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
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
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
针对白骨顶鸟优化算法(COOT)寻优精度低、容易陷入局部最优、收敛速度慢等问题,提出了基于柯西变异和差分进化的混沌白骨顶鸟算法(Logistic Chaos Coot bird algorithm based on Cauchy mutation and Differential evolution,CDLCOOT)...针对白骨顶鸟优化算法(COOT)寻优精度低、容易陷入局部最优、收敛速度慢等问题,提出了基于柯西变异和差分进化的混沌白骨顶鸟算法(Logistic Chaos Coot bird algorithm based on Cauchy mutation and Differential evolution,CDLCOOT)。首先,通过柯西变异使白骨顶鸟位置发生扰动,扩大搜索范围,提高算法的全局搜索能力;其次,对领导者白骨顶鸟采取差分进化策略,增加种群多样性,使适应度更好的领导者带领种群寻优,引导白骨顶鸟个体向最优解前进,帮助其更快地搜索;最后,在白骨顶鸟进行链式运动时加入logistic混沌因子,从而实现混沌的链式跟随运动,提高算法跳出局部最优的能力。在12个经典的测试函数和9个CEC2017测试函数上进行仿真实验,将CDLCOOT算法与正余弦算法(SCA)、灰狼优化算法(GWO)、蚁狮优化算法(ALO)、黑洞模拟算法(MVO)等其他先进算法及原始COOT算法、具有单一策略的原算法进行对比,验证改进算法的有效性。实验结果表明,CDLCOOT算法相比其他启发式算法和改进算法具有更好的全局寻优能力和更快的收敛速度。在经典测试函数中,对于4个单模态函数,CDLCOOT算法寻优平均值相比原始算法平均提高了76个数量级;在2个多模态函数上寻到理论最优值,在另外2个多模态函数上寻优平均值分别比原始算法提高了三四个数量级;在4个固定维度多模态函数上,算法都能寻到理论最优值,收敛速度更快。在CEC2017测试函数中,所提算法在单模态、多模态和混合模态上的收敛精度相比原算法都有所提升,且其收敛速度也比原算法和其他算法更快,算法稳定性更高。展开更多
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%.
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.
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%.
文摘针对白骨顶鸟优化算法(COOT)寻优精度低、容易陷入局部最优、收敛速度慢等问题,提出了基于柯西变异和差分进化的混沌白骨顶鸟算法(Logistic Chaos Coot bird algorithm based on Cauchy mutation and Differential evolution,CDLCOOT)。首先,通过柯西变异使白骨顶鸟位置发生扰动,扩大搜索范围,提高算法的全局搜索能力;其次,对领导者白骨顶鸟采取差分进化策略,增加种群多样性,使适应度更好的领导者带领种群寻优,引导白骨顶鸟个体向最优解前进,帮助其更快地搜索;最后,在白骨顶鸟进行链式运动时加入logistic混沌因子,从而实现混沌的链式跟随运动,提高算法跳出局部最优的能力。在12个经典的测试函数和9个CEC2017测试函数上进行仿真实验,将CDLCOOT算法与正余弦算法(SCA)、灰狼优化算法(GWO)、蚁狮优化算法(ALO)、黑洞模拟算法(MVO)等其他先进算法及原始COOT算法、具有单一策略的原算法进行对比,验证改进算法的有效性。实验结果表明,CDLCOOT算法相比其他启发式算法和改进算法具有更好的全局寻优能力和更快的收敛速度。在经典测试函数中,对于4个单模态函数,CDLCOOT算法寻优平均值相比原始算法平均提高了76个数量级;在2个多模态函数上寻到理论最优值,在另外2个多模态函数上寻优平均值分别比原始算法提高了三四个数量级;在4个固定维度多模态函数上,算法都能寻到理论最优值,收敛速度更快。在CEC2017测试函数中,所提算法在单模态、多模态和混合模态上的收敛精度相比原算法都有所提升,且其收敛速度也比原算法和其他算法更快,算法稳定性更高。