In the Internet of Things(IoT),the users have complex needs,and the Web Service Composition(WSC)was introduced to address these needs.The WSC’s main objective is to search for the optimal combination of web services ...In the Internet of Things(IoT),the users have complex needs,and the Web Service Composition(WSC)was introduced to address these needs.The WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services(QoS)constraints.The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints.In this paper,we introduce an extension of our previous works on the Artificial Bee Colony(ABC)and Bat Algorithm(BA).A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search.The bat agent is used to improve the solution of exhausted bees after a threshold(limits),and also an Elitist Strategy(ES)is added to BA to increase the convergence rate.The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-ofthe-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria(average fitness values,best fitness values,and execution time)that were measured for 30 different runs.These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets.The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors.TheWilcoxon signed-rank significant test is usedwhere the proposed algorithm results significantly differ fromother algorithms on 100%of datasets.展开更多
An optimization approach based on Artificial Bee Colony(ABC)algorithm is proposed for structural local damage detection in this study.The objective function for the damage identification problem is established by stru...An optimization approach based on Artificial Bee Colony(ABC)algorithm is proposed for structural local damage detection in this study.The objective function for the damage identification problem is established by structural parameters and modal assurance criteria(MAC).The ABC algorithm is presented to solve the certain objective function.Then the Tournament Selection Strategy and chaotic search mechanism is adopted to enhance global search ability of the certain algorithm.A coupled double-beam system is studied as numerical example to illustrate the correctness and efficiency of the propose method.The simulation results show that the modified ABC algorithm can identify the local damage of the structural system efficiently even under measurement noise,which demonstrates the proposed algorithm has a higher damage diagnosis precision.展开更多
Internet of things(IoT) imposes new challenges on service composition as it is difficult to manage a quick instantiation of a complex services from a growing number of dynamic candidate services. A cross-modified Arti...Internet of things(IoT) imposes new challenges on service composition as it is difficult to manage a quick instantiation of a complex services from a growing number of dynamic candidate services. A cross-modified Artificial Bee Colony Algorithm(CMABC) is proposed to achieve the optimal solution services in an acceptable time and high accuracy. Firstly, web service instantiation model was established. What is more, to overcome the problem of discrete and chaotic solution space, the global optimal solution was used to accelerate convergence rate by imitating the cross operation of Genetic algorithm(GA). The simulation experiment result shows that CMABC exhibited faster convergence speed and better convergence accuracy than some other intelligent optimization algorithms.展开更多
In order to solve the problems of multi-parameter,multi-extreme and multi-solution in the nonlinear iterative optimization process of Rayleigh wave inversion,the artificial bee colony(ABC)algorithm is selected for glo...In order to solve the problems of multi-parameter,multi-extreme and multi-solution in the nonlinear iterative optimization process of Rayleigh wave inversion,the artificial bee colony(ABC)algorithm is selected for global nonlinear inversion.The global nonlinear inversion method does not rely on a strict initial model and does not need to calculate the derivative of the objective function.The ABC algorithm uses the local optimization behavior of each individual artificial bee to finally highlight the global optimal value in the colony,and the convergence speed is faster.While searching for the global optimal solution,an effective local search can also be performed to ensure the reliability of the inversion results.This paper uses the ABC algorithm to perform Rayleigh wave dispersion inversion on the actual seismic data to obtain a clear undergrounding of shear wave velocity profile and accurately identify the location of the high-velocity interlayer.It is verified that the ABC algorithm used in the inversion of the Rayleigh wave dispersion curve is stable and converges quickly.展开更多
The patterns of ultrasonic backscattered echoes represent valuable information pertaining to the geometric shape, size, and orientation of the reflectors as well as the microstructure of the propagation path. Accurate...The patterns of ultrasonic backscattered echoes represent valuable information pertaining to the geometric shape, size, and orientation of the reflectors as well as the microstructure of the propagation path. Accurate estimation of the ultrasonic echo pattern is essential in determining the object or propagation path properties. This paper proposes a parameter estimation method for ultrasonic echoes based on Artificial Bee Colony (ABC) algorithm which is one of the most recent swarm intelligence based algorithms. A modified ABC (MABC) algorithm is given by adding an adjusting factor to the neighborhood search formula of traditional ABC algorithm in order to enhance its performance. The algorithm could overcome the impact of different search range on estimation accuracy to solve the multi-dimensional parameter optimization problems. The performance of the MABC algorithm is demonstrated by numerical simulation and ultrasonic detection experiments. Results show that MABC not only can accurately estimate various parameters of the ultrasonic echoes, but also can achieve the optimal solution in the global scope. The proposed algorithm also has the advantages of fast convergence speed, short running time and real-time parameters esti- mation.展开更多
针对滤波器组多载波-偏移正交幅度调制技术(Filter Bank Multicarrier-Offset Quadrature Amplitude Modulation,FBMC-OQAM)存在峰均功率比(Peak-to-Average Power Ratio,PAPR)过高的问题,以及传统部分传输序列(Partial Transfer Sequen...针对滤波器组多载波-偏移正交幅度调制技术(Filter Bank Multicarrier-Offset Quadrature Amplitude Modulation,FBMC-OQAM)存在峰均功率比(Peak-to-Average Power Ratio,PAPR)过高的问题,以及传统部分传输序列(Partial Transfer Sequence,PTS)算法对PAPR抑制效果不明显,提出了一种新的基于自适应搜索策略的人工蜂群部分传输序列算法(Adaptive Search Strategy Based Artifical Bee Colony PTS,ASSABC-PTS)。首先,根据FBMC-OQAM系统特性,利用传统PTS算法对系统进行初步优化,以降低FBMC-OQAM系统的PAPR;然后,针对PTS算法中存在的计算复杂度问题,引入人工蜂群(Artificial Bee Colony,ABC)算法进行优化;最后,在ABC算法中引入自适应搜索策略提升算法的局部寻优能力,加快ABC算法的收敛速度和搜索精度。仿真实验表明,ASSABC-PTS在有效降低系统算法复杂度的同时,也极大降低了FBMC-OQAM系统的峰均功率比。展开更多
基金The authors extend their appreciation to the Deputyship for Research and Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number 2022/01/22636.
文摘In the Internet of Things(IoT),the users have complex needs,and the Web Service Composition(WSC)was introduced to address these needs.The WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services(QoS)constraints.The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints.In this paper,we introduce an extension of our previous works on the Artificial Bee Colony(ABC)and Bat Algorithm(BA).A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search.The bat agent is used to improve the solution of exhausted bees after a threshold(limits),and also an Elitist Strategy(ES)is added to BA to increase the convergence rate.The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-ofthe-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria(average fitness values,best fitness values,and execution time)that were measured for 30 different runs.These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets.The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors.TheWilcoxon signed-rank significant test is usedwhere the proposed algorithm results significantly differ fromother algorithms on 100%of datasets.
基金the National Natural Science Foundation of China(11172333,11272361)the Fundamental Research Funds for the Central Universities(13lgzd06)+1 种基金Doctoral Program Foundation of Ministry of Education of China(20130171110039)the Guangdong Province Science and Technology Program(2012A030200011)。
文摘An optimization approach based on Artificial Bee Colony(ABC)algorithm is proposed for structural local damage detection in this study.The objective function for the damage identification problem is established by structural parameters and modal assurance criteria(MAC).The ABC algorithm is presented to solve the certain objective function.Then the Tournament Selection Strategy and chaotic search mechanism is adopted to enhance global search ability of the certain algorithm.A coupled double-beam system is studied as numerical example to illustrate the correctness and efficiency of the propose method.The simulation results show that the modified ABC algorithm can identify the local damage of the structural system efficiently even under measurement noise,which demonstrates the proposed algorithm has a higher damage diagnosis precision.
基金supported by a grant from the Project "Multifunctional mobile phone R & D and industrialization of the Internet of things" supported by the Project of the Provincial Department of research (2011A090200008)partly supported by National Science and Technology Major Project (No. 2010ZX07102-006)+3 种基金the National Basic Research Program of China (973 Program) (No. 2011CB505402)the Major Program of the National Natural Science Foundation of China (No. 61170117)the National Natural Science Foundation of China (No.61432004)the National Key Research and Development Program (No.2016YFB1001404)
文摘Internet of things(IoT) imposes new challenges on service composition as it is difficult to manage a quick instantiation of a complex services from a growing number of dynamic candidate services. A cross-modified Artificial Bee Colony Algorithm(CMABC) is proposed to achieve the optimal solution services in an acceptable time and high accuracy. Firstly, web service instantiation model was established. What is more, to overcome the problem of discrete and chaotic solution space, the global optimal solution was used to accelerate convergence rate by imitating the cross operation of Genetic algorithm(GA). The simulation experiment result shows that CMABC exhibited faster convergence speed and better convergence accuracy than some other intelligent optimization algorithms.
文摘In order to solve the problems of multi-parameter,multi-extreme and multi-solution in the nonlinear iterative optimization process of Rayleigh wave inversion,the artificial bee colony(ABC)algorithm is selected for global nonlinear inversion.The global nonlinear inversion method does not rely on a strict initial model and does not need to calculate the derivative of the objective function.The ABC algorithm uses the local optimization behavior of each individual artificial bee to finally highlight the global optimal value in the colony,and the convergence speed is faster.While searching for the global optimal solution,an effective local search can also be performed to ensure the reliability of the inversion results.This paper uses the ABC algorithm to perform Rayleigh wave dispersion inversion on the actual seismic data to obtain a clear undergrounding of shear wave velocity profile and accurately identify the location of the high-velocity interlayer.It is verified that the ABC algorithm used in the inversion of the Rayleigh wave dispersion curve is stable and converges quickly.
文摘The patterns of ultrasonic backscattered echoes represent valuable information pertaining to the geometric shape, size, and orientation of the reflectors as well as the microstructure of the propagation path. Accurate estimation of the ultrasonic echo pattern is essential in determining the object or propagation path properties. This paper proposes a parameter estimation method for ultrasonic echoes based on Artificial Bee Colony (ABC) algorithm which is one of the most recent swarm intelligence based algorithms. A modified ABC (MABC) algorithm is given by adding an adjusting factor to the neighborhood search formula of traditional ABC algorithm in order to enhance its performance. The algorithm could overcome the impact of different search range on estimation accuracy to solve the multi-dimensional parameter optimization problems. The performance of the MABC algorithm is demonstrated by numerical simulation and ultrasonic detection experiments. Results show that MABC not only can accurately estimate various parameters of the ultrasonic echoes, but also can achieve the optimal solution in the global scope. The proposed algorithm also has the advantages of fast convergence speed, short running time and real-time parameters esti- mation.
文摘针对滤波器组多载波-偏移正交幅度调制技术(Filter Bank Multicarrier-Offset Quadrature Amplitude Modulation,FBMC-OQAM)存在峰均功率比(Peak-to-Average Power Ratio,PAPR)过高的问题,以及传统部分传输序列(Partial Transfer Sequence,PTS)算法对PAPR抑制效果不明显,提出了一种新的基于自适应搜索策略的人工蜂群部分传输序列算法(Adaptive Search Strategy Based Artifical Bee Colony PTS,ASSABC-PTS)。首先,根据FBMC-OQAM系统特性,利用传统PTS算法对系统进行初步优化,以降低FBMC-OQAM系统的PAPR;然后,针对PTS算法中存在的计算复杂度问题,引入人工蜂群(Artificial Bee Colony,ABC)算法进行优化;最后,在ABC算法中引入自适应搜索策略提升算法的局部寻优能力,加快ABC算法的收敛速度和搜索精度。仿真实验表明,ASSABC-PTS在有效降低系统算法复杂度的同时,也极大降低了FBMC-OQAM系统的峰均功率比。