Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencie...Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.展开更多
Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In th...Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.展开更多
Maximum Likelihood （ML） method has an excellent performance for Direction-Of-Arrival （DOA） estimation, but a mul- tidimensional nonlinear solution search is required which complicates the computation and prevents ...Maximum Likelihood （ML） method has an excellent performance for Direction-Of-Arrival （DOA） estimation, but a mul- tidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use. To reduce the high computational burden of ML method and make it more suitable to engineering applications, we apply the Artificial Bee Colony （ABC） algorithm to maximize the likelihood function for DOA estimation. As a recently proposed bio-inspired computing algorithm, ABC algorithm is originally used to optimize multivariable functions by imitating the be- havior of bee colony finding excellent nectar sources in the nature environment. It offers an excellent alternative to the con- ventional methods in ML-DOA estimation. The performance of ABC-based ML and other popular meta-heuristic-based ML methods for DOA estimation are compared for various scenarios of convergence, Signal-to-Noise Ratio （SNR）, and number of iterations. The computation loads of ABC-based ML and the conventional ML methods for DOA estimation are also investi- gated. Simulation results demonstrate that the proposed ABC based method is more efficient in computation and statistical performance than other ML-based DOA estimation methods.展开更多
Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the n...Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the near future. On the basisof analyzing the structure of the MCAV/UCAV cooperative engage-ment system, this paper divides the unique system into three hi-erarchical levels, respectively, i.e., mission level, task-cluster leveland task level. To solve the formation and adjustment problem ofthe latter two levels, three corresponding mathematical modelsare established. To solve these models, three algorithms calledquantum artificial bee colony （QABC） algorithm, greedy strategy（GS） and two-stage greedy strategy （TSGS） are proposed. Finally,a series of simulation experiments are designed to verify the effec-tiveness and superiority of the proposed algorithms.展开更多
Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble ...Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability.In the hybrid stacking ensemble approach,we used an artificial bee colony(ABC)algorithm to find out the best combination of base classifiers(level 0)and determined a suitable meta-classifier(level 1)from a pool of 11 individual optimized machine learning(OML)algorithms.Finite element analysis(FEA)was conducted in order to form the synthetic database for the training stage(150 cases)of the proposed model while 107 real field slope cases were used for the testing stage.The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix,F1-score,and area under the curve,i.e.AUC-score.The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble(AUC?90.4%),which is 7%higher than the best of the 11 individual OML methods(AUC?82.9%).Then,a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction.The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method.Finally,the importance of the variables for slope stability was studied using linear vector quantization(LVQ)method.展开更多
基于Zig Bee协议的无线传感网络采集到的温湿度数据被存储到基站的SQLCE嵌入式数据库中。一个远程用户要连接到基站获得数据,实现两者即时通信,Internet通信是最快捷的方式,依此开发了基于Socket网络编程的无线传感器网络远程监控系统...基于Zig Bee协议的无线传感网络采集到的温湿度数据被存储到基站的SQLCE嵌入式数据库中。一个远程用户要连接到基站获得数据,实现两者即时通信,Internet通信是最快捷的方式,依此开发了基于Socket网络编程的无线传感器网络远程监控系统。实验系统以课题组自行开发的一种无线传感器网络系统为硬件平台,利用Visual Studio 2005开发环境,结合嵌入式数据库SQLCE开发技术,采用Sockets套接字,运用C#编程语言实现远程计算机对无线传感器网络数据的实时显示、存储和查询等功能。展开更多
基金supported by the National Natural Science Foundation of China(7177121671701209)
文摘Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.
基金supported in part by the National Natural Science Foundation of China(62073330)in part by the Natural Science Foundation of Hunan Province(2019JJ20021,2020JJ4339)in part by the Scientific Research Fund of Hunan Province Education Department(20B272)。
文摘Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.
文摘Maximum Likelihood （ML） method has an excellent performance for Direction-Of-Arrival （DOA） estimation, but a mul- tidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use. To reduce the high computational burden of ML method and make it more suitable to engineering applications, we apply the Artificial Bee Colony （ABC） algorithm to maximize the likelihood function for DOA estimation. As a recently proposed bio-inspired computing algorithm, ABC algorithm is originally used to optimize multivariable functions by imitating the be- havior of bee colony finding excellent nectar sources in the nature environment. It offers an excellent alternative to the con- ventional methods in ML-DOA estimation. The performance of ABC-based ML and other popular meta-heuristic-based ML methods for DOA estimation are compared for various scenarios of convergence, Signal-to-Noise Ratio （SNR）, and number of iterations. The computation loads of ABC-based ML and the conventional ML methods for DOA estimation are also investi- gated. Simulation results demonstrate that the proposed ABC based method is more efficient in computation and statistical performance than other ML-based DOA estimation methods.
基金supported by the National Natural Science Foundation of China(61573017)the Doctoral Innovation Found of Air Force Engineering University(KGD08101604)
文摘Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the near future. On the basisof analyzing the structure of the MCAV/UCAV cooperative engage-ment system, this paper divides the unique system into three hi-erarchical levels, respectively, i.e., mission level, task-cluster leveland task level. To solve the formation and adjustment problem ofthe latter two levels, three corresponding mathematical modelsare established. To solve these models, three algorithms calledquantum artificial bee colony （QABC） algorithm, greedy strategy（GS） and two-stage greedy strategy （TSGS） are proposed. Finally,a series of simulation experiments are designed to verify the effec-tiveness and superiority of the proposed algorithms.
基金We acknowledge the funding support from Australia Research Council(Grant Nos.DP200100549 and IH180100010).
文摘Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability.In the hybrid stacking ensemble approach,we used an artificial bee colony(ABC)algorithm to find out the best combination of base classifiers(level 0)and determined a suitable meta-classifier(level 1)from a pool of 11 individual optimized machine learning(OML)algorithms.Finite element analysis(FEA)was conducted in order to form the synthetic database for the training stage(150 cases)of the proposed model while 107 real field slope cases were used for the testing stage.The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix,F1-score,and area under the curve,i.e.AUC-score.The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble(AUC?90.4%),which is 7%higher than the best of the 11 individual OML methods(AUC?82.9%).Then,a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction.The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method.Finally,the importance of the variables for slope stability was studied using linear vector quantization(LVQ)method.
文摘基于Zig Bee协议的无线传感网络采集到的温湿度数据被存储到基站的SQLCE嵌入式数据库中。一个远程用户要连接到基站获得数据,实现两者即时通信,Internet通信是最快捷的方式,依此开发了基于Socket网络编程的无线传感器网络远程监控系统。实验系统以课题组自行开发的一种无线传感器网络系统为硬件平台,利用Visual Studio 2005开发环境,结合嵌入式数据库SQLCE开发技术,采用Sockets套接字,运用C#编程语言实现远程计算机对无线传感器网络数据的实时显示、存储和查询等功能。
