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
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
文章以黑龙江省13个地区1967~2016年(50年)旬降水量为例,构建基于蝗虫优化算法改进精细复合多尺度熵模型(The improved refined composite multi-scale entropy based on grasshopper optimization algorithm,GOARCMSE),在此基础上采用...文章以黑龙江省13个地区1967~2016年(50年)旬降水量为例,构建基于蝗虫优化算法改进精细复合多尺度熵模型(The improved refined composite multi-scale entropy based on grasshopper optimization algorithm,GOARCMSE),在此基础上采用信息贡献率方法对不同尺度熵值作加权,全面、准确、可靠地评估区域降水复杂性。此外,基于黑龙江省旬降水复杂性测度结果,探索影响黑龙江省降水复杂性潜在因素。结果表明,黑龙江省旬降水复杂性呈现西部低东部高的显著空间分布特征。此外,水域面积和城建面积与降水复杂性测度结果相关系数分别为-0.629和0.451,存在显著相关关系。为分析模型性能,引入蝗虫优化算法改进多尺度熵模型(The multiscale entropy based on grasshopper optimization algorithm,GOA-MSE),可知GOA-RCMSE区分度和Spearman等级相关系数分别为1.1141和0.995,而GOA-MSE区分度和Spearman等级相关系数分别为1.0935和0.973,表明GOARCMSE具备更高的可靠性和稳定性。综上,GOA-RCMSE可全面合理评价区域降水复杂性,同时为不同区域解决降水复杂性测度问题提供新思路。展开更多
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.
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.
文摘文章以黑龙江省13个地区1967~2016年(50年)旬降水量为例,构建基于蝗虫优化算法改进精细复合多尺度熵模型(The improved refined composite multi-scale entropy based on grasshopper optimization algorithm,GOARCMSE),在此基础上采用信息贡献率方法对不同尺度熵值作加权,全面、准确、可靠地评估区域降水复杂性。此外,基于黑龙江省旬降水复杂性测度结果,探索影响黑龙江省降水复杂性潜在因素。结果表明,黑龙江省旬降水复杂性呈现西部低东部高的显著空间分布特征。此外,水域面积和城建面积与降水复杂性测度结果相关系数分别为-0.629和0.451,存在显著相关关系。为分析模型性能,引入蝗虫优化算法改进多尺度熵模型(The multiscale entropy based on grasshopper optimization algorithm,GOA-MSE),可知GOA-RCMSE区分度和Spearman等级相关系数分别为1.1141和0.995,而GOA-MSE区分度和Spearman等级相关系数分别为1.0935和0.973,表明GOARCMSE具备更高的可靠性和稳定性。综上,GOA-RCMSE可全面合理评价区域降水复杂性,同时为不同区域解决降水复杂性测度问题提供新思路。
