检索结果-内蒙古大学图书馆

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

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

来源：评论

学校读者我要写书评

暂无评论

Identification of structural models using a modified artificial bee colony algorithm

引用

COMPUTERS & STRUCTURES 2013年第Jan.期116卷 59-74页

作者： Sun, Hao Lus, Hilmi Betti, Raimondo Columbia Univ Dept Civil Engn & Engn Mech New York NY 10027 USA Bogazici Univ Dept Civil Engn TR-34342 Istanbul Turkey

A modified version of the artificial bee colony (ABC) algorithm is presented to identify structural systems. ABC is a heuristic algorithm with simple structure, ease of implementation and robustness. A nonlinear factor for convergence control is introduced in the algorithm to enhance the balance of global and local searches. To investigate the applicability of this proposed technique to system identification, three examples are studied under different conditions regarding data availability, noise pollution level, priori knowledge of parameters, etc. Simulation results show the proposed technique produces excellent parameter estimation, even with few measurements and high noise corruptions. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Parameter estimation Structural system identification artificial bee colony algorithm Optimization

来源：评论

学校读者我要写书评

暂无评论

Image contrast enhancement using an artificial bee colony algorithm

引用

SWARM AND EVOLUTIONARY COMPUTATION 2018年 38卷 287-294页

作者： Chen, Jia Yu, Weiyu Tian, Jing Chen, Li Zhou, Zhili South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430081 Hubei Peoples R China Nanjing Univ Informat Sci & Technol Nanjing 210044 Jiangsu Peoples R China

The objective of image contrast enhancement is to improve the contrast level of images, which are degraded during image acquisition. Image contrast enhancement is considered as an optimization problem in this paper and the artificial bee colony (ABC) algorithm is utilized to find the optimal solution for this optimization problem. The contribution of the proposed approach is two-fold. First, in view of that the fitness function is indispensable to evaluate the quality of the enhanced image, a new objective fitness function is proposed in this paper. Second, the image transformation function is critical to generate new pixel intensities for the enhanced image from the original input image;more importantly, it guides the searching movements of the artificial bees. For that, a parametric image transformation function is utilized in this paper so that only the optimal parameters used in the transformation function need to be searched by the ABC algorithm. This is in contrast to that the whole space of image intensity levels is used in the conventional ABC-based image enhancement approaches. Extensive experiments are conducted to demonstrate that the proposed approach outperforms conventional image contrast enhancement approaches to achieve both better visual image quality and higher objective performance measures.

关键词： Image contrast enhancement artificial bee colony algorithm

来源：评论

学校读者我要写书评

暂无评论

An artificial bee colony algorithm for multi-objective optimisation

引用

APPLIED SOFT COMPUTING 2017年 50卷 235-251页

作者： Luo, Jianping Liu, Qiqi Yang, Yun Li, Xia Chen, Min-rong Cao, Wenming Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China

In addition to dominance-based and decomposition-based algorithms, performance indicator-based algorithms have been widely used and investigated in the field of evolutionary multi-objective optimisation. This study proposes a multi-objective artificial bee colony optimisation method called epsilon-MOABC?-MOABC based on performance indicators to solve multi-objective and many-objective problems. The proposed algorithm develops an external archive on the basis of both Pareto dominance and preference indicators to save the non-dominated solutions produced in each generation. The population of the presented algorithm includes employed bees, onlooker bees, and scout bees. Employed bees adjust their trajectories according to the information provided by other employed bees. Motivated by employed bees, onlooker bees select food sources to update their positions according to a power law probability, with which the food sources with high quality have a high probability to be selected for exploration. The quality of food sources is calculated on the basis of the quality indicator I epsilon+. Scout bees dispose of food sources with poor quality. The proposed algorithm proves to be competitive in dealing with multi-objective and many-objective optimisation problems in comparison with other state-of-the-art algorithms for CEC09, LZ09, and DTLZ test instances. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Evolutionary computing Intelligent computing Swarm intelligence Multi-objective optimisation Diversity artificial bee colony algorithm

来源：评论

学校读者我要写书评

暂无评论

A hybrid multi-objective artificial bee colony algorithm for flexible task scheduling problems in cloud computing system

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2020年第4期23卷 2483-2499页

作者： Li, Jun-qing Han, Yun-qi Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China

In this study, the flexible task scheduling problem in a cloud computing system is studied and solved by a hybrid discrete artificial bee colony (ABC) algorithm, where the considered problem is firstly modeled as a hybrid flowshop scheduling (HFS) problem. Both a single objective and multiple objectives are considered. In multiple objective HFS problems, three objectives, i.e., minimization of the maximum completion time, maximum device workload, and total workloads of all devices, are considered simultaneously. Two different kinds of HFS are considered, i.e., HFS with identical parallel machines and HFS with unrelated machines. In the proposed algorithm, three types of artificial bees are included as in the classical ABC algorithm, i.e., the employed bee, the onlooker bee, and the scout bee. Each solution is represented as an integer string. To consider the problem features, several different types of perturbation structures are investigated to enhance the searching abilities. An improved version of the adaptive perturbation structure is embedded in the proposed algorithm to balance the exploitation and exploration ability. A simple but efficient selection and updated approach are applied to enhance the exploitation process. To further improve the exploitation abilities, a deep-exploitation operator is designed. An improved scout bee employed with different local search methods for the best food source or the abandoned solution is designed and can increase the convergence ability of the proposed algorithm. The proposed algorithm is tested on sets of the well-known benchmark instances, and the performance of the proposed algorithm is verified.

关键词： Hybrid flowshop scheduling problem artificial bee colony algorithm Cloud system Flexible task scheduling

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion

引用

APPLIED MATHEMATICAL MODELLING 2013年第1-11期37卷 6758-6779页

作者： Tasgetiren, M. Fatih Pan, Quan-Ke Suganthan, P. N. Oner, Adalet Yasar Univ Dept Ind Engn Izmir Turkey Liaocheng Univ Coll Comp Sci Liaocheng Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this paper, we present a discrete artificial bee colony algorithm to solve the no-idle permutation flowshop scheduling problem with the total tardiness criterion. The no-idle permutation flowshop problem is a variant of the well-known permutation flowshop scheduling problem where idle time is not allowed on machines. In other words, the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions: First of all, a discrete artificial bee colony algorithm is presented to solve the problem on hand first time in the literature. Secondly, some novel methods of calculating the total tardiness from make-span are introduced for the no-idle permutation flowshop scheduling problem. Finally, the main contribution of the paper is due to the fact that a novel speed-up method for the insertion neighborhood is developed for the total tardiness criterion. The performance of the discrete artificial bee colony algorithm is evaluated against a traditional genetic algorithm. The computational results show its highly competitive performance when compared to the genetic algorithm. Ultimately, we provide the best known solutions for the total tardiness criterion with different due date tightness levels for the first time in the literature for the Taillard's benchmark suit. (C) 2013 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm No-idle permutation flowshop scheduling problem Metaheuristics Evolutionary algorithms Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved artificial bee colony algorithm based on two-dimensional queue structure for complex optimization problems

引用

ALEXANDRIA ENGINEERING JOURNAL 2024年 86卷 669-679页

作者： Pan, Xiuqin Wang, Yun Lu, Yong Sun, Na Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

In the face of increasingly complex optimization problems, the artificial bee colony algorithm still encounters some unresolved issues. For instance, the randomness involved in generating the initial population provides diversity to optimization solutions but also leads to convergence among individuals due to the current pseudo-randomness in this process. To address this challenge, this study introduces an improved ABC algorithm based on a two-dimensional queue structure, referred to as KLABC. The KLABC algorithm introduces several key advantages over traditional ABC algorithms. By employing the K-Means algorithm, it enhances the assessment of initial population individuals, ensuring a higher-quality population that aligns with specified requirements. The integration of a two-dimensional queue-based search strategy, along with the application of the first-in, first-out (FIFO) mechanism, not only preserves population diversity but also minimizes the risk of discarding potential optimal solutions during iterative searches. Augmenting this framework, the concepts of the global best solution (GBest) and queue best solution (QBest) are incorporated to fortify the exploration formula, expediting the algorithm's convergence rate. Experimental results on clustering optimization problems across seven UCI datasets validate the effectiveness and robustness of the proposed algorithm. Experimental results indicate that the KLABC algorithm achieves faster convergence compared to the traditional ABC algorithm, with an average performance improvement of 55.63%. Additionally, when comparing the runtime of different algorithms in handling large-scale data, the KLABC algorithm effectively maintains its performance even in scenarios with larger data sizes.

关键词： artificial bee colony algorithm Initial Population Optimization K-Means Global Best Solution Two-dimensional Queue Clustering Optimization UCI Datasets

来源：评论

学校读者我要写书评

暂无评论

A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization

引用

JOURNAL OF SUPERCOMPUTING 2020年第12期76卷 9330-9354页

作者： Jarrah, Muath Ibrahim Jaya, A. S. M. Alqattan, Zakaria N. Azam, Mohd Asyadi Abdullah, Rosni Jarrah, Hazim Abu-Khadrah, Ahmed Ismail Univ Tekn Malaysia Melaka Fac Informat & Commun Technol Durian Tunggal 76100 Melaka Malaysia Univ Sains Malaysia Sch Comp Sci Natl Adv IPv6 Ctr Gelugor 11800 Penang Malaysia Univ Tekn Malaysia Melaka Fac Mfg Engn Durian Tunggal 76100 Melaka Malaysia Auckland Univ Technol Dept Elect & Elect Engn Auckland New Zealand Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia

Over the past few decades, there has been a surge of interest of using swarm intelligence (SI) in computer-aided optimization. SI algorithms have demonstrated their efficacy in solving various types of real-world optimization problems. However, it is impossible to find an optimization algorithm that can obtain the global optimum for every optimization problem. Therefore, researchers extensively try to improve methods of solving complex optimization problems. Many SI search algorithms are widely applied to solve such problems. ABC is one of the most popular algorithms in solving different kinds of optimization problems. However, it has a weak local search performance where the equation of solution search in ABC performs good exploration, but poor exploitation. Besides, it has a fast convergence and can therefore be trapped in the local optima for some complex multimodal problems. In order to address such issues, this paper proposes a novel hybrid ABC with outstanding local search algorithm called beta-hill climbing (beta HC) and denoted by ABC-beta HC. The aim is to improve the exploitation mechanism of the standard ABC. The proposed algorithm was experimentally tested with parameters tuning process and validated using selected benchmark functions with different characteristics, and it was also evaluated and compared with well-known state-of-the-art algorithms. The evaluation process was investigated using different common measurement metrics. The result showed that the proposed ABC-beta HC had faster convergence in most benchmark functions and outperformed eight algorithms including the original ABC in terms of all the selected measurement metrics. For more validation, Wilcoxon's rank sum statistical test was conducted, and the p values were found to be mostly less than 0.05, which demonstrates that the superiority of the proposed ABC-beta HC is statistically significant.

关键词： Metaheuristic optimization algorithms Swarm intelligence algorithms artificial bee colony algorithm beta-Hill climbing Numerical benchmark function optimization

来源：评论

学校读者我要写书评

暂无评论

Improved artificial bee colony algorithm and Its Application to Fundus Retinal Blood Vessel Image Binarization

引用

IEEE ACCESS 2020年 8卷 123726-123734页

作者： Pan, Xiuqin Zhang, Qinrui Pan, Haichuan Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

The content of this work is based on the characteristics of standard artificial bee colony(ABC) algorithm with weak local search ability and slow convergence speed. Then, an improved algorithm named KD-ABC is proposed. For improving the diversity and quality of the solution, it changes the generation method of honey source. In the initialization phase, it uses the cluster center generated by the K-MEANS method as the initial honey source instead of the initialization in the standard method. For improving the local optimization ability and the convergence speed without reducing the global search, we proposed a dynamic neighborhood search mechanism based on the number of iterations in terms of ABC search strategy and neighborhood selection stage. In order to find a suitable threshold to divide the grayscale image into blood vessels and background parts, we applied the characteristics of the KD-ABC algorithm to the binary processing stage of the fundus retinal blood vessel image, which lays the foundation for future image recognition.

关键词： artificial bee colony algorithm Clustering algorithms Particle swarm optimization Blood vessels Biomedical imaging Heuristic algorithms Retina Initialization clustering algorithm dynamic neighborhood search binarization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：