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A novel grey wolf optimizer and its applications in 5G frequency selection surface design

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Frontiers of Information Technology & Electronic Engineering 2022年第9期23卷 1338-1353页

作者： Zhihao HE Gang JIN Yingjun WANG National Engineering Research Center of Novel Equipment for Polymer Processing South China University of TechnologyGuangzhou510641China Key Laboratory of Polymer Processing Engineering of the Ministry of Education South China University of TechnologyGuangzhou510641China Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing South China University of TechnologyGuangzhou510641China

In fifth-generation wireless communication system(5G),more connections are built between metaheuristics and electromagnetic equipment *** this paper,we propose a self-adaptive grey wolf optimizer(SAGWO)combined with a novel optimization model of a 5G frequency selection surface(FSS)based on FSS unit *** includes three improvement strategies,improving the initial distribution,increasing the randomness,and enhancing the local search,to accelerate the convergence and effectively avoid local *** benchmark tests,the proposed optimizer performs better than the five other optimization algorithms:original grey wolf optimizer(GWO),genetic algorithm(GA),particle swarm optimizer(PSO),improved grey wolf optimizer(IGWO),and selective opposition based grey wolf optimization(SOGWO).Due to its global searchability,SAGWO is suitable for solving the optimization problem of a 5G FSS that has a large design *** combination of SAGWO and the new FSS optimization model can automatically obtain the shape of the FSS unit with electromagnetic interference shielding capability at the center operating *** verify the performance of the proposed method,a double-layer ring FSS is designed with the purpose of providing electromagnetic interference shielding features at28 *** results show that the optimized FSS has better electromagnetic interference shielding at the center frequency and has higher angular ***,a sample of the optimized FSS is fabricated and tested.

关键词： grey wolf optimizer Fifth-generation wireless communication system(5G) Frequency selection surface Shape optimization

On the prediction of methane adsorption in shale using grey wolf optimizer support vector machine approach

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Petroleum 2022年第2期8卷 264-269页

作者： Rahmad Syah Mohammad Hossein Towfighi Naeem Reza Daneshfar Hossein Dehdar Bahram Soltani Soulgani DS&CI Research Group Universitas Medan AreaMedanIndonesia Department of Petroleum Engineering Faculty of Chemical EngineeringTarbiat Modares UniversityP.O.Box14155-4838TehranIran Department of Petroleum Engineering Ahwaz Faculty of Petroleum EngineeringPetroleum University of Technology(PUT)AhwazIran

With the advancement of technology,gas shales have become one of the most prominent energy sources all over the ***,estimating the amount of adsorbed gas in shale resources is necessary for the technical and economic foresight of the production *** paper presents a novel machine learning method called grey wolf optimizer support vector machine(GWO-SVM)to predict adsorbed *** this purpose,a data set containing temperature,pressure,total organic carbon(TOC),and humidity has been collected from several sources,and the GWO-SVM model was created based on *** results show that this model has R-squared and root mean square error equal to 0.982 and 0.08,***,the results ensure that the proposed model gives an excellent prediction of the amount of adsorbed gas compared to previously proposed ***,according to the sensitivity analysis,among the input parameters,humidity has the highest effect on gas adsorption.

关键词： Gas adsorption Shale Machine learning Model Support vector machine grey wolf optimizer

grey wolf optimizer with Ranking-Based Mutation Operator for IIR Model Identification

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Chinese Journal of Electronics 2018年第5期27卷 1071-1079页

作者： ZHANG Sen ZHOU Yongquan College of Information Science and Engineering Guangxi University for Nationalities School Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence

A variant of grey wolf optimizer(GWO),called grey wolf optimizer with Ranking-based mutation operator(RGWO) is applied to the Infinite impulse response(IIR) system identification problem. RGWO makes GWO faster and more robust. In RGWO, the rankingbased mutation operator is integrated into the GWO to accelerate the convergence speed, and thus enhance the performance. The simulation results over several models are presented and statistically validated. Compared to other robust evolutionary algorithms, RGWO performs significantly better in terms of the quality, speed, and the stability of the final solutions.

关键词： System identification IIR system grey wolf optimizer Ranking-based mutation operator

Enhanced Multi-Objective grey wolf optimizer with Lévy Flight and Mutation Operators for Feature Selection

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Computer Systems Science & Engineering 2023年第11期47卷 1937-1966页

作者： Qasem Al-Tashi Tareq M Shami Said Jadid Abdulkadir Emelia Akashah Patah Akhir Ayed Alwadain Hitham Alhussain Alawi Alqushaibi Helmi MD Rais Amgad Muneer Maliazurina B.Saad Jia Wu Seyedali Mirjalili Department of Imaging Physics The University of Texas MD Anderson Cancer CenterHoustonTXUSA Department of Electronic Engineering University of YorkYorkYO105DDUK Computer and Information Sciences Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Computer Science Department Community CollegeKing Saud UniversityRiyadhP.O.Box 145111Saudi Arabia Torrens University Australia BrisbaneQLDAustralia Yonsei Frontier Lab Yonsei UniversitySeoulKorea University Research and Innovation Center Obuda UniversityBudapest1034Hungary

The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be *** it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization *** paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection *** proposed approach integrates two effective operators into the existing Multi-objective grey wolf optimizer(MOGWO):a Lévy flight and a mutation *** Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected *** mutation operator is integrated to add more informative features that can assist in enhancing classification *** feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid *** evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization ***,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.

关键词： Feature selection multi-objective optimization grey wolf optimizer Lévy flight mutation classification

An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its apphcation

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Frontiers of Information Technology & Electronic Engineering 2017年第11期18卷 1705-1719页

作者： Xiao-qing ZHANG Zheng-feng MING School of Mechano-Electronic Engineering Xidian University Xi'an 710071 China School of Physics and Electronic Engineering Xianyang Normal University Xianyang 712000 China

Due to its simplicity and ease of use, the standard grey wolf optimizer （GWO） is attracting much attention. However, due to its imperfect search structure and possible risk of being trapped in local optima, its application has been limited. To perfect the performance of the algorithm, an optimized GWO is proposed based on a mutation operator and eliminating-reconstructing mechanism （MR-GWO）. By analyzing GWO, it is found that it conducts search with only three leading wolves at the core, and balances the exploration and exploitation abilities by adjusting only the parameter a, which means the wolves lose some diversity to some extent. Therefore, a mutation operator is introduced to facilitate better searching wolves, and an eliminating- reconstructing mechanism is used for the poor search wolves, which not only effectively expands the stochastic search, but also accelerates its convergence, and these two operations complement each other well. To verify its validity, MR-GWO is applied to the global optimization experiment of 13 standard continuous functions and a radial basis function （RBF） network approximation experiment. Through a comparison with other algorithms, it is proven that MR-GWO has a strong advantage.

关键词： Swarm intelligence grey wolf optimizer Optimization Radial basis function network

Medical Image Segmentation using PCNN based on Multi-feature grey wolf optimizer Bionic Algorithm

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Journal of Bionic Engineering 2021年第3期18卷 711-720页

作者： Xue Wang Zhanshan Li Heng Kang Yongping Huang Di Gai College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China ZICT Technology Co. Ltd.Shenzhen 518063China

Medical image segmentation is a challenging task especially in multimodality medical image *** this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image ***,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image *** image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy *** the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the *** modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN *** experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.

关键词： grey wolf optimizer pulse coupled neural network bionic algorithm medical image segmentation

Correction of array failure using grey wolf optimizer hybridized with an interior point algorithm

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Frontiers of Information Technology & Electronic Engineering 2018年第9期19卷 1191-1202页

作者： Shafqat Ullah KHAN M.K.A.RAHIM Liaqat ALI Advanced RF ＆ Microwave Research Group Department of Communication EngineeringFaculty of Electrical EngineeringUniversiti Teknologi Malaysi Department of Electrical Engineering University of Science and Technology Bann

We design a grey wolf optimizer hybridized with an interior point algorithm to correct a faulty antenna array. If a single sensor fails, the radiation power pattern of the entire array is disturbed in terms of sidelobe level（SLL） and null depth level（NDL）, and nulls are damaged and shifted from their original locations. All these issues can be solved by designing a new fitness function to reduce the error between the preferred and expected radiation power patterns and the null limitations. The hybrid algorithm has been designed to control the array＇s faulty radiation power pattern. Antenna arrays composed of 21 sensors are used in an example simulation scenario. The MATLAB simulation results confirm the good performance of the proposed method, compared with the existing methods in terms of SLL and NDL.

关键词： Failure correction grey wolf optimizer Interior point algorithm Sidelobes Deeper null depth level

Improved dynamic grey wolf optimizer

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Frontiers of Information Technology & Electronic Engineering 2021年第6期22卷 877-890页

作者： Xiaoqing ZHANG Yuye ZHANG Zhengfeng MING School of Physics and Electronic Engineering Xianyang Normal UniversityXianyang 712000China School of Mechano-Electronic Engineering Xidian UniversityXi’an 710071China

In the standard grey wolf optimizer(GWO), the search wolf must wait to update its current position until the comparison between the other search wolves and the three leader wolves is completed. During this waiting period, the standard GWO is seen as the static GWO. To get rid of this waiting period, two dynamic GWO algorithms are proposed: the first dynamic grey wolf optimizer(DGWO1) and the second dynamic grey wolf optimizer(DGWO2). In the dynamic GWO algorithms, the current search wolf does not need to wait for the comparisons between all other search wolves and the leading wolves, and its position can be updated after completing the comparison between itself or the previous search wolf and the leading wolves. The position of the search wolf is promptly updated in the dynamic GWO algorithms, which increases the iterative convergence rate. Based on the structure of the dynamic GWOs, the performance of the other improved GWOs is examined, verifying that for the same improved algorithm, the one based on dynamic GWO has better performance than that based on static GWO in most instances.

关键词： Swarm intelligence grey wolf optimizer Dynamic grey wolf optimizer Optimization experiment

Bat-grey wolf optimizer and kernel mapping for automatic incremental clustering

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International Journal of Modeling, Simulation, and Scientific Computing 2020年第4期11卷 154-176页

作者： C.Vidyadhari N.Sandhya P.Premchand Computer Science and Engineering Department JNTUK KakinadaIndia Computer Science and Engineering Department VNRVJIET HyderabadIndia Computer Science and Engineering Department University College of Engineering Osmania University HyderabadIndia

The technical advancement in information systems contributes towards the massive availability of the documents stored in the electronic databases such as e-mails,internet and web ***,it becomes a complex task for arranging and browsing the required *** paper proposes an approach for incremental clustering using the Batgrey wolf optimizer(BAGWO).The input documents are initially subjected to the pre-processing module to obtain useful keywords,and then the feature extraction is performed based on wordnet *** feature extraction,feature selection is carried out using entropy ***,the clustering is done using the proposed BAGWO *** BAGWO algorithm is designed by integrating the Bat Algorithm(BA)and grey wolf optimizer(GWO)for generating the different clusters of text ***,the clustering is determined using the BAGWO algorithm,yielding the group of *** the other side,upon the arrival of a new document,the same steps of pre-processing and feature extraction are *** on the features of the test document,the mapping is done between the features of the test document,and the clusters obtained by the proposed BAGWO *** mapping is performed using the kernel-based deep point distance and once the mapping terminated,the representatives are updated based on the fuzzy-based representative *** performance of the developed BAGWO outperformed the existing techniques in terms of clustering accuracy,Jaccard coefficient,and rand coefficient with maximal values 0.948,0.968,and 0.969,respectively.

关键词： Incremental clustering grey wolf optimizer bat optimization fuzzy concept kernel-based deep point distance.