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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Ng, K. K. H. Lee, C. K. M. Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781509036653

Due to the increase in air transportation demand, runway capacity is reaching a bottleneck at the international airports, especially during peak hours. Managing on aircraft sequencing or sequencing problems in airport perspective have become a crucial operation nowadays in order to maintain safety landing and utilize the runway facility to handle the schedules for all incoming aircraft under congestion. The traditional approach allows aircraft to remain an economic speed during approaching to the airport. However, such approach may not be applicable in congested air traffic situation. Therefore, the makespan minimization is more practical for the rescheduling efforts afterwards. This article presents a modified artificial bee colony algorithm to obtain nearly optimal solution to support the air tower controller in order to obtain last-minute decisions of landing sequence. The modified artificial bee colony algorithm for aircraft landing problem provides a promising optimal search within 6.1 seconds to handle last-minute disruption.

关键词： Aircraft landing problem Air traffic control Meta-heuristics artificial bee colony algorithm

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A Novel Comparative Study between Dual Population Genetic algorithm and artificial bee colony algorithm for Function Optimization 19

A Novel Comparative Study between Dual Population Genetic Al...

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19th International Conference on Computer and Information Technology (ICCIT)

作者： Alam, Faria Saadi, Hossain Shaikh Alam, Mohammad Shafiul Ahsanullah Univ Sci & Technol Dept Comp Sci & Engn Dhaka 1208 Bangladesh

ISBN: (纸本)9781509040902

This paper conducts a comparative study between an improved variants of genetic algorithm (GA) and a swarm intelligence algorithm (SIA), which are the Dual population Genetic algorithm (DPGA) and artificial bee colony (ABC) algorithm. DPGA is a multi-population genetic algorithm (MPGA) that implements two population such as the main population and a complementary population. Since the added population has a totally different fitness function, it is preserved to supply sufficient diversity to the main population by crossover operation. However DPGA employs a dynamic strategy to maintain an appropriate distance between two populations for maintaining diversity. On the contrary ABC, a simple but exceptional derivative of SIA, applies division of labor in single population of artificial bees and allocates some of them to exploration while the others to exploitation. DPGA has its own techniques to sustain the significant balance between exploration vs. exploitation. Thus many such analytical comparisons between DPGA and ABC are the center of attention of this paper. Experiments are conducted on seven benchmark functions using ABC and results are compared with DPGA. The results demonstrate that DPGA performs well for some of the functions but by considering the result of mean absolute error, ABC performs far better than DPGA.

关键词： Dual Population Genetic algorithm artificial bee colony algorithm Genetic algorithm Swarm Intelligence algorithm

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Product life cycle based demand forecasting by using artificial bee colony algorithm optimized two-stage polynomial fitting 11

Product life cycle based demand forecasting by using artific...

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Liu Yue Ju Wangwei Zhao Jianguo Gao Junjun Zheng Jiazhou Jiang Aiping Shanghai Univ Sch Comp Engn & Sci 99 Shangda Rd Shanghai 200444 Peoples R China Shanghai Univ Sydney Inst Language & Commerce Shanghai Peoples R China

Demand forecasting is one of the most essential components of supply chain management, which directly influences a company's overall performance and competitiveness. However, it is difficult to accurately forecast the demand of fashion products with short life cycle and high volatility characteristics such as footwear and apparel products. An integrated demand forecasting method named Improved ABC-PF is proposed in this paper based on Product Life Cycle (PLC) theory considering the characteristics of fashion products. First, a PLC model based on cubic polynomial which is divided into two stages by the best-selling point, is established instead of traditional PLC modeling methods. Second, an improved artificial bee colony (ABC) algorithm is utilized to optimize the parameters of the two-stage PLC function, which is conducted by initial population selection, optimization function design and convergence rate improvement. After that, an inventory control strategy based on PLC analysis is studied and applied in the "Precise Order" mode. Finally, the proposed method is validated by real-world data from a Chinese footwear and apparel retailer. After being compared with the other demand forecasting methods such as Moving Average (MA), Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN), it is indicated that the proposed improved ABC-PF method can achieve higher prediction accuracy and lower safety inventory level, which improve the overall profitability of the company, therefore generate product demand management insights for footwear and apparel enterprises.

关键词： Demand forecasting product life cycle artificial bee colony algorithm

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Optimal Placement and Sizing of DG in Distribution System using artificial bee colony algorithm 6

Optimal Placement and Sizing of DG in Distribution System us...

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6th IEEE International Conference on Power Systems (ICPS)

作者： Dixit, Mukul Kundu, Prasanta Jariwala, Hitesh R. SV Natl Inst Technol Dept Elect Engn Surat India

ISBN: (纸本)9781509001286

This paper presents an optimal allocation and sizing of Distributed Generation (DG) in Radial Distribution Network (RDN) for total power loss minimization and enhance the voltage profile of the system. The two main important part of this study first is to find optimal allocation and second is optimum size of DG. The locations of DGs are identified by using Index Vector Method (IVM) approach and artificial bee colony (ABC) optimization algorithm has been employed to determine the optimum size of DG. In this study, the DG has been placed on single and multiple allocations at different power factor. The proposed technique has been demonstrated on standard 15-bus and 33-bus RDN. The comparison of single and multiple DG in terms of real power loss, voltage profile at different power factor are also mentioned in this paper.

关键词： artificial bee colony algorithm distributed generation Index Vector Method power loss voltage profile

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Minimax Design of Linear Phase FIR Differentiators using artificial bee colony algorithm 8

Minimax Design of Linear Phase FIR Differentiators using Art...

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8th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Kwan, Hon Keung Raju, Rija Univ Windsor Dept Elect & Comp Engn Windsor ON Canada

ISBN: (纸本)9781509028603

artificial bee colony (ABC) algorithm is a swarm based meta-heuristic algorithm inspired by the foraging behavior of honey bees. Due to its simplicity, the ABC algorithm is used for minimax design of linear phase FIR fullband digital differentiators in this paper. Results in term of peak error obtained from designed digital differentiator examples indicate that the approach can reach smaller peak errors when compared to those obtained by the least-squares error minimization method and the optimal Parks-McClellan algorithm.

关键词： Digital differentiators Fullband digital differentiator design Type 4 linear phase FIR digital filters Minimax design artificial bee colony algorithm Evolutionary optimization Global optimization

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artificial bee colony algorithm with multiple search strategies

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APPLIED MATHEMATICS AND COMPUTATION 2015年 271卷 269-287页

作者： Gao, Wei-feng Huang, Ling-ling Liu, San-yang Chan, Felix T. S. Dai, Cai Shan, Xian China Univ Petr Sch Sci Qingdao 266580 Peoples R China Xidian Univ Sch Math & Stat Xian 710071 Shannxi Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China Shaanxi Normal Univ Coll Comp Sci Xian 710062 Peoples R China

Considering that the solution search equation of artificial bee colony (ABC) algorithm does well in exploration but badly in exploitation which results in slow convergence, this paper studies whether the performance of ABC can be improved by combining different search strategies, which have distinct advantages. Based on this consideration, we develop a novel ABC with multiple search strategies, named MuABC. MuABC uses three search strategies to constitute a strategy candidate pool. In order to further improve the performance of the algorithm, an adaptive selection mechanism is used to choose suitable search strategies to generate candidate solutions based on the previous search experience. In addition, a candidate solution is generated based on a Gaussian distribution to exploit the search ability. MuABC is tested on a set of 22 benchmark functions, and is compared with some other ABCs and several state-of-the-art algorithms. The comparison results show that the proposed algorithm offers the highest solution quality, the fastest global convergence, and the strongest robustness among all the contenders on almost all the cases. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Evolutionary algorithms artificial bee colony algorithm Strategy candidate pool Gaussian distribution Search equation

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An Improved Gbest-guided artificial bee colony algorithm Based on Dynamic Regulatory Factor 8

An Improved Gbest-guided Artificial Bee Colony Algorithm Bas...

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8th International Conference on Advanced Computational Intelligence (ICACI)

作者： Huo, Jiuyuan Meng, Fanming Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou Peoples R China

ISBN: (纸本)9781467377829

To address the problems of Gbest-guided artificial bee colony (GABC) algorithm that the low speed in the searching process and the solution is easy to fall into the local optimal, we presented an improved artificial algorithm with the dynamic regulatory factor (DRF-GABC). The dynamic regulatory factor was introduced into GABC algorithm to dynamically regulate the global optimization process and local optimal process of the algorithm. The improved algorithm was used for optimizing a set of numerical test functions and compared with the original ABC algorithm and the GABC algorithm. The experiments results show that the convergence performance of DRF-GABC algorithm is better than ABC algorithm and GABC algorithm, and it is suitable for solving optimization problems of complex function.

关键词： artificial bee colony algorithm gbest-guided artificial bee colony algorithm dynamic regulatory factor optimization algorithm

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A Gravitational artificial bee colony Optimization algorithm and Application

A Gravitational Artificial Bee Colony Optimization Algorithm...

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International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC)

作者： Lingling Zhang Noncommissioned Officer Academy of CAPF Hangzhou China

ISBN: (数字)9781538682463

ISBN: (纸本)9781538682470

In this paper, a novel Gravitational artificial bee colony (GABC) optimization algorithm was proposed and utilized to the non-supervised pattern recognition problems. In this approach, the gravitational search strategy was introduced into the artificial bee colony algorithm, and a gravitational bee colony was established. The gravitational bee could search the global optimal result under the influence of both gravitational force and colony cooperation, which makes the optimization process more effectively and efficiently. Based on GABC algorithm, an intelligent kernel clustering model was established, in which the clustering center and kernel parameters were combined to be the optimal variable, while the clustering index was used as the objective function. GABC was utilized to find the optimal result of the clustering model. The standard testing functions were used to test the proposed algorithm, and GABC showed high accuracy and convergence speed. Then the testing data and fault samples were utilized to test the performance of GABC based clustering model, and its superiority on effectiveness and efficiency was demonstrated.

关键词： Optimization Testing Clustering algorithms Search problems Kernel artificial bee colony algorithm Training

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Improved artificial bee colony algorithm with Randomized Halton Sequence 2

Improved Artificial Bee Colony Algorithm with Randomized Hal...

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2nd IEEE International Conference on Computer and Communications (ICCC)

作者： He, Zhen-An Ma, Caiwen Chinese Acad Sci Xian Inst Opt & Precis Mech Xian Shaanxi Peoples R China

ISBN: (纸本)9781467390262

The artificial bee colony (ABC) algorithm has gained popularity for solutions to many real-world complex problems because of its simplicity and efficiency. However, it is good at exploration but poor at exploitation and early gets trapped into local optimum in some case, which prevents the artificial bee colony algorithm from finding the final result accuracy and efficiently for complex problems. It's known to all that population initialization is an important factor affecting convergence performance of ABC. In this paper, a modified artificial bee colony algorithm with randomized Halton sequence was proposed. A series of experiments were performed on benchmark functions to testify the superiority of our proposed algorithm, and results were compared with other initialization algorithms including opposition-based learning, random initialization and chaotic initialization. The results indicate that our proposed algorithm provides higher solution accuracy and faster convergence speed.

关键词： artificial bee colony algorithm population initialization randomized Halton sequence

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Application of artificial bee: colony algorithm Using Hadoop 3

Application of Artificial BEE: Colony Algorithm Using Hadoop

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3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Bansal, Nupur Kumar, Sanjay Tripathi, Ashish Delhi Technol Univ Dept Comp Engn Delhi India

ISBN: (纸本)9789380544199

Big Data is the latest buzzword in IT industry especially for the last couple of years. Initially this word was used by organizations which had to deal with speedy growth of data such as web data, data which comes from scientific and business simulations or any other data resource. The Google File System and MapReduce Architecture have been the result of the pressure to handle continuously growing amount of data on the web (Ghemawat, Dean, & Sanjay, 2008). Evolutionary Computing (EC) is a stream of Computer Science that adapts the theory given as Darwinian evolution which is used to optimize many computing problems (Eiben & Smith, 2003). The concept of EC revolves around various natural evolution processes such as competition, reproduction, preying and random variation. Since evolution itself is an optimization process, thus the process of EC is applicable to a huge range of problems. One evolutionary algorithm is the artificial bee colony (ABC) which is simple and effective, but this algorithm may take long hours or days to optimize difficult deceptive and/or expensive objective functions. ABC can be expressed naturally in Google's MapReduce framework (Ghemawat, Dean, & Sanjay, 2008) to develop a simple and robust implementation which is parallel as well that includes communication, fault tolerance and load balancing. Since this implementation is flexible it is easy to do some modifications to it, which can improve optimization of objective functions and also improve parallel performance. In this paper, we propose and realize an approach to implement artificial bee colony algorithm in a parallel manner and use it to compute the effort of COCOMO Model. The population of the bee here are in the form of big data are stored in Hadoop Distributed File System (HDFS) on Hadoop to have better fault tolerance and to store large amount of data in distributed manner.

关键词： Big Data artificial bee colony algorithm Hadoap COCOMO Model Optimization

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