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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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Two modified artificial bee colony algorithms inspired by Grenade Explosion Method

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NEUROCOMPUTING 2015年第P3期151卷 1198-1207页

作者： Zhang, Chaoqun Zheng, Jianguo Zhou, Yongquan Donghua Univ Glorious Sun Sch Business & Management Shanghai 200051 Peoples R China Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China

artificial bee colony (ABC) algorithm, a popular swarm intelligence technique based on the intelligent foraging behavior of honey bees, is good at exploration but poor at exploitation. Grenade Explosion Method (GEM) which mimics the mechanism of a grenade explosion has high reliability and fast convergence. Two modified versions of ABC inspired by GEM, namely GABC1 and GABC2, are first proposed to enhance the classical ABC's exploitation ability. GEM is embedded in the employed bees' phase of GABC1, whereas it is embedded in the onlooker bees' phase of GABC2. The performance differences between GABC1 and GABC2 were assessed on two sets of well-known benchmark functions and compared with that of the classical ABC and several other improved ABC algorithms. The experiments show that GABC1 has similar or better performance than GABC2 in most cases, but GABC2 performs more robust and effective than GABC1 on all the functions, they significantly outperform the competitors. These results suggest that the proposed algorithms can effectively serve as alternatives for solving global optimization problems. (C) 2014 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Grenade Explosion Method Exploitation ability Optimal search dimension Global optimization

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artificial bee colony algorithm for Real Estate Portfolio Optimization Based on Risk Preference Coefficient

Artificial Bee Colony Algorithm for Real Estate Portfolio Op...

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17th International Conference on Management Science and Engineering (ICMSE)

作者： Liu Hong-mei Wang Zhuo-fu Li Hui-min Hohai Univ Sch Business Nanjing 210098 Peoples R China

ISBN: (纸本)9781424481194;9781424481170

Optimization of real estate portfolio is to select two or more different types of real estate for investment, and the previous models based on expected return-variance cannot meet the needs of the investors. Furthermore, the investors change their risk preference with the risk level. In this study, firstly, the semi-variance model of real estate investment portfolio based on risk preference coefficient was constructed. The return per unit of risk is the key factor to determine an investment decision. Secondly, artificial bee colony algorithm (ABC) was employed to solve the constructed model. Finally, a real-world case was analyzed to verify the performance of ABC. The result indicated that ABC could generate better solution than GA and it could be regarded as a useful approach for solving real estate portfolio problem.

关键词： real estate portfolio combinatorial optimization artificial bee colony algorithm risk preference coefficient semi-variance

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High-Quality Codebook Generation of Vector Quantization Using the HT-ABC-LBG algorithm

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2018年第1期34卷 81-102页

作者： Huang, Shu-Chien Natl Pingtung Univ Dept Comp Sci Pingtung 900 Taiwan

This paper proposes a vector quantization (VQ) codebook generation method for image data compression using a combined scheme of Hotelling transform (HT), the artificial bee colony (ABC) algorithm and the Linde-Buzo-Gray (LBG) algorithm. First, the grayscale image is divided into a set of non-overlapping image blocks. Each block is represented by an input vector, and these input vectors are then sorted by Hotelling transform. Second, the ABC algorithm is employed to select some of the sorted vectors to form an initial codebook. Third, this codebook serves as the input of the LBG algorithm to compute vector quantization codebook. The experimental results show that the proposed HT-ABC-LBG algorithm outperforms the FF-LBG algorithm in terms of the quality of the decompressed image and the computation time.

关键词： vector quantization image compression codebook generation Hotelling transform artificial bee colony algorithm

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An Optimal Image-Based Method for Identification of Acoustic Emission (AE) Sources in Plate-Like Structures Using a Lead Zirconium Titanate (PZT) Sensor Array

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SENSORS 2018年第2期18卷 631-631页

作者： Yan, Gang Zhou, Li Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn State Key Lab Mech & Control Mech Struct Nanjing 210016 Jiangsu Peoples R China

This paper proposes an innovative method for identifying the locations of multiple simultaneous acoustic emission (AE) events in plate-like structures from the view of image processing. By using a linear lead zirconium titanate (PZT) sensor array to record the AE wave signals, a reverse-time frequency-wavenumber (f-k) migration is employed to produce images displaying the locations of AE sources by back-propagating the AE waves. Lamb wave theory is included in the f-k migration to consider the dispersive property of the AE waves. Since the exact occurrence time of the AE events is usually unknown when recording the AE wave signals, a heuristic artificial bee colony (ABC) algorithm combined with an optimal criterion using minimum Shannon entropy is used to find the image with the identified AE source locations and occurrence time that mostly approximate the actual ones. Experimental studies on an aluminum plate with AE events simulated by PZT actuators are performed to validate the applicability and effectiveness of the proposed optimal image-based AE source identification method.

关键词： AE source identification reverse-time f-k migration image processing minimum Shannon entropy 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 & Multimedia Technology(第四届IEEE宽带网络与多媒体国际会议 4th IEEE IC-BNMT2011)

作者： Ming-Huwi Horng Ting-Wei Jiang Department of Computer Science and Information Engineering National PingTung Institute of Commerce Pingtung Taiwan China

The vector quantization was a powerful technique in image compression. The 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 quantization. The alterative method called the quantum particle swarm optimization was developed to improve the results of original PSO algorithm. The honey bee mating optimization was used to develop the algorithm for vector quantization. In this paper, we proposed a new method based on the artificial bee colony (ABC) algorithm to construct the codebook of vector quantization. The proposed method uses LBG method as the initial of ABC algorithm to develop the VQ algorithm. This method is called ABCLBG algorithm. The 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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An Improved artificial bee colony algorithm

An Improved Artificial Bee Colony Algorithm

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2011 3rd International Conference on Computer Research and Development(ICCRD 2011)

作者： Xiaojun Bi The College of Information and Communication Engineering The School of Harbin Engineering University Harbin,China Yanjiao Wang The College of Information and Communication Engineering The School of Harbin Engineering University Harbin,China

The artificial bee colony(ABC)algorithm is a new swarm optimization algorithm with good numerical optimization *** paper presents an improved algorithm called fast mutation artificial bee colony algorithm or *** choosing food sources,the onlookers use the pheromone and the sensitivity model in Free Search algorithm to replace the traditional roulette wheel selection ***,a mutation strategy based on opposition-based learning was proposed instead of the behavior of scouts. Application of this improved ABC algorithm on seven benchmark optimization functions shows a marked improvement in performance over the traditional ABC.

关键词： artificial bee colony algorithm selection sechem opposition-based learning numerical optimization

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Thermoeconomic analysis and multi-objective optimisation of MED-TVC and *** systems using ABC algorithm

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INTERNATIONAL JOURNAL OF EXERGY 2018年第3期26卷 329-358页

作者： Beyralvand, Davood Yaghoubi, Mahmood Shiraz Univ Sch Mech Engn Mollasadra Ave Shiraz *** Iran

In this study, 4E analysis tor feasible integrated systems for cogeneration of fresh water was investigated. Steam generation provided by a steam boiler, designed to supply the thermal heat requirements of MED-TVC and *** systems. Parametric analysis is made to determine CO2 emission rate, gain output ratio, exergy destruction rate and special heat rate for each system. By using artificial bee colony algorithm, systems were optimised for maximum exergy efficiency and minimum cost of the produced distilled water. The results showed that by selecting final optimum solutions, the distillated water cost reduced by 18.1% and 28.8% for MED-TVC and MED-TVC .FH systems respectively. Also exergy efficiency increased from 3.2% and 4.04% in the base case to 3.63% and 4.47% for MED-TVC and *** respectively in the optimum case. The system with feed water preheater, has more exergy efficiency and less CO2 emission, also the cost of distilled water reduced by 7. 86.%

关键词： 4E analysis MED-TVC MED-TVC.FH CO2 emission exergoeconomic multi objective optimisation artificial bee colony algorithm TRR method

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