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artificial bee colony algorithm with dynamic multi-population

MODERN PHYSICS LETTERS B 2017年第19-21期31卷

作者： Zhang, Ming Ji, Zhicheng Wang, Yan Jiangnan Univ Minist Educ Engn Res Ctr IoT Technol Applicat Wuxi 214122 Peoples R China Jiangnan Univ Sch IoT & Engn Wuxi 214122 Peoples R China

To improve the convergence rate and make a balance between the global search and local turning abilities, this paper proposes a decentralized form of artificial bee colony (ABC) algorithm with dynamic multi-populations by means of fuzzy C-means (FCM) clustering. Each subpopulation periodically enlarges with the same size during the search process, and the overlapping individuals among different subareas work for delivering information acting as exploring the search space with diffusion of solutions. Moreover, a Gaussian-based search equation with redefined local attractor is proposed to further accelerate the diffusion of the best solution and guide the search towards potential areas. Experimental results on a set of benchmarks demonstrate the competitive performance of our proposed approach.

关键词： artificial bee colony algorithm fuzzy C-meansclustering method dynamic subpopulation Gaussian-based search equation local attractor

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artificial bee colony algorithm for Two-dimensional Loading Capacitated Vehicle Routing Problem

Artificial Bee Colony Algorithm for Two-dimensional Loading ...

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20th International Annual Conference on Management Science and Engineering

作者： Wu Bin Cai Hong Cui Zhi-yong Nanjing Univ Technol Sch Econ & Management Nanjing 210009 Jiangsu Peoples R China

ISBN: (纸本)9781479904716

The artificial bee colony algorithm (ABC) with three loading heuristics for the two-dimensional loading capacitated vehicle routing problem (2L-CVRP) is presented in the paper. The 2L-CVRP is a combination of two well-known NP-hard problems, the capacitated vehicle routing problem, and the two-dimensional bin packing problem. It is very difficult to get a good performance solution in practice for these problems. The problem is solved by different heuristics for the loading part, and by artificial bee colony algorithm for the overall optimization. To solve the representation problem of the solution, a novel real encoding is presented to represent the solution for ABC. The effectiveness of the proposed algorithm is tested, and proven by extensive computational experiments on benchmark instances.

关键词： artificial bee colony algorithm 2L-CVRP bin packing problem vehicle routing problem

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artificial bee colony algorithm with Two-Stage Eagle Strategy

Artificial Bee Colony Algorithm with Two-Stage Eagle Strateg...

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9th International Conference on Computational Intelligence and Security (CIS)

作者： Jia, Ruimin He, Dengxu Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning Peoples R China Guangxi Univ Nationalities Coll Sci Nanning Peoples R China

ISBN: (纸本)9781479925483

Pointing at that artificial bee colony algorithm (ABC) has the defect of slow search speed and low precision, the article proposed an Improved artificial bee colony algorithm with Two-Eagle Strategy (ETABC) through using a kind of optimization method-Eagle Strategy, and proved the convergence of ETABC. The simulation results show that ETABC is more effective in solving optimization problems.

关键词： Eagle Strategy artificial bee colony algorithm convergence cuckoo search

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artificial bee colony algorithm with Hierarchical Groups for Global Numerical Optimization 1

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1st International Conference on Smart Computing and Communication (SmartCom)

作者： Cui, Laizhong Luo, Yanli Li, Genghui Lu, Nan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (数字)9783319520155

ISBN: (纸本)9783319520155;9783319520148

artificial bee colony (ABC) algorithm is a relatively new swarm-based optimization algorithm, which has been shown to be better than or at least competitive to other evolutionary algorithms (EAs). Since ABC generally performs well in exploration but poorly in exploitation, ABC often shows a slow convergence. In order to address this issue and improve its performance, in this paper, we present a novel artificial bee colony algorithm with hierarchical groups, named HGABC. In employed bee phase of HGABC, the population is divided into three groups based on the fitness values of the food source positions, and three solution search strategies with different characteristics are correspondingly employed by different groups. Moreover, in onlooker bee phase, onlooker bees conduct exploitation in the most promising area of search space, instead of around some good solutions. In order to demonstrate the performance of HGABC, we compare HGABC with four other state-of-the-art ABC variants on 22 benchmark functions with 30D. The experimental results show that HGABC is better than other competitors in terms of solution accuracy and convergence rate.

关键词： artificial bee colony algorithm Hierarchical group Exploitation in the most promising area Global numerical optimization

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artificial bee colony algorithm FOR POWER PLANT OPTIMIZATION

ARTIFICIAL BEE COLONY ALGORITHM FOR POWER PLANT OPTIMIZATION

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27th European Conference on Modelling and Simulation, ECMS 2013

作者： Biegler-Koenig, Friedrich Univ Appl Sci Dept Appl Math D-33615 Bielefeld Germany

ISBN: (纸本)9780956494467

The mathematical model of a fleet of power plants can be optimized with respect to energy production. This involves the solution of a mixed integer problem. Traditionally these problems are solved by linearization of the continuous and non-linear parts and subsequent application of a Simplex-type algorithm. In order to handle nonlinearities, so-called "biomimetic" optimization algorithms can be applied. As an example, we are proposing an approach to first model power plant blocks with fast Neural Networks. Afterwards, we optimize the operation of multi-block power plants over a period of time using an artificial bee colony algorithm.

关键词： artificial bee colony algorithm Evolutionary Methods Neural Networks Mixed Integer Optimization Power Plant Optimization

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artificial bee colony algorithm for the Parallel Test Tasks Scheduling

Artificial Bee Colony Algorithm for the Parallel Test Tasks ...

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International Conference on Manufacturing Engineering and Automation (ICMEA 2012)

作者： Chen Li'an Xiao Mingqing Yu Hang Zhao Liangliang Air Force Univ Automat Test Syst Lab Xian 710038 Peoples R China

ISBN: (纸本)9783037855300

Parallel test is the key technology of the NxTest technology and the parallel test tasks scheduling is one of the important part of parallel test. The mathematical model of the problem was introduced, according to the advantage of solving the problem of dynamic scheduling with artificial bee colony algorithm;an approach of parallel test scheduling based on artificial bee colony algorithm is brought forward. An example was given, the result of simulation shows that this algorithm's constringency fast and the result has a high precision, it is an efficient way of solving the problem of optimized parallel test tasks scheduling.

关键词： Parallel test Test tasks scheduling artificial bee colony algorithm

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artificial bee colony algorithm for Automatic Leukocytes Segmentation in Histopathological Images 9

Artificial Bee Colony Algorithm for Automatic Leukocytes Seg...

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9th Int Conf Ind Information Syst (ICIIS2014)

作者： Sharma, Harish Arya, K. V. Saraswat, Mukesh Rajasthan Tech Univ Kota India ABV Indian Inst Informat Technol & Management Gwalior India Jaypee Inst Informat Technol Noida India

ISBN: (纸本)9781479964994

An automatic segmentation of leukocytes can assist pharmaceutical companies to take decisions in the discovery of drug and encourages for development of automated leukocytes recognition system. Segmentation of leukocytes in tissue images is a complex process due to the presence of various noise effects, large variability in the images, and shape of the nuclei. Surprisingly, rare efforts have been done to automate the segmentation of leukocytes in various disease models on Hematoxylin and Eosin stained tissue images. The present work proposes a novel method based on artificial bee colony algorithm to segment the leukocytes from the images of mice skin sections stained with Hematoxylin and Eosin staining and acquired at 40 x magnification. The results show that the proposed method outperforms when compared with the well known segmentation methods.

关键词： diseases image segmentation medical image processing optimisation pharmaceutical technology shape recognition artificial bee colony algorithm automated leukocytes recognition system automatic leukocytes segmentation disease models drug discovery eosin stained tissue images hematoxylin stained tissue images histopathological images mice skin sections nuclei shape pharmaceutical companies tissue images Accuracy Drugs Image segmentation Mice Optimization Skin Tin image segmentation artificial bee colony algorithm Leukocytes pharmaceutical technology shape recognition Medical image processing Disease Model Drug Discovery Tin Pharmaceutical Preparations segmentation images skin Pattern recognition Segmentation

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artificial bee colony algorithm Based On Self-Adaptive Greedy Strategy 10

Artificial Bee Colony Algorithm Based On Self-Adaptive Greed...

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10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Yang, Zeyu Hu, Haidong Gao, Hao Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing Jiangsu Peoples R China Beijing Inst Control Engn Beijing Peoples R China

ISBN: (纸本)9781538643624

artificial bee colony algorithm is an effective optimization algorithm. In this paper, a novel artificial bee colony algorithm is developed based on a self-adaptive greedy strategy (SAGABC). Each bee should select whether adopts greedy strategy or not based on its fitness value on each generation. Individuals with worse fitness value update with non-greedy strategy then they have more opportunity to get a better score while individuals with better fitness value update with greedy strategy to speed up convergence. In other word, non-greedy part improves diversity of population which contributes to a better global searching ability. Finally, the experience results demonstrate that the developed method shows a competitive performance compared with traditional ABC and other optimization algorithms on a number of benchmark functions.

关键词： artificial bee colony algorithm greedy selection population diversity self-adaptive

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artificial bee colony algorithm Based on Neighboring Information Learning

Artificial Bee Colony Algorithm Based on Neighboring Informa...

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23rd International Conference on Neural Information Processing (ICONIP)

作者： Cui, Laizhong Li, Genghui Lin, Qiuzhen Chen, Jianyong Lu, Nan Zhang, Guanjing Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China E Techco Informat Technol Co Ltd Shenzhen 518060 Peoples R China

ISBN: (纸本)9783319466750;9783319466743

artificial bee colony (ABC) algorithm is one of the most effective and efficient swarm intelligence algorithms for global numerical optimization, which is inspired by the intelligent foraging behavior of honey bees and has shown good performance in most case. However, due to its solution search equation is good at exploration but poor at exploitation, ABC often suffers from a slow convergence speed. In order to solve this concerning issue, in this paper, we propose a novel artificial bee colony algorithm based on neighboring information learning (called NILABC), in which the employed bees and onlooker bees search candidate food source by learning the valuable information from the best food source among their neighbors. Furthermore, the size of the neighbors is linearly increased with the evolutionary process, which is used to ensure the employed bees and onlooker bees obtain the guidance from the best solution in local area at the early stage and the best solution in the global area at the late stage. Through the comparison of NILABC with the basic ABC and some other variants of ABC on 22 benchmark functions, the experimental results demonstrate that NILABC is better than the compared algorithms on most cases in terms of solution quality, robustness and convergence speed.

关键词： Evolutionary algorithm artificial bee colony algorithm Neighboring information learning Ranking-based probability selection Global numerical optimization

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artificial bee colony algorithm for Transient Performance Augmentation of Grid Connected Distributed Generation

Artificial Bee Colony Algorithm for Transient Performance Au...

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1st International Conference on Swarm, Evolutionary, and Memetic Computing

作者： Chatterjee, A. Ghoshal, S. P. Mukherjee, V. Asansol Engn Coll Dept Elect Engn Asansol W Bengal India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India Indian Sch Mines Dept Elect Engn Dhanbad India

ISBN: (纸本)9783642175626

In this paper, a conventional thermal power system equipped with automatic voltage regulator, IEEE type dual input power system stabilizer (PSS) PSS3B and integral controlled automatic generation control loop is considered. A distributed generation (DG) system consisting of aqua electrolyzer, photovoltaic cells, diesel engine generator, and some other energy storage devices like flywheel energy storage system and battery energy storage system is modeled. This hybrid distributed system is connected to the grid. While integrating this DG with the onventional thermal power system, improved transient performance is noticed. Further improvement in the transient performance of this grid connected DG is observed with the usage of superconducting magnetic energy storage device. The different tunable parameters of the proposed hybrid power system model are optimized by artificial bee colony (ABC) algorithm. The optimal solutions offered by the ABC algorithm are compared with those offered by genetic algorithm (GA). It is also revealed that the optimizing performance of the ABC is better than the GA for this specific application.

关键词： artificial bee colony algorithm distributed generation energy storage devices genetic algorithm superconducting magnetic energy storage device

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