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A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization

APPLIED SOFT COMPUTING 2015年 35卷 766-785页

作者： Xiang, Yi Zhou, Yuren Sun Yat Sen Univ Sch Data Sci & Comp Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Collaborat Innovat Ctr High Performance Comp Guangzhou 510275 Guangdong Peoples R China

This paper suggests a dynamic multi-colony multi-objective artificial bee colony algorithm (DMCMOABC) by using the multi-deme model and a dynamic information exchange strategy. In the proposed algorithm, K colonies search independently most of the time and share information occasionally. In each colony, there are S bees containing equal number of employed bees and onlooker bees. For each food source, the employed or onlooker bee will explore a temporary position generated by using neighboring information, and the better one determined by a greedy selection strategy is kept for the next iterations. The external archive is employed to store non-dominated solutions found during the search process, and the diversity over the archived individuals is maintained by using crowding-distance strategy. If a randomly generated number is smaller than the migration rate R, then an elite, defined as the intermediate individual with the maximum crowding-distance value, is identified and used to replace the worst food source in a randomly selected colony. The proposed DMCMOABC is evaluated on a set of unconstrained/constrained test functions taken from the CEC2009 special session and competition in terms of four commonly used metrics EPSILON, HV, IGD and SPREAD, and it is compared with other state-of-the-art algorithms by applying Friedman test on the mean of IGD. The test results show that DMCMOABC is significantly better than or at least comparable to its competitors for both unconstrained and constrained problems. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi objective optimization Multi colony model artificial bee colony algorithm Migration strategy Friedman test

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An artificial bee colony algorithm with guide of global & local optima and asynchronous scaling factors for numerical optimization

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APPLIED SOFT COMPUTING 2015年 37卷 608-618页

作者： Liu, Jianjun Zhu, Hongqiu Ma, Qiang Zhang, Lanlan Xu, Honglei China Univ Petr Coll Sci Beijing 102249 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Curtin Univ Dept Math & Stat Perth WA 6845 Australia

artificial bee colony (ABC) algorithm is a wildly used optimization algorithm. However, ABC is excellent in exploration but poor in exploitation. To improve the convergence performance of ABC and establish a better searching mechanism for the global optimum, an improved ABC algorithm is proposed in this paper. Firstly, the proposed algorithm integrates the information of previous best solution into the search equation for employed bees and global best solution into the update equation for onlooker bees to improve the exploitation. Secondly, for a better balance between the exploration and exploitation of search, an S-type adaptive scaling factors are introduced in employed bees' search equation. Furthermore, the searching policy of scout bees is modified. The scout bees need update food source in each cycle in order to increase diversity and stochasticity of the bees and mitigate stagnation problem. Finally, the improved algorithms is compared with other two improved ABCs and three recent algorithms on a set of classical benchmark functions. The experimental results show that the our proposed algorithm is effective and robust and outperform than other algorithms. (C) 2015 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Population based optimization Scaling factors Global optimization

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Optimal placement of elastic steel diagonal braces using artificial bee colony algorithm

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STEEL AND COMPOSITE STRUCTURES 2015年第2期19卷 349-368页

作者： Aydin, E. Sonmez, M. Karabork, T. Nigde Univ Dept Civil Engn TR-51240 Nigde Turkey Aksaray Univ Dept Civil Engn TR-68100 Aksaray Turkey

This paper presents a new algorithm to find the optimal distribution of steel diagonal braces (SDB) using artificial bee colony optimization technique. The four different objective functions are employed based on the transfer function amplitude of;the top displacement, the top absolute acceleration, the base shear and the base moment. The stiffness parameter of SDB at each floor level is taken into account as design variables and the sum of the stiffness parameter of the SDB is accepted as an active constraint. An optimization algorithm based on the artificial bee colony (ABC) algorithm is proposed to minimize the objective functions. The proposed ABC algorithm is applied to determine the optimal SDB distribution for planar buildings in order to rehabilitate existing planar steel buildings or to design new steel buildings. Three planar building models are chosen as numerical examples to demonstrate the validity of the proposed method. The optimal SDB designs are compared with a uniform SDB design that uniformly distributes the total stiffness across the structure. The results of the analysis clearly show that each optimal SDB placement, which is determined based on different performance objectives, performs well for its own design aim.

关键词： steel diagonal brace artificial bee colony algorithm structural optimization transfer functions

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Discrete gbest-guided artificial bee colony algorithm for cloud service composition

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APPLIED INTELLIGENCE 2015年第4期42卷 661-678页

作者： Huo, Ying Zhuang, Yi Gu, Jingjing Ni, Siru Xue, Yu Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China

The widespread application of cloud computing creates massive application services on the Internet, which is a new challenge for the models and algorithms of cloud service composition. This paper proposes a new method for cloud service composition. Time attenuation function is added into the service composition model, and service composition is formalized as a nonlinear integer programming problem. Moreover, the Discrete Gbest-guided artificial bee colony (DGABC) algorithm is proposed, which simulates the search for the optimal service composition solution through the exploration of bees for food. Experiments show that the service composition model with the time attenuation function can make the quality of service more consistent with the current characteristics of services. Compared with other algorithms, the DGABC algorithm has advantages in terms of the quality of solution and efficiency, especially for the large-scale data, and it can obtain a near-optimal solution within a short period of time.

关键词： Quality of service Reputation Service composition artificial bee colony algorithm

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Kurtosis forecasting of bearing vibration signal based on the hybrid model of empirical mode decomposition and RVM with artificial bee colony algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第11期42卷 5011-5018页

作者： Fei, Sheng-wei Donghua Univ Sch Mech Engn Shanghai 201620 Peoples R China

Accurate prediction for kurtosis of bearing vibration signal is helpful to find out the fault of bearing as soon as possible. As it is difficult to obtain an appropriate embedding dimension in creating directly the prediction model of kurtosis of bearing vibration signal by relevance vector machine (RVM), the hybrid model of empirical mode decomposition and RVM with artificial bee colony algorithm (EMD-ABCRVM) is proposed for kurtosis forecasting of bearing vibration signal. The seven decomposed signals with different frequency range can be obtained by empirical mode decomposition for kurtosis of bearing vibration signal. The prediction models of the seven decomposed signals can be established by RVNI with their each appropriate embedding dimension, and artificial bee colony algorithm (ABC) is used to select the appropriate kernel parameters of their RVM models. Thus, each RVM model of the seven decomposed signals has appropriate embedding dimension and kernel parameter. In order to show the superiority of the proposed EMD-ABCRVM method, the RVM models with several different embedding dimensions and Gaussian RBF kernel parameters are used to compare with the proposed EMD-ABCRVM method. The experimental results show that it is feasible for the proposed combination scheme to improve the prediction accuracy of RVM for kurtosis of bearing vibration signal. (C) 2015 Published by Elsevier Ltd.

关键词： Kurtosis prediction of bearing vibration signal Empirical mode decomposition Embedding dimension artificial bee colony algorithm

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Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm

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MECHANISM AND MACHINE THEORY 2015年 90卷 219-229页

作者： Yang Dalian Liu Yilun Li Songbai Li Xuejun Ma Liyong Cent S Univ Coll Mech & Elect Engn Changsha 410083 Hunan Peoples R China Cent S Univ Light Alloy Res Inst Changsha 410083 Hunan Peoples R China Hunan Univ Sci & Technol Hunan Prov Key Lab Hlth Maintenance Mech Equipmen Xiangtan 411201 Peoples R China Hebei Univ Architecture Sch Mech Engn Zhangjiakou 075051 Peoples R China

Gearbox is an important transmission component in mechanical equipment, which is prone to failure. When diagnosing the faults of gearbox, the recognition ability of support vector machine (SVM) is greatly influenced by the kernel function and its parameters. However, the optimal parameters are difficult to find. To solve this problem, the parameter optimization method of support vector machine based on the artificial colony bee algorithm was presented. The vibration acceleration signals of normal gear, chipped tooth gear, and missing tooth gear were collected and the fault features were extracted based on the EEMD and the kernel function. In the experiments, the optimization of Gauss RBF kernel support vector machine parameters was taken as the object, and the optimize performance of the genetic algorithm, the particle swarm optimization algorithm and the artificial bee colony algorithm were compared and analyzed. The results show that the artificial bee colony takes the least time, while the genetic algorithm takes the longest time, the accuracy of the artificial bee colony algorithm is better than the others. The recognition rates of gearbox faults were improved by using the SVM classification model which was optimized by the bee colony algorithm. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Support vector machine Parameter optimization Fault diagnosis

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An Improved Possibilistic Fuzzy Entropy Clustering Based on artificial bee colony algorithm

An Improved Possibilistic Fuzzy Entropy Clustering Based on ...

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2016 2nd International Conference on artificial Intelligence and Industrial Engineering (AIIE2016)

作者： Baofeng Guo Mingyan Jiang School of Information Science and Engineering Shandong university

ISBN: (纸本)9781510835368

In this paper,a possibilistic fuzzy entropy clustering algorithm(PFECM) based on unsupervised possibilistic clustering(UPC) algorithm and partition entropy(PE) has been ***,an efficient global optimization method—artificial bee colony(ABC) algorithm is introduced to optimize the proposed ***-PFECM has two significant advantages compared with other ***,it inherited the merits of PFCM including strong robust to noise and exclusion of the consistent ***,ABC-PFECM could eliminate the defects that PFCM is sensitive to the initial value and easily fall into the local optimal *** results show splendid performance of our algorithm in decreasing computational complexity,improving clustering accuracy and enhancing global optimization capabilities.

关键词： possibilistic fuzzy clustering partition entropy unsupervised possibilistic clustering artificial bee colony algorithm

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Hyperspectral image clustering method based on artificial bee colony algorithm and Markov random fields

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JOURNAL OF APPLIED REMOTE SENSING 2015年第1期9卷

作者： Sun, Xu Yang, Lina Gao, Lianru Zhang, Bing Li, Shanshan Li, Jun Chinese Acad Sci Inst Remote Sensing & Digital Earth Key Lab Digital Earth Sci Beijing 100094 Peoples R China Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Guangdong Peoples R China

Center-oriented hyperspectral image clustering methods have been widely applied to hyperspectral remote sensing image processing;however, the drawbacks are obvious, including the over-simplicity of computing models and underutilized spatial information. In recent years, some studies have been conducted trying to improve this situation. We introduce the artificial bee colony (ABC) and Markov random field (MRF) algorithms to propose an ABC MRF-cluster model to solve the problems mentioned above. In this model, a typical ABC algorithm framework is adopted in which cluster centers and iteration conditional model algorithm's results are considered as feasible solutions and objective functions separately, and MRF is modified to be capable of dealing with the clustering problem. Finally, four datasets and two indices are used to show that the application of ABC-cluster and ABC MRF-cluster methods could help to obtain better image accuracy than conventional methods. Specifically, the ABC-cluster method is superior when used for a higher power of spectral discrimination, whereas the ABC MRF-cluster method can provide better results when used for an adjusted random index. In experiments on simulated images with different signal-to-noise ratios, ABC-cluster and ABC MRF-cluster showed good stability. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： hyperspectral remote sensing cluster artificial bee colony algorithm

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Bare bones artificial bee colony algorithm with parameter adaptation and fitness-based neighborhood

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INFORMATION SCIENCES 2015年 316卷 180-200页

作者： Gao, Weifeng Chan, Felix T. S. Huang, Lingling Liu, Sanyang China Univ Petr Qingdao 266580 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China Xidian Univ Sch Sci Xian 710071 Peoples R China

Concerning this issue that the solution search equation of artificial be colony algorithm (ABC) does well in exploration but badly in exploitation, a bare bones ABC with parameter adaptation and fitness-based neighborhood is proposed, BABC for short reference. The proposed method employs a Gaussian search equation to produce a new candidate individual at the onlooker phase which exploits the valuable information hidden in the best individual to improve the exploitation, while, at the employed bee phase, a parameter adaptation strategy and a fitness-based neighborhood mechanism are integrated into the search equation which can take advantage of the information from the previous search and better individuals to enhance the search ability. The proposed framework can be applied to any ABC with minimal changes. Furthermore, the proposed framework is applied to the original ABC and several highly regarded ABC variants. The comparison results demonstrate that the proposed framework is able to significantly improve the performance of the original ABC and the modified ABC variants. (C) 2015 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Gaussian search equation Parameter adaptation strategy Fitness-based neighborhood mechanism

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Coupling Optimization Design of Aspirated Compressor Airfoil and Aspirated Scheme Based on artificial bee colony algorithm and CST Method

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INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES 2015年第1期32卷 25-39页

作者： Li, Jun Liu, Bo Zhao, Yan Yang, Xiaodong Lu, Xiaofeng Wang, Lei Northwestern Polytech Univ Sch Power & Energy Xian 710072 Shaanxi Peoples R China AVIC Shenyang Engine Design & Res Inst Shenyang Liaoning Peoples R China AVIC Aerodynam Res Inst Shenyang Liaoning Peoples R China

This paper focuses on creating a new design method optimizing both aspirated compressor airfoil and the aspiration scheme simultaneously. The optimization design method is based on the artificial bee colony algorithm and the CST method, while the flow field is computed by one 2D computational program. The optimization process of the rotor tip and stator tip airfoil from an aspirated fan stage is demonstrated to verify the effectiveness of the new coupling method. The results show that the total pressure losses of the optimized stator tip and rotor tip airfoil are reduced relatively by 54% and 20%, respectively. artificial bee colony algorithm and CST method indicate a satisfying applicability in aspirated airfoil optimization design. Finally, the features of aspirated airfoil designing process are concluded.

关键词： aspirated compressor airfoil aspirated scheme coupling optimization design artificial bee colony algorithm CST method

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