Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw...Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.展开更多
Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PC...Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image segmentation.Specifically,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image segmentation.The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region.In 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 image.Two modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm.The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.展开更多
As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions an...As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions and navigational circumstances.There-fore,this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system.The proposed framework considers multiple optimizations of route,speed planning,and energy management under the constraints of sea conditions during navigation.First,a complex hybrid ship power model consisting of diesel generation system,propulsion system,energy storage system,photovoltaic power generation system,and electric boiler system is established,where sea state information and ship resistance model are considered.With objective optimization functions of cost and greenhouse gas(GHG)emissions,a two-stage optimization framework consisting of route planning,speed scheduling,and energy management is constructed.Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route,speed,and energy optimization scheduling.Finally,simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption,operating costs,and carbon emissions by 17.8%,17.39%,and 13.04%,respectively,compared with the non-optimal control group.展开更多
The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inven...The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inventory uncertain programming model was presented. The grey fuzzy simulation technology can generate input-output data for the uncertain functions. The neural network trained from the inputoutput data can approximate the uncertain functions. The designed hybrid intelligent algorithm by embedding the trained neural network into genetic algorithm can optimize the general grey fuzzy programming problems. Finally, one numerical example is provided to illustrate the effectiveness of the model and the hybrid intelligent algorithm.展开更多
Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertai...Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.展开更多
Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of ...Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.展开更多
Detecting communities in real and complex networks is a highly contested topic in network analysis.Although many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively ...Detecting communities in real and complex networks is a highly contested topic in network analysis.Although many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively fulfill large-scale and real-world networks.Thus,this paper presents a new discrete version of the Improved Grey Wolf Optimizer(I-GWO)algorithm named DI-GWOCD for effectively detecting communities of different networks.In the proposed DI-GWOCD algorithm,I-GWO is first armed using a local search strategy to discover and improve nodes placed in improper communities and increase its ability to search for a better solution.Then a novel Binary Distance Vector(BDV)is introduced to calculate the wolves’distances and adapt I-GWO for solving the discrete community detection problem.The performance of the proposed DI-GWOCD was evaluated in terms of modularity,NMI,and the number of detected communities conducted by some well-known real-world network datasets.The experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon tests.The comparison and the statistical analysis show that the proposed DI-GWOCD can detect the communities with higher quality than other comparative algorithms.展开更多
Fraud Transactions are haunting the economy of many individuals with several factors across the globe.This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ens...Fraud Transactions are haunting the economy of many individuals with several factors across the globe.This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions.This research proposes a novel methodology through three stages.Firstly,Synthetic Minority Oversampling Technique(SMOTE)is applied to get balanced data.Secondly,SMOTE is fed to the nature-inspired Meta Heuristic(MH)algorithm,namely Binary Harris Hawks Optimization(BinHHO),Binary Aquila Optimization(BAO),and Binary Grey Wolf Optimization(BGWO),for feature selection.BinHHO has performed well when compared with the other two.Thirdly,features from BinHHO are fed to the supervised learning algorithms to classify the transactions such as fraud and non-fraud.The efficiency of BinHHO is analyzed with other popular MH algorithms.The BinHHO has achieved the highest accuracy of 99.95%and demonstrates amore significant positive effect on the performance of the proposed model.展开更多
Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapid...Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.展开更多
A new and efficient Grey Wolf Optimization(GWO)algorithm is implemented to solve real power economic dispatch(RPED)problems in this paper.The nonlinear RPED problem is one the most important and fundamental optimizati...A new and efficient Grey Wolf Optimization(GWO)algorithm is implemented to solve real power economic dispatch(RPED)problems in this paper.The nonlinear RPED problem is one the most important and fundamental optimization problem which reduces the total cost in generating real power without violating the constraints.Conventional methods can solve the ELD problem with good solution quality with assumptions assigned to fuel cost curves without which these methods lead to suboptimal or infeasible solutions.The behavior of grey wolves which is mimicked in the GWO algorithm are leadership hierarchy and hunting mechanism.The leadership hierarchy is simulated using four types of grey wolves.In addition,searching,encircling and attacking of prey are the social behaviors implemented in the hunting mechanism.The GWO algorithm has been applied to solve convex RPED problems considering the all possible constraints.The results obtained from GWO algorithm are compared with other state-ofthe-art algorithms available in the recent literatures.It is found that the GWO algorithm is able to provide better solution quality in terms of cost,convergence and robustness for the considered ELD problems.展开更多
The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple pr...The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these disadvantages.However,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence.To solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this study.VGWO first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering cases.For 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value.展开更多
Rubber producers,consumers,traders,and those who are involved in the rubber industry face major risks of rubber price fluctuations.As a result,decision-makers are required to make an accurate estimation of the price o...Rubber producers,consumers,traders,and those who are involved in the rubber industry face major risks of rubber price fluctuations.As a result,decision-makers are required to make an accurate estimation of the price of rubber.This paper aims to propose hybrid intelligent models,which can be utilized to forecast the price of rubber in Malaysia by employing monthly Malaysia’s rubber pricing data,spanning from January 2016 to March 2021.The projected hybrid model consists of different algorithms with the symbolic Radial Basis Functions Neural Network k-Satisfiability Logic Mining(RBFNN-kSAT).These algorithms,including Grey Wolf Optimization Algorithm,Artificial Bee Colony Algorithm,and Particle Swarm Optimization Algorithm were utilized in the forecasting data analysis.Several factors,which affect the monthly price of rubber,such as rubber production,total exports of rubber,total imports of rubber,stocks of rubber,currency exchange rate,and crude oil prices were also considered in the analysis.To evaluate the results of the introduced model,a comparison has been conducted for each model to identify the most optimum model for forecasting the price of rubber.The findings showed that GWO with RBFNN-kSAT represents the most accurate and efficient model compared with ABC with RBFNNkSAT and PSO with RBFNN-kSAT in forecasting the price of rubber.The GWO with RBFNN-kSAT obtained the greatest average accuracy(92%),with a better correlation coefficient R=0.983871 than ABC with RBFNN-kSAT and PSO with RBFNN-kSAT.Furthermore,the empirical results of this study provided several directions for policymakers to make the right decision in terms of devising proper measures in the industry to address frequent price changes so that the Malaysian rubber industry maintains dominance in the international markets.展开更多
Crimping is widely adopted in the production of large-diameter submerged-arc welding pipes. Traditionally, designers obtain the technical parameters for crimping from experience or by trial and error through experimen...Crimping is widely adopted in the production of large-diameter submerged-arc welding pipes. Traditionally, designers obtain the technical parameters for crimping from experience or by trial and error through experiments and the finite element(FE) method. However, it is difficult to achieve ideal crimping quality by these approaches. To resolve this issue, crimping parameter design was investigated by multi-objective optimization. Crimping was simulated using the FE code ABAQUS and the FE model was validated experimentally. A welding pipe made of X80 high-strength pipeline steel was considered as a target object and the optimization problem for its crimping was formulated as a mathematical model and crimping was optimized. A response surface method based on the radial basis function was used to construct a surrogate model; the genetic algorithm NSGA-II was adopted to search for Pareto solutions; grey relational analysis was used to determine the most satisfactory solution from the Pareto solutions. The obtained optimal design of parameters shows good agreement with the initial design and remarkably improves the crimping quality. Thus, the results provide an effective approach for improving crimping quality and reducing design times.展开更多
The Grey Wolf Optimization(GWO)algorithm is acknowledged as an effective method for rock acoustic emission localization.However,the conventional GWO algorithm encounters challenges related to solution accuracy and con...The Grey Wolf Optimization(GWO)algorithm is acknowledged as an effective method for rock acoustic emission localization.However,the conventional GWO algorithm encounters challenges related to solution accuracy and convergence speed.To address these concerns,this paper develops a Simplex Improved Grey Wolf Optimizer(SMIGWO)algorithm.The randomly generating initial populations are replaced with the iterative chaotic sequences.The search process is optimized using the convergence factor optimization algorithm based on the inverse incompleteГfunction.The simplex method is utilized to address issues related to poorly positioned grey wolves.Experimental results demonstrate that,compared to the conventional GWO algorithm-based AE localization algorithm,the proposed algorithm achieves a higher solution accuracy and showcases a shorter search time.Additionally,the algorithm demonstrates fewer convergence steps,indicating superior convergence efficiency.These findings highlight that the proposed SMIGWO algorithm offers enhanced solution accuracy,stability,and optimization performance.The benefits of the SMIGWO algorithm extend universally across various materials,such as aluminum,granite,and sandstone,showcasing consistent effectiveness irrespective of material type.Consequently,this algorithm emerges as a highly effective tool for identifying acoustic emission signals and improving the precision of rock acoustic emission localization.展开更多
The wheel wear of light rail trains is difficult to predict due to poor information and small data samples.However,the amount of wear gradually increases with the running mileage.The grey future prediction model is su...The wheel wear of light rail trains is difficult to predict due to poor information and small data samples.However,the amount of wear gradually increases with the running mileage.The grey future prediction model is supposed to deal with this problem effectively.In this study,we propose an improved non-equidistant grey model GM(1,1)with background values optimized by a genetic algorithm(GA).While the grey model is not good enough to track data series with features of randomness and nonlinearity,the residual error series of the GA-GM(1,1)model is corrected through a back propagation neural network(BPNN).To further improve the performance of the GA-GM(1,1)-BPNN model,a particle swarm optimization(PSO)algorithm is implemented to train the weight and bias in the neural network.The traditional non-equidistant GM(1,1)model and the proposed GA-GM(1,1),GA-GM(1,1)-BPNN,and GA-GM(1,1)-PSO-BPNN models were used to predict the wheel diameter and wheel flange wear of the Changchun light rail train and their validity and rationality were verified.Benefitting from the optimization effects of the GA,neural network,and PSO algorithm,the performance ranking of the four methods from highest to lowest was GA-GM(1,1)-PSO-BPNN>GA-GM(1,1)-BPNN>GA-GM(1,1)>GM(1,1)in both the fitting and prediction zones.The GA-GM(1,1)-PSO-BPNN model performed best,with the lowest fitting and forecasting maximum relative error,mean absolute error,mean absolute percentage error,and mean squared error of all four models.Therefore,it is the most effective and stable model in field application of light rail train wheel wear prediction.展开更多
For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compare...For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compared with the type-1 TSK fuzzy logic system method,interval type-2 fuzzy sets could simultaneously model both intra-personal uncertainty and inter-personal uncertainty based on the training of the existing error back propagation(BP)algorithm,and the IGWO algorithm was used for training the model premise and consequent parameters to further improve the predictive performance of the model.By improving the gray wolf optimization algorithm,the early convergence judgment mechanism,nonlinear cosine adjustment strategy,and Levy flight strategy were introduced to improve the convergence speed of the algorithm and avoid the problem of falling into local optimum.The interval type-2 TSK FLS method based on the IGWO algorithm was applied to the real-world photovoltaic power time series forecasting instance.Under the same conditions,it was also compared with different IT2 TSK FLS methods,such as type I TSK FLS method,BP algorithm,genetic algorithm,differential evolution,particle swarm optimization,biogeography optimization,gray wolf optimization,etc.Experimental results showed that the proposed method based on IGWO algorithm outperformed other methods in performance,showing its effectiveness and application potential.展开更多
With the passage of time, it has become important to investigate new methods for updating data to better fit the trends of the grey prediction model. The traditional GM（1,1） usually sets the grey action quantity as ...With the passage of time, it has become important to investigate new methods for updating data to better fit the trends of the grey prediction model. The traditional GM（1,1） usually sets the grey action quantity as a constant. Therefore, it cannot effectively fit the dynamic characteristics of the sequence, which results in the grey model having a low precision. The linear grey action quantity model cannot represent the index change law. This paper presents a grey action quantity model, the exponential optimization grey model（EOGM（1,1））, based on the exponential type of grey action quantity; it is constructed based on the exponential characteristics of the grey prediction model. The model can fully reflect the exponential characteristics of the simulation series with time. The exponential sequence has a higher fitting accuracy. The optimized result is verified using a numerical example for the fluctuating sequence and a case study for the index of the tertiary industry＇s GDP. The results show that the model improves the precision of the grey forecasting model and reduces the prediction error.展开更多
Location information plays an important role in most of the applications in Wireless Sensor Network(WSN).Recently,many localization techniques have been proposed,while most of these deals with two Dimensional applicat...Location information plays an important role in most of the applications in Wireless Sensor Network(WSN).Recently,many localization techniques have been proposed,while most of these deals with two Dimensional applications.Whereas,in Three Dimensional applications the task is complex and there are large variations in the altitude levels.In these 3D environments,the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level.For such applications,2D localization models are not reliable.Due to this,the design of 3D localization systems in WSNs faces new challenges.In this paper,in order to find unknown nodes in Three-Dimensional environment,only single anchor node is used.In the simulation-based environment,the nodes with unknown locations are moving at middle&lower layers whereas the top layer is equipped with single anchor node.A novel soft computing technique namely Adaptive Plant Propagation Algorithm(APPA)is introduced to obtain the optimized locations of these mobile nodes.Thesemobile target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity(Degree of Irregularity(DOI))value set to 0.01.The simulation results present that proposed APPAalgorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error,computational time,and the located sensor nodes.展开更多
In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selec...In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.展开更多
In this paper,a hybrid of grey wolf optimization(GWO)and genetic algorithm(GA)has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system.It was named as hybrid of grey wolf op...In this paper,a hybrid of grey wolf optimization(GWO)and genetic algorithm(GA)has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system.It was named as hybrid of grey wolf optimization and genetic algorithm(HGWOGA).HGWOGA was applied to this hybrid problem through three procedures.First,the balance between the exploration and the exploitation process was done by grey wolf optimizer algorithm.Then,we divided the population into subpopulation and used the arithmetical crossover operator to utilize the dimension reduction and the population partitioning processes.At last,mutation operator was applied in the whole population in order to refrain from the premature convergence and trapping in local minima.MATLAB code was designed to implement the proposed methodology.The result of this algorithm is compared with the results of iteration method,GWO,GA,artificial bee colony(ABC)and particle swarm optimization(PSO)techniques.The results obtained by this algorithm are better when compared with those mentioned in the text.展开更多
文摘Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.
基金This research is supported by the National Key Research and Development Program of China(2018YFB0804202,2018YFB0804203)Regional Joint Fund of NSFC(U19A2057),the National Natural Science Foundation of China(61672259,61876070)and the Jilin Province Science and Technology Development Plan Project(20190303134SF,20180201064SF).
文摘Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image segmentation.Specifically,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image segmentation.The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region.In 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 image.Two modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm.The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.
基金supported by the National Natural Science Foundation of China under Grant 62473328by the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology under No.XTCX202203.
文摘As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions and navigational circumstances.There-fore,this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system.The proposed framework considers multiple optimizations of route,speed planning,and energy management under the constraints of sea conditions during navigation.First,a complex hybrid ship power model consisting of diesel generation system,propulsion system,energy storage system,photovoltaic power generation system,and electric boiler system is established,where sea state information and ship resistance model are considered.With objective optimization functions of cost and greenhouse gas(GHG)emissions,a two-stage optimization framework consisting of route planning,speed scheduling,and energy management is constructed.Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route,speed,and energy optimization scheduling.Finally,simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption,operating costs,and carbon emissions by 17.8%,17.39%,and 13.04%,respectively,compared with the non-optimal control group.
基金Supported bythe Science and Research Foundationof Shanghai Municipal Educational Commssion (05DZ33)
文摘The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inventory uncertain programming model was presented. The grey fuzzy simulation technology can generate input-output data for the uncertain functions. The neural network trained from the inputoutput data can approximate the uncertain functions. The designed hybrid intelligent algorithm by embedding the trained neural network into genetic algorithm can optimize the general grey fuzzy programming problems. Finally, one numerical example is provided to illustrate the effectiveness of the model and the hybrid intelligent algorithm.
基金The Science and Research Foundation of Shanghai Municipal Education Commission (No06DZ033)the Doctoral Science and Research Foundation of Shanghai Nor mal University ( No PL719)the Science and Research Foundation of Shanghai Nor mal University (NoSK200741)
文摘Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.
文摘Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.
文摘Detecting communities in real and complex networks is a highly contested topic in network analysis.Although many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively fulfill large-scale and real-world networks.Thus,this paper presents a new discrete version of the Improved Grey Wolf Optimizer(I-GWO)algorithm named DI-GWOCD for effectively detecting communities of different networks.In the proposed DI-GWOCD algorithm,I-GWO is first armed using a local search strategy to discover and improve nodes placed in improper communities and increase its ability to search for a better solution.Then a novel Binary Distance Vector(BDV)is introduced to calculate the wolves’distances and adapt I-GWO for solving the discrete community detection problem.The performance of the proposed DI-GWOCD was evaluated in terms of modularity,NMI,and the number of detected communities conducted by some well-known real-world network datasets.The experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon tests.The comparison and the statistical analysis show that the proposed DI-GWOCD can detect the communities with higher quality than other comparative algorithms.
文摘Fraud Transactions are haunting the economy of many individuals with several factors across the globe.This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions.This research proposes a novel methodology through three stages.Firstly,Synthetic Minority Oversampling Technique(SMOTE)is applied to get balanced data.Secondly,SMOTE is fed to the nature-inspired Meta Heuristic(MH)algorithm,namely Binary Harris Hawks Optimization(BinHHO),Binary Aquila Optimization(BAO),and Binary Grey Wolf Optimization(BGWO),for feature selection.BinHHO has performed well when compared with the other two.Thirdly,features from BinHHO are fed to the supervised learning algorithms to classify the transactions such as fraud and non-fraud.The efficiency of BinHHO is analyzed with other popular MH algorithms.The BinHHO has achieved the highest accuracy of 99.95%and demonstrates amore significant positive effect on the performance of the proposed model.
文摘Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.
文摘A new and efficient Grey Wolf Optimization(GWO)algorithm is implemented to solve real power economic dispatch(RPED)problems in this paper.The nonlinear RPED problem is one the most important and fundamental optimization problem which reduces the total cost in generating real power without violating the constraints.Conventional methods can solve the ELD problem with good solution quality with assumptions assigned to fuel cost curves without which these methods lead to suboptimal or infeasible solutions.The behavior of grey wolves which is mimicked in the GWO algorithm are leadership hierarchy and hunting mechanism.The leadership hierarchy is simulated using four types of grey wolves.In addition,searching,encircling and attacking of prey are the social behaviors implemented in the hunting mechanism.The GWO algorithm has been applied to solve convex RPED problems considering the all possible constraints.The results obtained from GWO algorithm are compared with other state-ofthe-art algorithms available in the recent literatures.It is found that the GWO algorithm is able to provide better solution quality in terms of cost,convergence and robustness for the considered ELD problems.
文摘The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these disadvantages.However,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence.To solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this study.VGWO first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering cases.For 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value.
基金supported by the Ministry of Higher Education Malaysia (MOHE)through the Fundamental Research Grant Scheme (FRGS),FRGS/1/2022/STG06/USM/02/11 and Universiti Sains Malaysia.
文摘Rubber producers,consumers,traders,and those who are involved in the rubber industry face major risks of rubber price fluctuations.As a result,decision-makers are required to make an accurate estimation of the price of rubber.This paper aims to propose hybrid intelligent models,which can be utilized to forecast the price of rubber in Malaysia by employing monthly Malaysia’s rubber pricing data,spanning from January 2016 to March 2021.The projected hybrid model consists of different algorithms with the symbolic Radial Basis Functions Neural Network k-Satisfiability Logic Mining(RBFNN-kSAT).These algorithms,including Grey Wolf Optimization Algorithm,Artificial Bee Colony Algorithm,and Particle Swarm Optimization Algorithm were utilized in the forecasting data analysis.Several factors,which affect the monthly price of rubber,such as rubber production,total exports of rubber,total imports of rubber,stocks of rubber,currency exchange rate,and crude oil prices were also considered in the analysis.To evaluate the results of the introduced model,a comparison has been conducted for each model to identify the most optimum model for forecasting the price of rubber.The findings showed that GWO with RBFNN-kSAT represents the most accurate and efficient model compared with ABC with RBFNNkSAT and PSO with RBFNN-kSAT in forecasting the price of rubber.The GWO with RBFNN-kSAT obtained the greatest average accuracy(92%),with a better correlation coefficient R=0.983871 than ABC with RBFNN-kSAT and PSO with RBFNN-kSAT.Furthermore,the empirical results of this study provided several directions for policymakers to make the right decision in terms of devising proper measures in the industry to address frequent price changes so that the Malaysian rubber industry maintains dominance in the international markets.
基金Project(Y2012035)supported by the Natural Science Foundation of Hebei Provincial Education Department,ChinaProject(12211014)supported by the Natural Science Foundation of Hebei Provincial Technology Department,China+2 种基金Project(NJZY14006)supported by the Inner Mongolia Higher School Science and Technology Research Program,ChinaProject(2014BS0502)supported by the Natural Science Foundation of Inner Mongolia,ChinaProject(135143)supported by the Program of Higher-level Talents Fund of Inner Mongolia University,China
文摘Crimping is widely adopted in the production of large-diameter submerged-arc welding pipes. Traditionally, designers obtain the technical parameters for crimping from experience or by trial and error through experiments and the finite element(FE) method. However, it is difficult to achieve ideal crimping quality by these approaches. To resolve this issue, crimping parameter design was investigated by multi-objective optimization. Crimping was simulated using the FE code ABAQUS and the FE model was validated experimentally. A welding pipe made of X80 high-strength pipeline steel was considered as a target object and the optimization problem for its crimping was formulated as a mathematical model and crimping was optimized. A response surface method based on the radial basis function was used to construct a surrogate model; the genetic algorithm NSGA-II was adopted to search for Pareto solutions; grey relational analysis was used to determine the most satisfactory solution from the Pareto solutions. The obtained optimal design of parameters shows good agreement with the initial design and remarkably improves the crimping quality. Thus, the results provide an effective approach for improving crimping quality and reducing design times.
基金support from the National Science Foundation of China(52304137,5192780752274124,52325403)Tiandi Science and Technology Co.,Ltd.(2022-2-TDMS012 and SKLIS202417)Sichuan University(SKHL2215).
文摘The Grey Wolf Optimization(GWO)algorithm is acknowledged as an effective method for rock acoustic emission localization.However,the conventional GWO algorithm encounters challenges related to solution accuracy and convergence speed.To address these concerns,this paper develops a Simplex Improved Grey Wolf Optimizer(SMIGWO)algorithm.The randomly generating initial populations are replaced with the iterative chaotic sequences.The search process is optimized using the convergence factor optimization algorithm based on the inverse incompleteГfunction.The simplex method is utilized to address issues related to poorly positioned grey wolves.Experimental results demonstrate that,compared to the conventional GWO algorithm-based AE localization algorithm,the proposed algorithm achieves a higher solution accuracy and showcases a shorter search time.Additionally,the algorithm demonstrates fewer convergence steps,indicating superior convergence efficiency.These findings highlight that the proposed SMIGWO algorithm offers enhanced solution accuracy,stability,and optimization performance.The benefits of the SMIGWO algorithm extend universally across various materials,such as aluminum,granite,and sandstone,showcasing consistent effectiveness irrespective of material type.Consequently,this algorithm emerges as a highly effective tool for identifying acoustic emission signals and improving the precision of rock acoustic emission localization.
基金supported by the National Natural Science Foundation of China(No.52178436)the Shanghai Collaborative Innovation Research Center for Multi-network&Multi-modal Rail Transit,China.
文摘The wheel wear of light rail trains is difficult to predict due to poor information and small data samples.However,the amount of wear gradually increases with the running mileage.The grey future prediction model is supposed to deal with this problem effectively.In this study,we propose an improved non-equidistant grey model GM(1,1)with background values optimized by a genetic algorithm(GA).While the grey model is not good enough to track data series with features of randomness and nonlinearity,the residual error series of the GA-GM(1,1)model is corrected through a back propagation neural network(BPNN).To further improve the performance of the GA-GM(1,1)-BPNN model,a particle swarm optimization(PSO)algorithm is implemented to train the weight and bias in the neural network.The traditional non-equidistant GM(1,1)model and the proposed GA-GM(1,1),GA-GM(1,1)-BPNN,and GA-GM(1,1)-PSO-BPNN models were used to predict the wheel diameter and wheel flange wear of the Changchun light rail train and their validity and rationality were verified.Benefitting from the optimization effects of the GA,neural network,and PSO algorithm,the performance ranking of the four methods from highest to lowest was GA-GM(1,1)-PSO-BPNN>GA-GM(1,1)-BPNN>GA-GM(1,1)>GM(1,1)in both the fitting and prediction zones.The GA-GM(1,1)-PSO-BPNN model performed best,with the lowest fitting and forecasting maximum relative error,mean absolute error,mean absolute percentage error,and mean squared error of all four models.Therefore,it is the most effective and stable model in field application of light rail train wheel wear prediction.
基金supported by National Natural Science Foundation of China(No.12172157)Key Project of Natural Science Foundation of Gansu Province(No.25JRRA150)Key Research and Development Planning Project of Gansu Province(No.23YFWA0007).
文摘For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compared with the type-1 TSK fuzzy logic system method,interval type-2 fuzzy sets could simultaneously model both intra-personal uncertainty and inter-personal uncertainty based on the training of the existing error back propagation(BP)algorithm,and the IGWO algorithm was used for training the model premise and consequent parameters to further improve the predictive performance of the model.By improving the gray wolf optimization algorithm,the early convergence judgment mechanism,nonlinear cosine adjustment strategy,and Levy flight strategy were introduced to improve the convergence speed of the algorithm and avoid the problem of falling into local optimum.The interval type-2 TSK FLS method based on the IGWO algorithm was applied to the real-world photovoltaic power time series forecasting instance.Under the same conditions,it was also compared with different IT2 TSK FLS methods,such as type I TSK FLS method,BP algorithm,genetic algorithm,differential evolution,particle swarm optimization,biogeography optimization,gray wolf optimization,etc.Experimental results showed that the proposed method based on IGWO algorithm outperformed other methods in performance,showing its effectiveness and application potential.
基金supported by the National Key Research and Development Program of China(2016YFC1402000)the National Science Foundation of China(41701593+2 种基金7137109871571157)the National Social Science Fund Major Project(14ZDB151)
文摘With the passage of time, it has become important to investigate new methods for updating data to better fit the trends of the grey prediction model. The traditional GM（1,1） usually sets the grey action quantity as a constant. Therefore, it cannot effectively fit the dynamic characteristics of the sequence, which results in the grey model having a low precision. The linear grey action quantity model cannot represent the index change law. This paper presents a grey action quantity model, the exponential optimization grey model（EOGM（1,1））, based on the exponential type of grey action quantity; it is constructed based on the exponential characteristics of the grey prediction model. The model can fully reflect the exponential characteristics of the simulation series with time. The exponential sequence has a higher fitting accuracy. The optimized result is verified using a numerical example for the fluctuating sequence and a case study for the index of the tertiary industry＇s GDP. The results show that the model improves the precision of the grey forecasting model and reduces the prediction error.
基金This research was supported by X-mind Corps program of National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(No.2019H1D8A1105622)and the Soonchunhyang University Research Fund.
文摘Location information plays an important role in most of the applications in Wireless Sensor Network(WSN).Recently,many localization techniques have been proposed,while most of these deals with two Dimensional applications.Whereas,in Three Dimensional applications the task is complex and there are large variations in the altitude levels.In these 3D environments,the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level.For such applications,2D localization models are not reliable.Due to this,the design of 3D localization systems in WSNs faces new challenges.In this paper,in order to find unknown nodes in Three-Dimensional environment,only single anchor node is used.In the simulation-based environment,the nodes with unknown locations are moving at middle&lower layers whereas the top layer is equipped with single anchor node.A novel soft computing technique namely Adaptive Plant Propagation Algorithm(APPA)is introduced to obtain the optimized locations of these mobile nodes.Thesemobile target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity(Degree of Irregularity(DOI))value set to 0.01.The simulation results present that proposed APPAalgorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error,computational time,and the located sensor nodes.
基金the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for funding this research(IFKSUOR3-014-3).
文摘In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.
文摘In this paper,a hybrid of grey wolf optimization(GWO)and genetic algorithm(GA)has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system.It was named as hybrid of grey wolf optimization and genetic algorithm(HGWOGA).HGWOGA was applied to this hybrid problem through three procedures.First,the balance between the exploration and the exploitation process was done by grey wolf optimizer algorithm.Then,we divided the population into subpopulation and used the arithmetical crossover operator to utilize the dimension reduction and the population partitioning processes.At last,mutation operator was applied in the whole population in order to refrain from the premature convergence and trapping in local minima.MATLAB code was designed to implement the proposed methodology.The result of this algorithm is compared with the results of iteration method,GWO,GA,artificial bee colony(ABC)and particle swarm optimization(PSO)techniques.The results obtained by this algorithm are better when compared with those mentioned in the text.
