO) is proposed, denoted PCR-GW. The algorithm uses the centralization method to solve the translation matrix and then simplifies the points of the initial point cloud models by using the intrinsic shape signatures (ISS) feature. Next, various parameters of the rotation matrix are obtained via the GWO algorithm by employing the quadratic sum of the distances between corresponding points in the simplified point cloud as the objective function. Finally, the point cloud registration process is completed by using the obtained transformation matrix. By conducting a registration experiment on the point cloud library model and comparing PCR-GW with the traditional algorithms, the algorithm proposed in this article is shown to be promising for improving the computation speed and registration accuracy.

关键词： Point cloud registration feature point extraction grey wolf optimization algorithm

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Improved grey wolf Optimizer and Their Applications 7

Improved Grey Wolf Optimizer and Their Applications

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7th IEEE International Conference on Computer Science and Network Technology (ICCSNT)

作者： Liang, Xu Wang, Di Huang, Ming Dalian Jiaotong Univ Dalian Peoples R China

ISBN: (纸本)9781728132990

grey wolf Optimizer is a kind of artificial intelligence optimization algorithm. Aiming at the problem of local optimum and low convergence accuracy of GWO, this paper uses reverse learning strategy to generate initial population to increase population diversity;and uses simulated annealing algorithm's strong ability to jump out of local optimum solution to make up for the shortcoming that GWO is easy to fall into local optimum;finally, the first three individuals of population fitness are mutated to improve the improvement of the algorithm. The speed and accuracy of the algorithm are improved to avoid falling into local optimum. The superiority of the improved algorithm is verified by simulation experiments.

关键词： grey wolf optimization algorithm job shop scheduling simulated annealing

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