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8th International Conference on Information and Knowledge Technology (IKT)

作者： Reisi, Manijeh Moradi, Parham Abdollahpouri, Alireza Univ Kurdistan Dept Comp Engn Sanandaj Iran

ISBN: (纸本)9781509043354

Data clustering is a powerful technique for data analysis that used in many applications. The goal of clustering is to detect groups that objects of each group have the most similarity together. artificial bee colony (ABC) is a simple algorithm with few control parameters to solve clustering problem. However, traditional ABC algorithm is considered the equal importance for all features, while real world applications carry different importance on features. To overcome this issue, we proposed a feature weighting based artificial bee colony (FWABC) algorithm for data clustering. The proposed algorithm considers a specific importance to each feature. The performance of the proposed method has been tested on various datasets and compared to well-known and state-of-the-art methods, the reported results show that the proposed method outperforms other methods.

关键词： Data clustering artificial bee colony algorithm feature weigthing

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Heuristic Task Scheduling with artificial bee colony algorithm for Virtual Machines 8

Heuristic Task Scheduling with Artificial Bee Colony Algorit...

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Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) / 17th International Symposium on Advanced Intelligent Systems (ISIS)

作者： Kimpan, Warangkhana Kruekaew, Boonhatai King Mongkuts Inst Technol Ladkrabang Fac Sci Dept Comp Sci Bangkok Thailand

ISBN: (纸本)9781509026784

Cloud computing is one of the Information Technology services which are provided from IT infrastructures to application services. It is the combination of Distributed computing and virtualization technology using virtual machines, an essential component in Cloud computing. Therefore, task scheduling is an important matter to consider for virtual machines to balance load of each machine and to efficiently use the resources in Cloud computing. This paper proposes the use of Heuristic task scheduling with artificial bee colony algorithm for virtual machines in heterogeneous Cloud computing, called HABC. The research aim is to introduce HABC, which is a new task scheduling and load balancing algorithm, for virtual machines in heterogeneous environments to reduce the makespan in the system. In the experiments, CloudSim was simulated to compare various types of the optimization task scheduling in using the virtual machines. The experimental results indicated that using the proposed artificial bee colony algorithm when large job was considered first (HABC_LJF) in virtual machine scheduling, improved the efficiency in task scheduling and load balancing of virtual machines in Cloud computing. In addition, the proposed algorithm can minimize the makespan even if the tasks are increased and the different types of data are distributed.

关键词： cloud scheduling artificial bee colony algorithm scheduling load balancing normal distribution gaussian distribution heuristics

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A Multiswarm artificial bee colony algorithm for Dynamic Optimization Problems

A Multiswarm Artificial Bee Colony Algorithm for Dynamic Opt...

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International Conference on Information System and artificial Intelligence (ISAI)

作者： Jia, Dong-Li Qu, Shuang-Xue Li, Long-Yan Hebei Univ Engn Sch Informat & Elect Engn Handan Peoples R China

ISBN: (纸本)9781509015856

artificial bee colony (ABC) algorithm has been applied to many scientific and engineering problems for its efficiency. However, the original ABC technique cannot be used in dynamic environments directly. This paper proposes a multiswarm ABC algorithm, or MABC, that has a similar framework as the original ABC but uses environment detecting technique to track the moving of the optimum of dynamic problems. Experiments have been carried out by comparing MABC with original ABC on moving peaks benchmark (MPB). Results show that the proposed MABC performs better in terms of the offline error, convergence speed, and robustness.

关键词： artificial bee colony algorithm swarm intelligence dynamic environment moving peaks benchmark

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Assembly Sequence Planning Based on Hybrid artificial bee colony algorithm 9th

Assembly Sequence Planning Based on Hybrid Artificial Bee Co...

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9th IFIP TC 12 International Conference on Intelligent Information Processing (IIP)

作者： Yuan, Wenbing Chang, Liang Zhu, Manli Gu, Tianlong Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China

ISBN: (纸本)9783319483900;9783319483894

Intelligent algorithm provides a promising approach for solving the Assembly Sequence Planning (ASP) problem on complex products, but there is still challenge in finding best solutions efficiently. In this paper, the artificial bee colony algorithm is modified to deal with this challenge. The algorithm is modified from four aspects. First, for the phase that employed bee works, a simulated annealing operator is introduced to enrich the diversity of nectar sources and to enhance the local searching ability. Secondly, in order to prevent the swarm from falling into local optimal solutions quickly, a tournament selection mechanism is introduced for the onlooker bees to choose the food source. Thirdly, for the phase that scout bee works, a learning mechanism is introduced to improve the quality of new generated food sources and to increase the convergence speed of the algorithm. Finally, a fitness function based on the evaluation indexes of assemblies is proposed to evaluate and select nectar sources. The experimental results show that the modified algorithm is effective and efficient for the ASP problem.

关键词： artificial bee colony algorithm Simulated annealing operators Assembly sequence planning Multi-objective optimization

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The Bisection-artificial bee colony algorithm to solve Fixed point problems

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APPLIED SOFT COMPUTING 2015年 26卷 143-148页

作者： Mansouri, P. Asady, B. Gupta, N. Islamic Azad Univ Dept Math Arak Branch Arak Iran Univ Delhi Dept Comp Sci Delhi 110007 India

In this paper, we introduce a novel iterative method to finding the fixed point of a nonlinear function. Therefore, we combine ideas proposed in artificial bee colony algorithm (Karaboga and Basturk, 2007) and Bisection method (Burden and Douglas, 1985). This method is new and very efficient for solving a nonlinear equation. We illustrate this method with four benchmark functions and compare results with others methods, such as ABC, PSO, GA and Firefly algorithms. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Bisection method Fixed point problems artificial bee colony algorithm

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Parallel Modified artificial bee colony algorithm for Solving Conditional Nonlinear Optimal Perturbation

Parallel Modified Artificial Bee Colony Algorithm for Solvin...

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18th IEEE International Conference on High Performance Computing and Communications (HPCC) / 14th IEEE International Conference on Smart City (Smart City) / 2nd IEEE International Conference on Data Science and Systems (DSS)

作者： Ren, Juhui Yuan, Shijin Mu, Bin Tongji Univ Sch Software Engn Shanghai Peoples R China

ISBN: (纸本)9781509042975

Intelligent algorithms have been applied to solving conditional nonlinear optimal perturbation (CNOP), which plays an important role in the study of weather and climate predictability. Single particle intelligent optimization algorithms can get similar CNOP to adjoint method, and show higher time efficiency in solving CNOP. However, swarm intelligent optimization algorithms can only get similar CNOP, and still show lower time efficiency than adjoint method. In this paper, we proposed a modified artificial bee colony algorithm (MABC) to solve CNOP, and to accelerate the computation speed, we parallelize the MABC algorithm with MPI technology. In order to demonstrate its validity and efficiency, we apply MABC algorithm to solving CNOP in Zebiak`-Cane model, which is a medium-complexity numerical model. The results obtained are compared with those from standard bee colony algorithm (ABC) algorithm, generic algorithm (GA) and adjoint method which is the benchmark. The MABC algorithm can get better results than the standard ABC and GA algorithm in solving CNOP, and can obtain similar results with adjoint method in CNOP magnitude and pattern aspects. The parallel MABC with MPI also shows a higher efficiency than adjoint method. All the experimental results show that it is feasible and efficient to solve CNOP with the proposed parallel modified artificial bee colony algorithm.

关键词： artificial bee colony algorithm CNOP MPI Zebiak-Cane model

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artificial bee colony algorithm to design two-channel quadrature mirror filter banks

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SWARM AND EVOLUTIONARY COMPUTATION 2015年 21卷 24-31页

作者： Agrawal, S. K. Sahu, O. P. Natl Inst Technol Elect & Commun Engn Dept Kurukshetra 136119 Haryana India

artificial bee colony (ABC) algorithm has been introduced recently for solving optimization problems. The ABC algorithm is based on intelligent foraging behavior of honeybee swarms and has many advantages over earlier swarm intelligence algorithms. In this work, a new method based on ABC algorithm for designing two-channel quadrature mirror filter (QMF) banks with linear phase is presented. To satisfy the perfect reconstruction condition, low-pass prototype filter coefficients are optimized to minimize an objective function. The objective function is formulated as a weighted sum of four terms, pass-band error, and stop-band residual energy of low-pass analysis filter, square error of the overall transfer function at the quadrature frequency and amplitude distortion of the QMF bank. The design results of the proposed method are compared with earlier reported results of particle swarm optimization (PSO), differential-evolution (DE) and conventional optimization algorithms. (C) 2014 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Perfect reconstruction Swarm intelligence Multirate filter bank

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Solving Hybrid Flow-Shop Scheduling Based on Improved Multi-Objective artificial bee colony algorithm 2

Solving Hybrid Flow-Shop Scheduling Based on Improved Multi-...

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2nd International Conference on Cloud Computing and Internet of Things (CCIOT)

作者： Liang Xu Ji Yeming Huang Ming DJDU Coll Software Dalian Peoples R China

ISBN: (纸本)9781467398237

In the model of hybrid flow shop scheduling problem with unrelated parallel machines, the makespan, total weighted earliness/tardiness and total waiting time are established as evaluation index. An algorithm of artificial bee colony based on the method of adaptive neighborhood search is designed. According to the characteristics of the model, initial processing sequence is used as solution vector in order to narrow down feasible solutions. Fitness of populations is distinguished by non-dominated sorting. In the process of iteration, excellent individuals are retained so that the diversity of population distribution is increased. Finally, the method is applied to a simulation example, compared with the traditional multi-objective algorithm. The results obtained demonstrate that the improved ABC algorithm for hybrid flow shop scheduling problem is good effective and diversified.

关键词： artificial bee colony algorithm hybrid flow-shop scheduling multi-objective unrelated parallel machines

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A Clustering-Based artificial bee colony algorithm 16th

A Clustering-Based Artificial Bee Colony Algorithm

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Joint Conference of the 16th Asia Simulation Conference / SCS International Autumn Simulation Multi-Conference (AsiaSim/SCS AutumnSim)

作者： Zhang, Ming Tian, Na Ji, Zhicheng Wang, Yan Jiangnan Univ Minist Educ Engn Res Ctr Internet Things Technol Applicat Wuxi 214122 Peoples R China

ISBN: (纸本)9789811026638;9789811026621

An advanced artificial bee colony (ABC) algorithm based on fuzzy C-means (FCM) clustering method is presented in this paper, aiming to make a balance between the exploitation and exploration. Firstly, FCM method is employed to divide the population into subpopulations, so that individuals only interact with those in the same subpopulation. Furthermore, the idea of overlapping area has been introduced to the clustering partition, in order to promote the information sharing among different subpopulations. Inspired from the fact that elitist can accelerate convergence, two modified search mechanism has been proposed. The results of experiments based on a set of benchmark functions indicate that our approach is efficient and effective when comparing with some state-of-the-art ABCs.

关键词： artificial bee colony algorithm Fuzzy C-means clustering Overlapping area Modified search mechanism

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A Proposal of Parameters Setting Method for artificial bee colony algorithm with Network Structure 55

A Proposal of Parameters Setting Method for Artificial Bee C...

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55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

作者： Kaihara, Toshiya Fujii, Nobutada Kokuryo, Daisuke Imamura, Shunta Kobe Univ Grad Sch Syst Informat Kobe Hyogo Japan

ISBN: (纸本)9784907764500

artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees is one of the swarm intelligence systems. This algorithm can provide the efficient exploration of the optimal solutions using three different types of the agents for optimization problems with multimodal function. However, the performance of the conventional ABC algorithm decreases for high-dimensional problems. In this study, we propose an improved algorithm to enhance the ability for global search using the network structure of agents. The efficacy of the proposed algorithm is evaluated by performing computer experiments with high-dimensional benchmark problems. We validate solution search performance to consider how to set the parameters.

关键词： artificial bee colony algorithm Network structure Swarm Intelligence High-dimensional problem

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