As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphas...As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems(ITS).This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering(GOAVNET)algorithm,an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks(VANETs),leveraging the Grasshopper Optimization Algorithm(GOA)to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems(ITS).The proposed GOA-VNET employs an iterative and interactive optimization mechanism to dynamically adjust node positions and cluster configurations,ensuring robust adaptability to varying vehicular densities and transmission ranges.Key features of GOA-VNET include the utilization of attraction zone,repulsion zone,and comfort zone parameters,which collectively enhance clustering efficiency and minimize congestion within Regions of Interest(ROI).By managing cluster configurations and node densities effectively,GOA-VNET ensures balanced load distribution and seamless data transmission,even in scenarios with high vehicular densities and varying transmission ranges.Comparative evaluations against the Whale Optimization Algorithm(WOA)and Grey Wolf Optimization(GWO)demonstrate that GOA-VNET consistently outperforms these methods by achieving superior clustering efficiency,reducing the number of clusters by up to 10%in high-density scenarios,and improving data transmission reliability.Simulation results reveal that under a 100-600 m transmission range,GOA-VNET achieves an average reduction of 8%-15%in the number of clusters and maintains a 5%-10%improvement in packet delivery ratio(PDR)compared to baseline algorithms.Additionally,the algorithm incorporates a heat transfer-inspired load-balancing mechanism,ensuring equitable distribution of nodes among cluster leaders(CLs)and maintaining a stable network environment.These results validate GOA-VNET as a reliable and scalable solution for VANETs,with significant potential to support next-generation ITS.Future research could further enhance the algorithm by integrating multi-objective optimization techniques and exploring broader applications in complex traffic scenarios.展开更多
In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending netw...In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.展开更多
Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of emails.Not only is spam wasting users’time and bandwidth.In addition,it ...Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of emails.Not only is spam wasting users’time and bandwidth.In addition,it limits the storage space of the email box as well as the disk space.Thus,spam detection is a challenge for individuals and organizations alike.To advance spam email detection,this work proposes a new spam detection approach,using the grasshopper optimization algorithm(GOA)in training a multilayer perceptron(MLP)classifier for categorizing emails as ham and spam.Hence,MLP and GOA produce an artificial neural network(ANN)model,referred to(GOAMLP).Two corpora are applied Spam Base and UK-2011Web spam for this approach.Finally,the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.展开更多
Ontology alignment is an essential and complex task to integrate heterogeneous ontology.The meta-heuristic algorithm has proven to be an effective method for ontology alignment.However,it only applies the inherent adv...Ontology alignment is an essential and complex task to integrate heterogeneous ontology.The meta-heuristic algorithm has proven to be an effective method for ontology alignment.However,it only applies the inherent advantages of metaheuristics algorithm and rarely considers the execution efficiency,especially the multi-objective ontology alignment model.The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications.In this paper,two multi-objective grasshopper optimization algorithms(MOGOA)are proposed to enhance ontology alignment.One isε-dominance concept based GOA(EMO-GOA)and the other is fast Non-dominated Sorting based GOA(NS-MOGOA).The performance of the two methods to align the ontology is evaluated by using the benchmark dataset.The results demonstrate that the proposed EMO-GOA and NSMOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods.展开更多
The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and imprecision.Grasshopper optimization algorithm(GOA)is ...The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and imprecision.Grasshopper optimization algorithm(GOA)is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature,which has good convergence ability towards optima.The main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an IT2-FLS.The antecedent part parameters(Gaussian membership function parameters)are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm.Tuning of the consequent part parameters are accomplished using extreme learning machine.The optimized IT2-FLS(GOAIT2FELM)obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and prices.The forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization algorithm.Analysis of the performance,on the same data-sets,reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.展开更多
In the era of big data,personalised recommendation systems are essential for enhancing user engagement and driving business growth.However,traditional recommendation algorithms,such as collaborative filtering,face sig...In the era of big data,personalised recommendation systems are essential for enhancing user engagement and driving business growth.However,traditional recommendation algorithms,such as collaborative filtering,face significant challenges due to data sparsity,algorithm scalability,and the difficulty of adapting to dynamic user preferences.These limitations hinder the ability of systems to provide highly accurate and personalised recommendations.To address these challenges,this paper proposes a clustering-based recommendation method that integrates an enhanced Grasshopper Optimisation Algorithm(GOA),termed LCGOA,to improve the accuracy and efficiency of recommendation systems by optimising cluster centroids in a dynamic environment.By combining the K-means algorithm with the enhanced GOA,which incorporates a Lévy flight mechanism and multi-strategy co-evolution,our method overcomes the centroid sensitivity issue,a key limitation in traditional clustering techniques.Experimental results across multiple datasets show that the proposed LCGOA-based method significantly outperforms conventional recommendation algorithms in terms of recommendation accuracy,offering more relevant content to users and driving greater customer satisfaction and business growth.展开更多
This paper uses a Grasshopper Optimization Algorithm (GOA) optimized PDF plus (1 + PI) controller for Automatic generation control (AGC) of a power system with Flexible AC Transmission system (FACTS) devices. Three di...This paper uses a Grasshopper Optimization Algorithm (GOA) optimized PDF plus (1 + PI) controller for Automatic generation control (AGC) of a power system with Flexible AC Transmission system (FACTS) devices. Three differently rated reheat turbine operated thermal units with appropriate generation rate constraint (GRC) are considered along with different FACTS devices. A new multistage controller design structure of a PDF plus (1 + PI) is introduced in the FACTS empowered power system for AGC while the controller gains are tuned by the GOA. The superiority of the proposed algorithm over the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms is demonstrated. The dynamic responses of GOA optimized PDF plus (1 + PI) are compared with PIDF, PID and PI controllers on the same system. It is demonstrated that GOA optimized PDF plus (1 + PI) controller provides optimum responses in terms of settling time and peak deviations compared to other controllers. In addition, a GOA-tuned PDF plus (1 + PI) controller with Interline Power Flow Controller (IPFC) exhibits optimal results compared to other FACTS devices. The sturdiness of the projected controller is validated using sensitivity analysis with numerous load patterns and a wide variation of parameterization. To further validate the real-time feasibility of the proposed method, experiments using OPAL-RT OP5700 RCP/HIL and FPGA based real-time simulations are carried out.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
This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’performance.The algorithm starts by processing data by a modified K-means technique as a hie...This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’performance.The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance.The work of this paper consists of two parts.The first part is based on collecting data of employees to calculate and illustrate the performance of each employee.The second part is based on the classification and prediction techniques of the employee performance.This model is designed to help companies in their decisions about the employees’performance.The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features.Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years.Results also show that the Grasshopper Optimization,followed by“KF”with the Gradient Boosting Tree as classifier and predictor,is characterized by a high accuracy.The proposed algorithm is compared with other known techniques where our results are fund to be superior.展开更多
Communication is a significant part of being human and living in the world.Diverse kinds of languages and their variations are there;thus,one person can speak any language and cannot effectively communicate with one w...Communication is a significant part of being human and living in the world.Diverse kinds of languages and their variations are there;thus,one person can speak any language and cannot effectively communicate with one who speaks that language in a different accent.Numerous application fields such as education,mobility,smart systems,security,and health care systems utilize the speech or voice recognition models abundantly.Though,various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional languages.It is necessary to understand the speech recognition field,in which the major concentrated stages are feature extraction and classification.This paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network(RNN).Here,the preprocessing of the input signal is performed by smoothing and median filtering.After preprocessing the feature extraction is carried out using MFCC and Spectral features to get precise features from the input Marathi Speech corpus.The optimized RNN classifier is used for speech recognition after completing the feature extraction task,where the optimization of hidden neurons in RNN is performed by the Grasshopper Optimization Algorithm(GOA).Finally,the comparison with the conventional techniques has shown that the proposed model outperforms most competing models on a benchmark dataset.展开更多
Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but ...Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.展开更多
Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-...Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.展开更多
This study proposed a novel object-based hybrid classification model named GMNN that combines Grasshopper Optimization Algorithm(GOA)and the multiple-class Neural network(MNN)for urban pattern detection in Hanoi,Vietn...This study proposed a novel object-based hybrid classification model named GMNN that combines Grasshopper Optimization Algorithm(GOA)and the multiple-class Neural network(MNN)for urban pattern detection in Hanoi,Vietnam.Four bands of SPOT 7 image and derivable NDVI,NDWI were used to generate image segments with associated attributes by PCI Geomatics software.These segments were classified into four urban surface types(namely water,impervious surface,vegetation and bare soil)by the proposed model.Alternatively,three training and validation datasets of different sizes were used to verify the robustness of this model.For all tests,the overall accuracies of the classification were approximately 87%,and the Area under Receiver Operating Characteristic curves for each land cover type was 0.97.Also,the performance of this model was examined by comparing several statistical indicators with common benchmark classifiers.The results showed that GMNN out-performed established methods in all comparable indicators.These results suggested that our hybrid model was successfully deployed in the study area and could be used as an alternative classification method for urban land cover studies.In a broader sense,classification methods will be enriched with the active and fast-growing contribution of metaheuristic algorithms.展开更多
Water quality prediction is vital for solving water pollution and protecting the water environment.In terms of the characteristics of nonlinearity,instability,and randomness of water quality parameters,a short-term wa...Water quality prediction is vital for solving water pollution and protecting the water environment.In terms of the characteristics of nonlinearity,instability,and randomness of water quality parameters,a short-term water quality prediction model was proposed based on variational mode decomposition(VMD)and improved grasshopper optimization algorithm(IGOA),so as to optimize long short-term memory neural network(LSTM).First,VMD was adopted to decompose the water quality data into a series of relatively stable components,with the aim to reduce the instability of the original data and increase the predictability,then each component was input into the iGOA-LSTM model for prediction.Finally,each component was added to obtain the predicted values.In this study,the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and prediction.The experimental results showed that the prediction accuracy of the VMDIGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition(EEMD),the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),Nonlinear Autoregressive Network with Exogenous Inputs(NARX),Recurrent Neural Network(RNN),as well as other models,showing better performance in short-term prediction.The current study will provide a reliable solution for water quality prediction studies in other areas.展开更多
Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods.To address this challenge,this paper proposes an effective local spectr...Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods.To address this challenge,this paper proposes an effective local spectrum enhancement‐based diagnostic method that can identify weak fault frequencies in the original complicated raw signals.For this purpose,a traversal frequency band segmentation technique is first proposed for dividing the raw signal into a series of subfrequency bands.Then,the proposed synthetic quantitative index is constructed for selecting the most informative local frequency band(ILFB)containing fault features from the divided subfrequency bands.Furthermore,an improved grasshopper optimization algorithmbased stochastic resonance(SR)system is developed for enhancing weak fault features contained in the selected most ILFB with less computation cost.Finally,the enhanced weak fault frequencies are extracted from the output of the SR system using a common spectrum analysis.Two experiments on a laboratory planetary gearbox and an open bearing data set are used to verify the effectuality of the proposed method.The diagnostic results demonstrate that the proposed method can identify incipient faults of gears and bearings in an effective and accurate manner.Furthermore,the advantages of the proposed method are highlighted by comparison with other methods.展开更多
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2024-00337489Development of Data Drift Management Technology to Overcome Performance Degradation of AI Analysis Models).
文摘As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems(ITS).This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering(GOAVNET)algorithm,an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks(VANETs),leveraging the Grasshopper Optimization Algorithm(GOA)to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems(ITS).The proposed GOA-VNET employs an iterative and interactive optimization mechanism to dynamically adjust node positions and cluster configurations,ensuring robust adaptability to varying vehicular densities and transmission ranges.Key features of GOA-VNET include the utilization of attraction zone,repulsion zone,and comfort zone parameters,which collectively enhance clustering efficiency and minimize congestion within Regions of Interest(ROI).By managing cluster configurations and node densities effectively,GOA-VNET ensures balanced load distribution and seamless data transmission,even in scenarios with high vehicular densities and varying transmission ranges.Comparative evaluations against the Whale Optimization Algorithm(WOA)and Grey Wolf Optimization(GWO)demonstrate that GOA-VNET consistently outperforms these methods by achieving superior clustering efficiency,reducing the number of clusters by up to 10%in high-density scenarios,and improving data transmission reliability.Simulation results reveal that under a 100-600 m transmission range,GOA-VNET achieves an average reduction of 8%-15%in the number of clusters and maintains a 5%-10%improvement in packet delivery ratio(PDR)compared to baseline algorithms.Additionally,the algorithm incorporates a heat transfer-inspired load-balancing mechanism,ensuring equitable distribution of nodes among cluster leaders(CLs)and maintaining a stable network environment.These results validate GOA-VNET as a reliable and scalable solution for VANETs,with significant potential to support next-generation ITS.Future research could further enhance the algorithm by integrating multi-objective optimization techniques and exploring broader applications in complex traffic scenarios.
文摘In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.
文摘Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of emails.Not only is spam wasting users’time and bandwidth.In addition,it limits the storage space of the email box as well as the disk space.Thus,spam detection is a challenge for individuals and organizations alike.To advance spam email detection,this work proposes a new spam detection approach,using the grasshopper optimization algorithm(GOA)in training a multilayer perceptron(MLP)classifier for categorizing emails as ham and spam.Hence,MLP and GOA produce an artificial neural network(ANN)model,referred to(GOAMLP).Two corpora are applied Spam Base and UK-2011Web spam for this approach.Finally,the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.
基金the Ministry of Education-China Mobile Joint Fund Project(MCM2020J01)。
文摘Ontology alignment is an essential and complex task to integrate heterogeneous ontology.The meta-heuristic algorithm has proven to be an effective method for ontology alignment.However,it only applies the inherent advantages of metaheuristics algorithm and rarely considers the execution efficiency,especially the multi-objective ontology alignment model.The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications.In this paper,two multi-objective grasshopper optimization algorithms(MOGOA)are proposed to enhance ontology alignment.One isε-dominance concept based GOA(EMO-GOA)and the other is fast Non-dominated Sorting based GOA(NS-MOGOA).The performance of the two methods to align the ontology is evaluated by using the benchmark dataset.The results demonstrate that the proposed EMO-GOA and NSMOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods.
文摘The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and imprecision.Grasshopper optimization algorithm(GOA)is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature,which has good convergence ability towards optima.The main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an IT2-FLS.The antecedent part parameters(Gaussian membership function parameters)are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm.Tuning of the consequent part parameters are accomplished using extreme learning machine.The optimized IT2-FLS(GOAIT2FELM)obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and prices.The forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization algorithm.Analysis of the performance,on the same data-sets,reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.
基金Natural Science Research Project of Education Department of Anhui Province of China,Grant/Award Number:2023AH051020Key Project of Anhui Province's Science and Technology Innovation Tackle Plan,Grant/Award Number:202423k09020040+3 种基金National Key Research and Development Program of China,Grant/Award Number:2023YFD1802200Natural Science Foundation of Anhui Province,Grant/Award Number:2308085MF21National Natural Science Foundation of China,Grant/Award Numbers:32472007,62301006,62306008University Synergy Innovation Program of Anhui Province,Grant/Award Number:GXXT-2022-046。
文摘In the era of big data,personalised recommendation systems are essential for enhancing user engagement and driving business growth.However,traditional recommendation algorithms,such as collaborative filtering,face significant challenges due to data sparsity,algorithm scalability,and the difficulty of adapting to dynamic user preferences.These limitations hinder the ability of systems to provide highly accurate and personalised recommendations.To address these challenges,this paper proposes a clustering-based recommendation method that integrates an enhanced Grasshopper Optimisation Algorithm(GOA),termed LCGOA,to improve the accuracy and efficiency of recommendation systems by optimising cluster centroids in a dynamic environment.By combining the K-means algorithm with the enhanced GOA,which incorporates a Lévy flight mechanism and multi-strategy co-evolution,our method overcomes the centroid sensitivity issue,a key limitation in traditional clustering techniques.Experimental results across multiple datasets show that the proposed LCGOA-based method significantly outperforms conventional recommendation algorithms in terms of recommendation accuracy,offering more relevant content to users and driving greater customer satisfaction and business growth.
文摘This paper uses a Grasshopper Optimization Algorithm (GOA) optimized PDF plus (1 + PI) controller for Automatic generation control (AGC) of a power system with Flexible AC Transmission system (FACTS) devices. Three differently rated reheat turbine operated thermal units with appropriate generation rate constraint (GRC) are considered along with different FACTS devices. A new multistage controller design structure of a PDF plus (1 + PI) is introduced in the FACTS empowered power system for AGC while the controller gains are tuned by the GOA. The superiority of the proposed algorithm over the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms is demonstrated. The dynamic responses of GOA optimized PDF plus (1 + PI) are compared with PIDF, PID and PI controllers on the same system. It is demonstrated that GOA optimized PDF plus (1 + PI) controller provides optimum responses in terms of settling time and peak deviations compared to other controllers. In addition, a GOA-tuned PDF plus (1 + PI) controller with Interline Power Flow Controller (IPFC) exhibits optimal results compared to other FACTS devices. The sturdiness of the projected controller is validated using sensitivity analysis with numerous load patterns and a wide variation of parameterization. To further validate the real-time feasibility of the proposed method, experiments using OPAL-RT OP5700 RCP/HIL and FPGA based real-time simulations are carried out.
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This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’performance.The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance.The work of this paper consists of two parts.The first part is based on collecting data of employees to calculate and illustrate the performance of each employee.The second part is based on the classification and prediction techniques of the employee performance.This model is designed to help companies in their decisions about the employees’performance.The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features.Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years.Results also show that the Grasshopper Optimization,followed by“KF”with the Gradient Boosting Tree as classifier and predictor,is characterized by a high accuracy.The proposed algorithm is compared with other known techniques where our results are fund to be superior.
基金Taif University Researchers Supporting Project number(TURSP-2020/349),Taif University,Taif,Saudi Arabia.
文摘Communication is a significant part of being human and living in the world.Diverse kinds of languages and their variations are there;thus,one person can speak any language and cannot effectively communicate with one who speaks that language in a different accent.Numerous application fields such as education,mobility,smart systems,security,and health care systems utilize the speech or voice recognition models abundantly.Though,various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional languages.It is necessary to understand the speech recognition field,in which the major concentrated stages are feature extraction and classification.This paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network(RNN).Here,the preprocessing of the input signal is performed by smoothing and median filtering.After preprocessing the feature extraction is carried out using MFCC and Spectral features to get precise features from the input Marathi Speech corpus.The optimized RNN classifier is used for speech recognition after completing the feature extraction task,where the optimization of hidden neurons in RNN is performed by the Grasshopper Optimization Algorithm(GOA).Finally,the comparison with the conventional techniques has shown that the proposed model outperforms most competing models on a benchmark dataset.
基金supported by Korea Research Fellowship Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(Grant No.2019H1D3A1A01102993)the Inha University Research Grant(2022).
文摘Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+1 种基金Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4331004DSR031)supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).
文摘Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.
基金Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant Number[105.99-2016.05].
文摘This study proposed a novel object-based hybrid classification model named GMNN that combines Grasshopper Optimization Algorithm(GOA)and the multiple-class Neural network(MNN)for urban pattern detection in Hanoi,Vietnam.Four bands of SPOT 7 image and derivable NDVI,NDWI were used to generate image segments with associated attributes by PCI Geomatics software.These segments were classified into four urban surface types(namely water,impervious surface,vegetation and bare soil)by the proposed model.Alternatively,three training and validation datasets of different sizes were used to verify the robustness of this model.For all tests,the overall accuracies of the classification were approximately 87%,and the Area under Receiver Operating Characteristic curves for each land cover type was 0.97.Also,the performance of this model was examined by comparing several statistical indicators with common benchmark classifiers.The results showed that GMNN out-performed established methods in all comparable indicators.These results suggested that our hybrid model was successfully deployed in the study area and could be used as an alternative classification method for urban land cover studies.In a broader sense,classification methods will be enriched with the active and fast-growing contribution of metaheuristic algorithms.
基金the Zhejiang Provincial Natural Science Foundation of China(No.LY23H180001)the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,the China Institute of Water Resources and Hydropower Research(No.IWHR-SKL-201905)the National Natural Science Foundation of China(No.11701363).
文摘Water quality prediction is vital for solving water pollution and protecting the water environment.In terms of the characteristics of nonlinearity,instability,and randomness of water quality parameters,a short-term water quality prediction model was proposed based on variational mode decomposition(VMD)and improved grasshopper optimization algorithm(IGOA),so as to optimize long short-term memory neural network(LSTM).First,VMD was adopted to decompose the water quality data into a series of relatively stable components,with the aim to reduce the instability of the original data and increase the predictability,then each component was input into the iGOA-LSTM model for prediction.Finally,each component was added to obtain the predicted values.In this study,the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and prediction.The experimental results showed that the prediction accuracy of the VMDIGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition(EEMD),the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),Nonlinear Autoregressive Network with Exogenous Inputs(NARX),Recurrent Neural Network(RNN),as well as other models,showing better performance in short-term prediction.The current study will provide a reliable solution for water quality prediction studies in other areas.
基金sponsored by the National Natural Science Foundation of China(No.51875105).
文摘Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods.To address this challenge,this paper proposes an effective local spectrum enhancement‐based diagnostic method that can identify weak fault frequencies in the original complicated raw signals.For this purpose,a traversal frequency band segmentation technique is first proposed for dividing the raw signal into a series of subfrequency bands.Then,the proposed synthetic quantitative index is constructed for selecting the most informative local frequency band(ILFB)containing fault features from the divided subfrequency bands.Furthermore,an improved grasshopper optimization algorithmbased stochastic resonance(SR)system is developed for enhancing weak fault features contained in the selected most ILFB with less computation cost.Finally,the enhanced weak fault frequencies are extracted from the output of the SR system using a common spectrum analysis.Two experiments on a laboratory planetary gearbox and an open bearing data set are used to verify the effectuality of the proposed method.The diagnostic results demonstrate that the proposed method can identify incipient faults of gears and bearings in an effective and accurate manner.Furthermore,the advantages of the proposed method are highlighted by comparison with other methods.
