为了提高超短期风电功率的预测精度,提出了一种基于COOT算法优化的变分模态分解(VMD)、分层主成分分析(hierarchical principal components analysis,HPCA)与门控循环单元神经网络(GRU)的组合预测模型。首先,利用能量差值法确定变分模...为了提高超短期风电功率的预测精度,提出了一种基于COOT算法优化的变分模态分解(VMD)、分层主成分分析(hierarchical principal components analysis,HPCA)与门控循环单元神经网络(GRU)的组合预测模型。首先,利用能量差值法确定变分模态分解子模态数,从而将具有强非线性的原始功率序列分解为一组相对平稳的子模态。其次,利用灰色关联度分析计算高维气象特征与功率序列的关联度值并进行排序分层,利用主成分分析提取各分层特征变量的第一主成分,实现对高维气象特征的降维。最后,引入COOT算法对门控循环单元预测模型的超参数进行优化,加速模型收敛速度,提高模型预测精度。对贵州某风电场的实测数据进行仿真分析,结果表明:相较于传统GRU模型的预测结果,所提方法的均方根误差、平均绝对误差、平均绝对百分误差分别下降了67.41%、72.25%、45.69%,且预测精度高于其他4种组合预测模型,有效提高了超短期风电功率预测精度。展开更多
城市作战的重要性日益凸显,城市作战路径规划也受到了更多的关注。如何在城市复杂的环境和众多危险区中寻找安全迅速的路径是非常重要的。为保障作战安全,提出了一种基于选拔科特鸟和路径缩减的不规则危险区路径规划算法。首先,结合城...城市作战的重要性日益凸显,城市作战路径规划也受到了更多的关注。如何在城市复杂的环境和众多危险区中寻找安全迅速的路径是非常重要的。为保障作战安全,提出了一种基于选拔科特鸟和路径缩减的不规则危险区路径规划算法。首先,结合城市危险区特征和受限情况以构建更符合真实战场的不规则危险区数学模型。其次,建立路径空间缩减模型对路径威胁度进行评估和量化,以剔除掉高威胁路径来降低作战风险。最后,基于选拔策略的科特鸟优化算法(COOT Bird Optimization Algorithm based on Selection Strategy,SS-COOT)结合优质个体以提高算法的寻优效率。经实验验证,该算法在结合不规则危险区的城市路径规划问题上具有搜索速度快、寻优效果好的特点。展开更多
Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose var...Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold-start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and mean reciprocal rank (MRR) by 5.7%.展开更多
Territory and territorial behavior of the Common Coot（Fulica atra） were studied in two breeding sites,Anbanghe Nature Reserve and Daqing Longfeng wetland,in Heilongjiang Province,China from April to October in 2008 ...Territory and territorial behavior of the Common Coot（Fulica atra） were studied in two breeding sites,Anbanghe Nature Reserve and Daqing Longfeng wetland,in Heilongjiang Province,China from April to October in 2008 and 2009.In the breeding season,the breeding pairs occupied an area and protected it throughout the reproduction,and both interspecific and intraspecific conflicts were observed.Territory activities became severe since early May,the peak of territory behaviors appeared at late May,and then declined gradually.The territorial activities level was higher than that in the nest building period than in the laying and incubation periods.The most adopted behavioral model was expelling,which was the least energy cost.The degree of territorial behavior tended to be descended since the development of breeding phase.The territory size differed from 1 333 m2 to above 5 000 m2.Wintering population was observed in Poyang Lake of Jiangxi Province.The coots gathered in the open water;however,there was no territory behavior both in the interspecies and intraspecies in wintering sites.The hypotheses why there was territory behaviors for coots both in the interspecies and intraspecies were also discussed.展开更多
Harris Hawks Optimization(HHO)is a novel meta-heuristic algorithm that imitates the predation characteristics of Harris Hawk and combines Lévy flight to solve complex multidimensional problems.Nevertheless,the ba...Harris Hawks Optimization(HHO)is a novel meta-heuristic algorithm that imitates the predation characteristics of Harris Hawk and combines Lévy flight to solve complex multidimensional problems.Nevertheless,the basic HHO algorithm still has certain limitations,including the tendency to fall into the local optima and poor convergence accuracy.Coot Bird Optimization(CBO)is another new swarm-based optimization algorithm.CBO originates from the regular and irregular motion of a bird called Coot on the water’s surface.Although the framework of CBO is slightly complicated,it has outstanding exploration potential and excellent capability to avoid falling into local optimal solutions.This paper proposes a novel enhanced hybrid algorithm based on the basic HHO and CBO named Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization(EHHOCBO).EHHOCBO can provide higher-quality solutions for numerical optimization problems.It first embeds the leadership mechanism of CBO into the population initialization process of HHO.This way can take full advantage of the valuable solution information to provide a good foundation for the global search of the hybrid algorithm.Secondly,the Ensemble Mutation Strategy(EMS)is introduced to generate the mutant candidate positions for consideration,further improving the hybrid algorithm’s exploration trend and population diversity.To further reduce the likelihood of falling into the local optima and speed up the convergence,Refracted Opposition-Based Learning(ROBL)is adopted to update the current optimal solution in the swarm.Using 23 classical benchmark functions and the IEEE CEC2017 test suite,the performance of the proposed EHHOCBO is comprehensively evaluated and compared with eight other basic meta-heuristic algorithms and six improved variants.Experimental results show that EHHOCBO can achieve better solution accuracy,faster convergence speed,and a more robust ability to jump out of local optima than other advanced optimizers in most test cases.Finally,EHHOCBOis applied to address four engineering design problems.Our findings indicate that the proposed method also provides satisfactory performance regarding the convergence accuracy of the optimal global solution.展开更多
The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation...The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers.Cyber-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)attacks.In this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their damage.The recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying cyber-attacks.The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT environment.The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment.To accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test.To detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this study.Finally,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency.The proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct aspects.The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.展开更多
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi...The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.展开更多
In this paper,load frequency control is performed for a two-area power system incorporating a high penetration of renewable energy sources.A droop controller for a type 3 wind turbine is used to extract the stored kin...In this paper,load frequency control is performed for a two-area power system incorporating a high penetration of renewable energy sources.A droop controller for a type 3 wind turbine is used to extract the stored kinetic energy from the rotating masses during sudden load disturbances.An auxiliary storage controller is applied to achieve effec-tive frequency response.The coot optimization algorithm(COA)is applied to allocate the optimum parameters of the fractional-order proportional integral derivative(FOPID),droop and auxiliary storage controllers.The fitness function is represented by the summation of integral square deviations in tie line power,and Areas 1 and 2 frequency errors.The robustness of the COA is proven by comparing the results with benchmarked optimizers including:atomic orbital search,honey badger algorithm,water cycle algorithm and particle swarm optimization.Performance assessment is confirmed in the following four scenarios:(i)optimization while including PID controllers;(ii)optimization while including FOPID controllers;(iii)validation of COA results under various load disturbances;and(iv)validation of the proposed controllers under varying weather conditions.展开更多
Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly ob...Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.展开更多
文摘为了提高超短期风电功率的预测精度,提出了一种基于COOT算法优化的变分模态分解(VMD)、分层主成分分析(hierarchical principal components analysis,HPCA)与门控循环单元神经网络(GRU)的组合预测模型。首先,利用能量差值法确定变分模态分解子模态数,从而将具有强非线性的原始功率序列分解为一组相对平稳的子模态。其次,利用灰色关联度分析计算高维气象特征与功率序列的关联度值并进行排序分层,利用主成分分析提取各分层特征变量的第一主成分,实现对高维气象特征的降维。最后,引入COOT算法对门控循环单元预测模型的超参数进行优化,加速模型收敛速度,提高模型预测精度。对贵州某风电场的实测数据进行仿真分析,结果表明:相较于传统GRU模型的预测结果,所提方法的均方根误差、平均绝对误差、平均绝对百分误差分别下降了67.41%、72.25%、45.69%,且预测精度高于其他4种组合预测模型,有效提高了超短期风电功率预测精度。
文摘城市作战的重要性日益凸显,城市作战路径规划也受到了更多的关注。如何在城市复杂的环境和众多危险区中寻找安全迅速的路径是非常重要的。为保障作战安全,提出了一种基于选拔科特鸟和路径缩减的不规则危险区路径规划算法。首先,结合城市危险区特征和受限情况以构建更符合真实战场的不规则危险区数学模型。其次,建立路径空间缩减模型对路径威胁度进行评估和量化,以剔除掉高威胁路径来降低作战风险。最后,基于选拔策略的科特鸟优化算法(COOT Bird Optimization Algorithm based on Selection Strategy,SS-COOT)结合优质个体以提高算法的寻优效率。经实验验证,该算法在结合不规则危险区的城市路径规划问题上具有搜索速度快、寻优效果好的特点。
文摘Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold-start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and mean reciprocal rank (MRR) by 5.7%.
基金supported by Natural Science Foundation of Heilongjiang Province (C201036)
文摘Territory and territorial behavior of the Common Coot（Fulica atra） were studied in two breeding sites,Anbanghe Nature Reserve and Daqing Longfeng wetland,in Heilongjiang Province,China from April to October in 2008 and 2009.In the breeding season,the breeding pairs occupied an area and protected it throughout the reproduction,and both interspecific and intraspecific conflicts were observed.Territory activities became severe since early May,the peak of territory behaviors appeared at late May,and then declined gradually.The territorial activities level was higher than that in the nest building period than in the laying and incubation periods.The most adopted behavioral model was expelling,which was the least energy cost.The degree of territorial behavior tended to be descended since the development of breeding phase.The territory size differed from 1 333 m2 to above 5 000 m2.Wintering population was observed in Poyang Lake of Jiangxi Province.The coots gathered in the open water;however,there was no territory behavior both in the interspecies and intraspecies in wintering sites.The hypotheses why there was territory behaviors for coots both in the interspecies and intraspecies were also discussed.
基金supported by the National Natural Science Foundation of China under Grant 52075090Key Research and Development Program Projects of Heilongjiang Province under Grant GA21A403+1 种基金the Fundamental Research Funds for the Central Universities under Grant 2572021BF01Natural Science Foundation of Heilongjiang Province under Grant YQ2021E002.
文摘Harris Hawks Optimization(HHO)is a novel meta-heuristic algorithm that imitates the predation characteristics of Harris Hawk and combines Lévy flight to solve complex multidimensional problems.Nevertheless,the basic HHO algorithm still has certain limitations,including the tendency to fall into the local optima and poor convergence accuracy.Coot Bird Optimization(CBO)is another new swarm-based optimization algorithm.CBO originates from the regular and irregular motion of a bird called Coot on the water’s surface.Although the framework of CBO is slightly complicated,it has outstanding exploration potential and excellent capability to avoid falling into local optimal solutions.This paper proposes a novel enhanced hybrid algorithm based on the basic HHO and CBO named Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization(EHHOCBO).EHHOCBO can provide higher-quality solutions for numerical optimization problems.It first embeds the leadership mechanism of CBO into the population initialization process of HHO.This way can take full advantage of the valuable solution information to provide a good foundation for the global search of the hybrid algorithm.Secondly,the Ensemble Mutation Strategy(EMS)is introduced to generate the mutant candidate positions for consideration,further improving the hybrid algorithm’s exploration trend and population diversity.To further reduce the likelihood of falling into the local optima and speed up the convergence,Refracted Opposition-Based Learning(ROBL)is adopted to update the current optimal solution in the swarm.Using 23 classical benchmark functions and the IEEE CEC2017 test suite,the performance of the proposed EHHOCBO is comprehensively evaluated and compared with eight other basic meta-heuristic algorithms and six improved variants.Experimental results show that EHHOCBO can achieve better solution accuracy,faster convergence speed,and a more robust ability to jump out of local optima than other advanced optimizers in most test cases.Finally,EHHOCBOis applied to address four engineering design problems.Our findings indicate that the proposed method also provides satisfactory performance regarding the convergence accuracy of the optimal global solution.
基金This research was supported by the Universiti Sains Malaysia(USM)and the ministry of Higher Education Malaysia through Fundamental Research GrantScheme(FRGS-Grant No:FRGS/1/2020/TK0/USM/02/1).
文摘The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers.Cyber-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)attacks.In this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their damage.The recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying cyber-attacks.The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT environment.The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment.To accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test.To detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this study.Finally,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency.The proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct aspects.The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.
文摘The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.
文摘In this paper,load frequency control is performed for a two-area power system incorporating a high penetration of renewable energy sources.A droop controller for a type 3 wind turbine is used to extract the stored kinetic energy from the rotating masses during sudden load disturbances.An auxiliary storage controller is applied to achieve effec-tive frequency response.The coot optimization algorithm(COA)is applied to allocate the optimum parameters of the fractional-order proportional integral derivative(FOPID),droop and auxiliary storage controllers.The fitness function is represented by the summation of integral square deviations in tie line power,and Areas 1 and 2 frequency errors.The robustness of the COA is proven by comparing the results with benchmarked optimizers including:atomic orbital search,honey badger algorithm,water cycle algorithm and particle swarm optimization.Performance assessment is confirmed in the following four scenarios:(i)optimization while including PID controllers;(ii)optimization while including FOPID controllers;(iii)validation of COA results under various load disturbances;and(iv)validation of the proposed controllers under varying weather conditions.
基金funded by the Deanship of Scientific Research(DSR),King Abdulaziz University(KAU),Jeddah,Saudi Arabia under Grant No.(IFPIP:631-612-1443).
文摘Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.
