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3rd International Conference on Intelligent Information Processing (ICIIP)

作者： Cheng, Xiaohui Liu, Dezhi Guilin Univ Technol Coll Informat Sci & Engn Guangxi Key Lab Embedded Technol & Intelligent Sy Guilin Guangxi Peoples R China Guilin Univ Technol Coll Mech & Control Engn Guilin Guangxi Peoples R China

ISBN: (纸本)9781450364966

In order to improve the electronic triaxial handheld Cloud terrace system response speed, the optimization speed and convergence of the algorithm, the artificial bee colony (ABC) algorithm uses for triaxial stabilization axis motor controller parameters optimization. Simulation shows that Using artificial bee colony algorithm to optimize the control system of PID, the overshoot is reduced by about 80% and the regulation time is reduced by about 60% compared with the traditional PID control system. Therefore, it is feasible to use the optimization mechanism of artificial bee colony algorithm and PID control to improve the response characteristics of the electronic triaxial handheld cloud terrace control system.

关键词： artificial bee colony algorithm Triaxial stabilizer The PID controller

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Forward and backward predictions of the friction stir welding parameters using fuzzy-artificial bee colony-imperialist competitive algorithm systems

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JOURNAL OF INTELLIGENT MANUFACTURING 2015年第2期26卷 307-319页

作者： Teimouri, Reza Baseri, Hamid Babol Univ Technol Dept Mech Engn Babol Sar Iran

Friction stir welding (FSW) process is an environmentally friendly alternative of welding processes. Due to contribution of too many parameters in this process, generation of predictive models which can estimate the process characteristics is really complex. Thus, in order to develop predictive models in this work the fuzzy approaches were applied to anticipate tensile strength, elongation and hardness of FSWed aluminum joints according to variation of tool rotational speed and welding. Current work consists of two main approaches, in first approach manually fuzzy models were used to correlate relationships between inputs and outputs based on human expertise, then these models have been modified using artificial bee colony algorithm (ABC) by selection of appropriate half width for each membership function which minimizes root mean square error (RMSE). In second approach backward mapping was fulfilled to predict appropriate inputs for specified output by using of imperialistic competitive algorithm (ICA) which minimizes modeling error. Results indicated that the developed fuzzy-ABC system generates more accurate prediction rather than manually fuzzy model according to values of RMSE. Also association of modified fuzzy network with ICA is a suitable tool for reverse mapping of FSW process.

关键词： Fuzzy logic Forward and backward mapping artificial bee colony algorithm Imperialistic competitive algorithm

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Evolutionary algorithms for Designing Metalenses 3

Evolutionary Algorithms for Designing Metalenses

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3rd International Conference on Microwave and Photonics (ICMAP)

作者： Donda, Krupali D. Hegde, Ravi S. Indian Inst Technol Dept Elect Engn Gandhinagar India

ISBN: (纸本)9781538609330

The paper describes an evolutionary algorithm based method for the rapid design of efficient metasurface flat optics lenses (metalens). Current design methods based on the local-phase approach suffer from reduced efficiency, particularly, towards the edges of the lenses. The shortcomings of the local phase approach can be addressed by resorting to extended unit cells. We have recently reported on evolutionary approaches Ill that are helpful in designing such extended unit-cells. The work on beam-deflecting metasurfaces using extended unit-cells reported earlier is extended here to the design of an entire metalens composed of many such beam-deflectors.

关键词： All dielectric metasurface global optimization genetic optimization artificial bee colony algorithm

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Comparative Survey of Swarm Intelligence Optimization Approaches for ANN Optimization 2nd

Comparative Survey of Swarm Intelligence Optimization Approa...

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2nd International Conference on Intelligent Communication, Control and Devices (ICICCD)

作者： Kaur, Jaspreet Kalra, Ashima Sharma, Dolly Chandigarh Engn Coll Elect & Commun Engn Landran India

ISBN: (纸本)9789811059032;9789811059025

Swarm intelligence (SI) approaches are a group of populace-dependent, nature influenced meta-heuristic approaches that are impressed via collective intelligence of homogeneous insects, birds, etc. These algorithms simulate the behaviour of the group of homogeneous biological entities to get a global ideal solution in optimization problems, where classical optimization algorithms may fail. Examples consist of a flock of birds, colonies of bees, colonies of ants, school of fish, etc. This paper presents a comparative study of different swarm intelligence approaches: particles swarm optimization (PSO) algorithm, intelligent water drop (IWD) approach, artificial bee colony (ABC) algorithm and ant colony optimization (ACO) algorithm for the optimization of single-layer neural networks.

关键词： Swarm intelligence Particles swarm optimization algorithm Intelligent water drop approach artificial bee colony algorithm Ant colony optimization approach

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An Efficient Diagnosis System for Thyroid Disease Based on Enhanced Kernelized Extreme Learning Machine Approach 1

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2nd International Conference on Cognitive Computing (ICCC) Held as Part of the Services Conference Federation (SCF)

作者： Ma, Chao Guan, Jian Zhao, Wenyong Wang, Chaolun Shenzhen Inst Informat Technol Coll Digital Media Shenzhen 518172 Peoples R China Harbin Inst Technol Shenzhen Grad Sch Comp Applicat Res Ctr Shenzhen 518055 Peoples R China DFH Satellite Co Ltd Beijing 100094 Peoples R China

ISBN: (数字)9783319943077

ISBN: (纸本)9783319943077;9783319943060

In this paper, we present a novel hybrid diagnosis system named LFDA-EKELM, which integrates local fisher discriminant analysis (LFDA) and kernelized extreme learning machine method for thyroid disease diagnosis. The proposed method comprises of three stages. Focusing on dimension reduction, the first stage employs LFDA as a feature extraction tool to construct more discriminative subspace for classification, the system switches from feature extraction to model construction. And then, the obtained feature subsets are fed into designed kernelized ELM (KELM) classifier to train an optimal predictor model whose parameters are adaptively specified by improving artificial bee colony (IABC) approach. Here, the proposed IABC method introduces an improved solution search equation to enhance the exploitation of searching for solutions, and provides a new framework to make the global converge rapidly. Finally, the enhanced-KELM (EKELM) model is applied to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed system is evaluated on the thyroid disease dataset in terms of classification accuracy. Experimental results demonstrate that LFDA-EKELM outperforms the baseline methods.

关键词： Kernelized extreme learning machine Feature extraction Medical diagnosis artificial bee colony algorithm

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Design of QMF Bank According to Power Spectral Intensity Using ABC algorithm 26

Design of QMF Bank According to Power Spectral Intensity Usi...

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26th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Koza, Turker Karaboga, Nurhan Bozok Univ Elekt Programi Yozgat Turkey Erciyes Univ Elekt Elekt Muhendisligi Bolumu Kayseri Turkey

ISBN: (纸本)9781538615010

Quarter mirror filters (QMF) banks are filter banks where a prototype filter is designed and the other filters are obtained by converting the prototype filter. In this study, a new QMF design method is proposed based on the power spectral density of the signals. Parameters of the filter bank designed for an artificially generated EEG signal are designed with the aid of artificial bee colony (ABC) algorithm. The success of the designed filter bank was also tested by testing on real EEG signals and the results obtained were interpreted.

关键词： quadrature mirror filter bank artificial bee colony algorithm power spectral density

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Image Threshold Segmentation Based on An Improved bee colony algorithm 8

Image Threshold Segmentation Based on An Improved Bee Colony...

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8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Huo Fengcai Wang Di Ren Weijian Heilongjiang Prov Key Lab Networking & Intelligen Daqing Peoples R China Northeast Petr Univ Dept Elect Informat Engn Daqing Peoples R China

ISBN: (纸本)9781538682463

Image segmentation means that the image is divided into specific and unique regions. There are many existing image segmentation methods, and the threshold-based segmentation method is widely applied because of its easy implementation, simplicity and high efficiency. In this paper, artificial bee colony algorithm is applied to image threshold segmentation. Kapur entropy is used as a fitness function, the artificial bee colony algorithm is improved through the adaptive scaling factor. The search area is enlarged through the large-scale factor, the neighborhood search scope is reduced through the small-scale factor and the search efficiency is enhanced. Finally, by comparing the PSNR values of the image, the algorithm has a good segmentation effect and good convergence performance.

关键词： image threshold segmentation artificial bee colony algorithm improved artificial bee colony algorithm Kapur entropy

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Energy-Aware Parallel Computing for Cloud Architecture using ANN

Energy-Aware Parallel Computing for Cloud Architecture using...

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Global Conference for Advancement in Technology (GCAT)

作者： Sakshi Kapoor Surya Narayan Panda Chitkara University Institute Engineering and Technology Punjab India

ISBN: (纸本)9781728136950

In parallel processing, task scheduling is a way to assign task to different processors. A number of algorithms are used by parallel computing so that the time for executing jobs can be minimized. Presently, there are number of algorithms that are utilized for the enhancement of the execution time of task as well as to reduce the energy utilization. In this research, to resolve the energy consumption problem, the concept of load balancing has been introduced along with the artificial bee colony (ABC) and artificial Neural Network (ANN) techniques. ABC is used to optimize the properties of processors based on the appropriate selection of fitness function. On the basis of these optimized properties, ANN is trained and hence balance the load so that energy can be optimized along with minimization of makespan.

关键词： artificial bee colony algorithm cloud computing minimisation neural nets parallel processing power aware computing resource allocation scheduling PARALLEL PROCESSING (COMPUTERS) power aware computing Neural network Cloud Computing artificial neural networks Resource utilization artificial bee colony algorithm execution time energy utilisation management of resources PROCESSOR algorithms

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Probabilistic Dynamic Deployment of Wireless Sensor Networks by artificial bee colony algorithm

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SENSORS 2011年第6期11卷 6056-6065页

作者： Ozturk, Celal Karaboga, Dervis Gorkemli, Beyza Erciyes Univ Dept Comp Engn Fac Engn TR-38039 Kayseri Turkey

As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks.

关键词： artificial bee colony algorithm wireless sensor networks dynamic deployment probabilistic detection model

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An Interval Number Optimization Method for Post - Disaster Repair of Distribution Network considering Island

An Interval Number Optimization Method for Post - Disaster R...

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International Conference on Power System Technology (POWERCON)

作者： Song Xudong Zhang Xiaoping Xiao Zilong Guangdong Power Grid Co Ltd Elect Power Res Inst Guangzhou Guangdong Peoples R China China Southern Power Grid Key Lab Power Grid Automat Lab Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538664612

In this paper, the optimization method of post-disaster distribution network repair strategy based on interval optimization is proposed. According to the problems existing in the process of repairing, the repair model considering the various uncertainties in the repair process and the corresponding constraints has put forward, in order to reduce the social and economic losses as the ultimate goal. According to the radial structure of the distribution network, topology search is used to quickly find the reoperation area. Through fuzzy equilibrium, the formulation of post-disaster repair strategies is accelerated, and an improved bee colony algorithm is used to find the optimal solution. Finally, a 15-node system is introduced, and the simulation and analysis are carried out on the MATLAB platform to verify the practicability and effectiveness of the optimization strategy.

关键词： Post-disaster repair distribution network interval optimization artificial bee colony algorithm

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