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
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
This study assessed the effect of cyanogenic potential (CNP) in leaf tissue on grasshopper incidence and severity of damage in cassava for the identification of parents with desired complementary traits for crossing. T...This study assessed the effect of cyanogenic potential (CNP) in leaf tissue on grasshopper incidence and severity of damage in cassava for the identification of parents with desired complementary traits for crossing. The experiment was conducted at the Foya Wulleh, Njala experimental site in Sierra Leone during 2020 and 2021 cropping seasons in a randomized complete block design with three replications. A total of 30 genotypes comprising 26 breeding lines, two improved and two local genotypes were assessed. Results showed a significant (p < 0.05) linear relationship between leaf CNP and grasshopper infestation (incidence and severity of damage) among cassava genotypes. Findings showed that the higher leaf CNP, the lower the grasshopper infestation in cassava genotypes. About two genotypes (Cooksoon and Cocoa) had low leaf CNP;three genotypes (TR0020, TR0037 and TR0013) CNP had moderately low leaf CNP;eight genotypes (SLICASS 6, TR0029, TR0032, TR0011, TR0012, TR0016-1/17, TR0002 and TR0010) had intermediate leaf CNP;seven (TR0009, TR0015-1/17, TR0036, TR0022-1/17, SLICASS 4, TR0007 and TR0026-1/17) had moderately high leaf CNP;eight (TR0008, TR0019-1/17, TR0006, TR0005, TR0021, TR0021-1/17, TR0022 and TR0024-1/17) had high leaf CNP;and two genotypes (TR0001 and TR0018-1/17) had very high leaf CNP. This suggests the indirect dependence of leaf cyanogenic potential on grasshopper infestation (incidence and severity of damage) in cassava that could be exploited for the genetic improvement of cassava for improved resistance to grasshopper infestation, nutrition and utilization of the crop.展开更多
Female grasshoppers can affect the fitness of their offspring through their selection of oviposition site. Knowledge of soil type on oviposition, and its effects on subsequent development can provide guidelines for ha...Female grasshoppers can affect the fitness of their offspring through their selection of oviposition site. Knowledge of soil type on oviposition, and its effects on subsequent development can provide guidelines for habitat manipulations that reduce the harmful effects of these pests on farmers fields. The influence of soil types on the oviposition site preference of variegated grasshopper (Zonocerus variegatus L.) reared some cassava (Manihot esculenta Crantz) varieties, was investigated in a cage trial carried out at the Bio factory laboratory, School of Agriculture and Food Sciences, Njala University, Sierra Leone during 2022/2023. The treatments comprised three soil types (Sandy, Loamy and Clay), each with three replications laid out in a randomized complete block design (RCBD) in wooden cages. Data were collected on the following development parameters including, Net reproductive growth ratio (R0), Generation time (Tc), Intrinsic rate of increase (rm), Finite rate of increase (), Doubling time (Dt), and overall survivorship. Findings revealed that, Z. variegatus L. preferred sandy soil in which, on average, most eggs were deposited (338, 6.62 4.40), followed by loamy soil, 286 (5.53 3.96), and then, clayey soil, 200 (3.91 3.85);though, the differences were not significant. This study established that Z. variegatus deposited more eggs in sandy soil > loamy soil > clayey soil, respectively;and subsequent survivorship of the immature unto mature adult insect, revealed a similar order. This indicates that the sandy soil is the most preferred substrate for oviposition and subsequent development into adult insects.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.
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This study assessed the effect of cyanogenic potential (CNP) in leaf tissue on grasshopper incidence and severity of damage in cassava for the identification of parents with desired complementary traits for crossing. The experiment was conducted at the Foya Wulleh, Njala experimental site in Sierra Leone during 2020 and 2021 cropping seasons in a randomized complete block design with three replications. A total of 30 genotypes comprising 26 breeding lines, two improved and two local genotypes were assessed. Results showed a significant (p < 0.05) linear relationship between leaf CNP and grasshopper infestation (incidence and severity of damage) among cassava genotypes. Findings showed that the higher leaf CNP, the lower the grasshopper infestation in cassava genotypes. About two genotypes (Cooksoon and Cocoa) had low leaf CNP;three genotypes (TR0020, TR0037 and TR0013) CNP had moderately low leaf CNP;eight genotypes (SLICASS 6, TR0029, TR0032, TR0011, TR0012, TR0016-1/17, TR0002 and TR0010) had intermediate leaf CNP;seven (TR0009, TR0015-1/17, TR0036, TR0022-1/17, SLICASS 4, TR0007 and TR0026-1/17) had moderately high leaf CNP;eight (TR0008, TR0019-1/17, TR0006, TR0005, TR0021, TR0021-1/17, TR0022 and TR0024-1/17) had high leaf CNP;and two genotypes (TR0001 and TR0018-1/17) had very high leaf CNP. This suggests the indirect dependence of leaf cyanogenic potential on grasshopper infestation (incidence and severity of damage) in cassava that could be exploited for the genetic improvement of cassava for improved resistance to grasshopper infestation, nutrition and utilization of the crop.
文摘Female grasshoppers can affect the fitness of their offspring through their selection of oviposition site. Knowledge of soil type on oviposition, and its effects on subsequent development can provide guidelines for habitat manipulations that reduce the harmful effects of these pests on farmers fields. The influence of soil types on the oviposition site preference of variegated grasshopper (Zonocerus variegatus L.) reared some cassava (Manihot esculenta Crantz) varieties, was investigated in a cage trial carried out at the Bio factory laboratory, School of Agriculture and Food Sciences, Njala University, Sierra Leone during 2022/2023. The treatments comprised three soil types (Sandy, Loamy and Clay), each with three replications laid out in a randomized complete block design (RCBD) in wooden cages. Data were collected on the following development parameters including, Net reproductive growth ratio (R0), Generation time (Tc), Intrinsic rate of increase (rm), Finite rate of increase (), Doubling time (Dt), and overall survivorship. Findings revealed that, Z. variegatus L. preferred sandy soil in which, on average, most eggs were deposited (338, 6.62 4.40), followed by loamy soil, 286 (5.53 3.96), and then, clayey soil, 200 (3.91 3.85);though, the differences were not significant. This study established that Z. variegatus deposited more eggs in sandy soil > loamy soil > clayey soil, respectively;and subsequent survivorship of the immature unto mature adult insect, revealed a similar order. This indicates that the sandy soil is the most preferred substrate for oviposition and subsequent development into adult insects.
