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 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
[ Objective] The relationship between the genetic evolution and phylogenesis of the main grasshopper species in Inner Mongolia grasslands in molecular level was studied. [ Method] Random amplified polymorphic DNA （R...[ Objective] The relationship between the genetic evolution and phylogenesis of the main grasshopper species in Inner Mongolia grasslands in molecular level was studied. [ Method] Random amplified polymorphic DNA （RAPD） technique was used to amplify the 80 individuals of 8 grasshoppers （4 families, 6 genera） in Acridoidea, the polymorphisms of their genomic DNA were compared. [ Result] 64 specific fragments were amplified by 7 primers with the molecular weight of 300 -2 000 bp. The genetic distance between 8 grasshoppers was 0.228 2 -0.589 6. Band pat- tern showed that polymorphism was commonly existed in different genus within the same family and different species within the same genus. The resuits were conducted UPGMA cluster analysis according to Neis＇ genetic distance, the results showed that the species within the same genus first clustered together, then the species in the same family clustered together. [ Condusloa] The study could provide molecular biological basis for system development and evolution research of main grasshoppers in Inner Mongolia grassland.展开更多
The grassland in western Heilongiiang basically belongs to mesophytic meadow grassland with more than 103 species of grasses, while Leymus chinensis meadow grassland and L. chinensis herbage steppe have the largest di...The grassland in western Heilongiiang basically belongs to mesophytic meadow grassland with more than 103 species of grasses, while Leymus chinensis meadow grassland and L. chinensis herbage steppe have the largest distribution. L. chinensis, ArundineUa hirta （ Thunb. ） C. Tanaka, Gleistogenes chinensis （ Max- im. ） Keng, Stipa baicalensis Roshev, Carex tristachya, Deyeuxia langsdorffii （Link） Kunth, Artemisia integrifolia, Carex meyeriana and Cyperus rotundus are the main dominant and subdominant species. There are about 17 common species of grasshoppers causing damage on grassland, while eight of them are dominant spe- cies. According to the characters of grassland, vegetation and the species of grasshoppers, the paper has raised the integrated control measures for grasshoppers in western Heilongjiang.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.
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.
基金Supported by Basic Scientific Research Fund Project of Nonprofit Research Institutions(Grassland Research Institute,Chinese Academy of Agricultural Sciences)~~
文摘[ Objective] The relationship between the genetic evolution and phylogenesis of the main grasshopper species in Inner Mongolia grasslands in molecular level was studied. [ Method] Random amplified polymorphic DNA （RAPD） technique was used to amplify the 80 individuals of 8 grasshoppers （4 families, 6 genera） in Acridoidea, the polymorphisms of their genomic DNA were compared. [ Result] 64 specific fragments were amplified by 7 primers with the molecular weight of 300 -2 000 bp. The genetic distance between 8 grasshoppers was 0.228 2 -0.589 6. Band pat- tern showed that polymorphism was commonly existed in different genus within the same family and different species within the same genus. The resuits were conducted UPGMA cluster analysis according to Neis＇ genetic distance, the results showed that the species within the same genus first clustered together, then the species in the same family clustered together. [ Condusloa] The study could provide molecular biological basis for system development and evolution research of main grasshoppers in Inner Mongolia grassland.
基金Supported by Natural Science Foundation of Heilongjiang Province (C9833)~~
文摘The grassland in western Heilongiiang basically belongs to mesophytic meadow grassland with more than 103 species of grasses, while Leymus chinensis meadow grassland and L. chinensis herbage steppe have the largest distribution. L. chinensis, ArundineUa hirta （ Thunb. ） C. Tanaka, Gleistogenes chinensis （ Max- im. ） Keng, Stipa baicalensis Roshev, Carex tristachya, Deyeuxia langsdorffii （Link） Kunth, Artemisia integrifolia, Carex meyeriana and Cyperus rotundus are the main dominant and subdominant species. There are about 17 common species of grasshoppers causing damage on grassland, while eight of them are dominant spe- cies. According to the characters of grassland, vegetation and the species of grasshoppers, the paper has raised the integrated control measures for grasshoppers in western Heilongjiang.
