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Differential artificial bee colony algorithm for Global Numerical Optimization

JOURNAL OF COMPUTERS 2011年第5期6卷 841-848页

作者： Wu, Bin Qian, Cun Hua Nanjing Univ Technol Coll Management Sci & Engn Nanjing Peoples R China

artificial bee colony (ABC) is the one of the newest nature inspired heuristics for optimization problem. In order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions, a differential ABC (DABC) is proposed. The differential operator obeys uniform distribution and creates candidate food position that can fully represent the solution space. So the diversity of populations and capability of global search will be enhanced. To show the performance of our proposed DABC, a number of experiments are carried out on a set of well-known benchmark continuous optimization problems. Simulation results and comparisons with the standard ABC and several meta-heuristics show that the DABC can effectively enhance the searching efficiency and greatly improve the searching quality.

关键词： artificial bee colony algorithm Global Numerical Optimization Differential Evolution

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Dynamic economic dispatch using artificial bee colony algorithm for units with valve-point effect

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EUROPEAN TRANSACTIONS ON ELECTRICAL POWER 2011年第1期21卷 70-81页

作者： Hemamalini, S. Simon, Sishaj P. Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli 620015 Tamil Nadu India

Dynamic economic dispatch (DED) is an important dynamic problem in power system operation and control. The objective of the problem is to schedule power generation for the online units over a time horizon, satisfying the unit and ramp rate constraints. In this paper, artificial bee colony (ABC) algorithm is used to solve the dynamic economic dispatch problem for generating units with valve-point effect. The feasibility of the proposed method is validated with ten- and five-unit-test systems for a period of 24 hours. In addition, the effects of control parameters on the performance of ABC algorithm for DED problem are studied. Results obtained with the proposed approach are compared with other techniques in the literature. The results obtained substantiate the applicability of the proposed method for solving DED problems with non-smooth cost functions in terms of solution quality and computation efficiency. Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： dynamic economic dispatch artificial bee colony algorithm ramp rate limits valve-point loading

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An artificial bee colony algorithm Based on Problem Data Properties for Scheduling Job Shops

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Procedia Engineering 2011年 23卷 131-136页

作者： Rui Zhang School of Economics and Management Nanchang University Nanchang 330031 PR China

To solve the job shop scheduling problem with the objective of minimizing total weighted tardiness, an artificial bee colony algorithm based on problem data analysis is proposed. First, characteristic values are defined to describe the criticality of each job in the process of scheduling and optimization. Then, a fuzzy inference system is employed to evaluate the characteristic values according to practical scheduling knowledge. Finally, a local search mechanism is designed based on the idea that critical jobs should be processed with higher priority. Numerical computations are conducted with an artificial bee colony algorithm which integrates the local search module. The computational results for problems of different sizes show that the proposed algorithm is both effective and efficient.

关键词： artificial bee colony algorithm Job shop scheduling problem Local search Problem data

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artificial bee colony algorithm for small signal model parameter extraction of MESFET

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ENGINEERING APPLICATIONS OF artificial INTELLIGENCE 2010年第5期23卷 689-694页

作者： Sabat, Samrat L. Udgata, Siba K. Abraham, Ajith Univ Hyderabad Sch Phys Hyderabad 500046 Andhra Pradesh India Univ Hyderabad Dept Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India Sci Network Innovat & Res Excellence Machine Intelligence Res Labs Auburn WA 98071 USA

This paper presents an application of swarm intelligence technique namely artificial bee colony (ABC) to extract the small signal equivalent circuit model parameters of GaAs metal extended semiconductor field effect transistor (MESFET) device and compares its performance with particle swarm optimization (PSO) algorithm. Parameter extraction in MESFET process involves minimizing the error, which is measured as the difference between modeled and measured S parameter over a broad frequency range. This error surface is viewed as a multi-modal error surface and robust optimization algorithms are required to solve this kind of problem. This paper proposes an ABC algorithm that simulates the foraging behavior of honey bee swarm for model parameter extraction. The performance comparison of both the algorithms (ABC and PSO) are compared with respect to computational time and the quality of solutions (QoS). The simulation results illustrate that these techniques extract accurately the 16-element small signal model parameters of MESFET. The efficiency of this approach is demonstrated by a good fit between the measured and modeled S-parameter data over a frequency range of 0.5-25 GHz. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Particle swarm optimization Parameter extraction MESFET small signal model Swarm intelligence

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Design and analysis of the artificial bee colony algorithms for domain-oriented QoS-aware service composition

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INTERNATIONAL JOURNAL OF SERVICES TECHNOLOGY AND MANAGEMENT 2016年第6期22卷 378-405页

作者： Wang, Haifang Xu, Xiaofei Wang, Zhongjie Liu, Zhizhong Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

Recently, various evolutionary algorithms have been successfully applied to acquire approximately optimal solutions for QoS-aware service composition problems. Especially, artificial bee colony algorithm (ABC) stands out due to its advantages of few parameters, strong robustness and search capability. In addition, for these algorithms, domain features are widely utilised as heuristic. However, how to combine these two points to achieve more optimal solutions is becoming a challenge. To address this critical challenge, this paper summarises three domain features (priori, similarity and correlation) and proposes the artificial bee colony algorithms for domain-oriented service composition named S-ABCsc paradigm. Besides, the framework of the paradigm is presented, and its seven configurable points are identified. Moreover, two types of algorithms are derived from the paradigm. To apply the paradigm, a support tool is realised, which helps users obtain a concrete algorithm. Furthermore, several comparison experiments are conducted, which have proved the effectiveness of the paradigm.

关键词： service composition service-oriented ABC paradigm artificial bee colony algorithm domain service QoS support tool algorithm generation domain features evolutionary algorithms parameter setting

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A novel clustering approach: artificial bee colony (ABC) algorithm

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APPLIED SOFT COMPUTING 2011年第1期11卷 652-657页

作者： Karaboga, Dervis Ozturk, Celal Erciyes Univ Intelligent Syst Res Grp Dept Comp Engn Kayseri Turkey

artificial bee colony (ABC) algorithm which is one of the most recently introduced optimization algorithms, simulates the intelligent foraging behavior of a honey bee swarm. Clustering analysis, used in many disciplines and applications, is an important tool and a descriptive task seeking to identify homogeneous groups of objects based on the values of their attributes. In this work, ABC is used for data clustering on benchmark problems and the performance of ABC algorithm is compared with Particle Swarm Optimization (PSO) algorithm and other nine classification techniques from the literature. Thirteen of typical test data sets from the UCI Machine Learning Repository are used to demonstrate the results of the techniques. The simulation results indicate that ABC algorithm can efficiently be used for multivariate data clustering. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Classification Clustering analysis artificial bee colony algorithm Particle Swarm Optimization

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A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2017年第16期55卷 4765-4784页

作者： Zhou, Jiajun Yao, Xifan South China Univ Technol Sch Mech & Automot Engn Guangzhou Guangdong Peoples R China

This paper proposes a multi-objective hybrid artificial bee colony (MOHABC) algorithm for service composition and optimal selection (SCOS) in cloud manufacturing, in which both the quality of service and the energy consumption are considered from the perspectives of economy and environment that are two pillars of sustainable manufacturing. The MOHABC uses the concept of Pareto dominance to direct the searching of a bee swarm, and maintains non-dominated solution found in an external archive. In order to achieve good distribution of solutions along the Pareto front, cuckoo search with Levy flight is introduced in the employed bee search to maintain diversity of population. Furthermore, to ensure the balance of exploitation and exploration capabilities for MOHABC, the comprehensive learning strategy is designed in the onlooker search so that every bee learns from the external archive elite, itself and other onlookers. Experiments are carried out to verify the effect of the improvement strategies and parameters' impacts on the proposed algorithm and comparative study of the MOHABC with typical multi-objective algorithms for SCOS problems are addressed. The results show that the proposed approach obtains very promising solutions that significantly surpass the other considered algorithms.

关键词： cloud manufacturing combinatorial optimisation Pareto optimisation manufacturing service composition artificial bee colony algorithm quality of service

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Identification of Parameters in Kinetic Models Using artificial bee colony algorithm

Identification of Parameters in Kinetic Models Using Artific...

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The second International Conference of Electrical and Electronics Engineering(ICEEE 2011)

作者： Guangzhou Chen Jiaquan Wang Chuanjun Li Department of Environmental Engineering Anhui University of Architecture School of Resources and Environmental Engineering Hefei University of Technology School of Management Anhui University of Architecture

Parameter identification is a key step in establishing kinetic modelsAimed at the above problem, it can be transformed into an optimization problem by constructing objective function that minimizes simulation errorsIn this study, a novel swarm intelligence optimization algorithm-artificial bee colony algorithm is usedIn the experiments, each variable is optimized according to its own reasonable scopeThen, two examples of kinetic models are analyzed and their computation results are compared with that of modified genetic algorithm, standard particle swarm optimization and its modified algorithmsThe results show that artificial bee colony algorithm has good adaptability to various problems and better optimization precisionMoreover, it needs few control parameters of algorithmSo it is an effective optimization method.

关键词： artificial bee colony algorithm parameter identification kinetic models global optimization

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Genetic and artificial bee colony algorithms for scheduling of multi-skilled manpower in combined manpower-vehicle routing problem

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PRODUCTION AND MANUFACTURING RESEARCH-AN OPEN ACCESS JOURNAL 2016年第1期4卷 133-151页

作者： Seidgar, Hany Kiani, Morteza Fazlollahtabar, Hamed Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Iran Univ Sci & Technol Fac Ind Engn Tehran Iran

This paper investigates a scheduling combined manpower-vehicle routing problem with a central depot in and a set of multi-skilled manpower for serving to customers. Teams are in different range of competencies that it will affect the service time duration. Vehicles are in different moving speeds and costs and not all the vehicles are capable to move toward all the customers' sites. The objective is to minimize the total cost of servicing, routing, and lateness penalties. This paper presents a mixed integer programming model and two meta-heuristic approaches of genetic algorithm (GA) and artificial bee colony algorithm (ABC) are developed to solve the generated problems. Furthermore, Taguchi experimental design method is applied to set the proper values of parameters. The available results show the higher performance of proposed GA compared with ABC, in quality of solutions.

关键词： Vehicle routing problem manpower competency of working team genetic algorithm artificial bee colony algorithm

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THE artificial bee colony algorithm FOR VECTOR QUANTIZATION IN IMAGE COMPRESSION

THE ARTIFICIAL BEE COLONY ALGORITHM FOR VECTOR QUANTIZATION ...

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2011 4th IEEE International Conference on Broadband Network and Multimedia Technology

作者： Ming-Huwi Horng Department of Computer Science and Information Engineering National PingTung Institute of CommercePingtung

The vector quantization was a powerful technique in image *** widely used method such as the Linde-Buzo-Gray(LBG)algorithm always generated local optimal codebook. Recently,particle swarm optimization was adapted to obtain the near-global optimal codebook of vector *** alterative method called the quantum particle swarm optimization was developed to improve the results of original PSO *** honey bee mating optimization was used to develop the algorithm for vector *** this paper,we proposed a new method based on the artificial bee colony(ABC) algorithm to construct the codebook of vector *** proposed method uses LBG method as the initial of ABC algorithm to develop the VQ *** method is called ABC-LBG *** ABC-LBG algorithm is compared with four algorithms described above. Experimental results showed that the ABC-LBG algorithm is more reliable and the reconstructed images get higher quality compared to other methods.

关键词： Vector quantization LBG algorithm Particle swarm optimization Quantum particle swarm optimization Honey bee mating optimization artificial bee colony algorithm

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