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IEEE International Conference on Power, Intelligent Computing and Systems

作者： Junqi Ge Xutao Zhang Gongyou Liu Yu Sun College of Computer Science and Technology Jilin University

ISBN: (纸本)9781728137216

The classification of microarray data has positive significance for the judgment of cancer and the determination of clinical programs. However, the high dimensionality and small sample characteristics of the microarray data has brought classification a difficult problem. Aiming at the feature selection problem in microarray data classification, a feature selection algorithm based on artificial bee colony algorithm and genetic algorithm is proposed to solve the dimensionality disaster problem in microarray data classification. Finally, the feature subsets obtained by the algorithm are combined with the SVM (Support Vector Machine) classifier to apply to the six published microarray datasets. The experimental results show that the feature subsets obtained based on the proposed scheme can significantly improve the classification performance.

关键词： Feature selection artificial bee colony algorithm genetic algorithm Microarray data Feature extraction artificial bee colony algorithm data classification Genetic algorithm (GA) Microarray selection algorithm Taxonomy algorithms

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artificial bee colony algorithm with dynamic multi-population

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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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Knowledge map construction using text mining and artificial bee colony algorithm 42

Knowledge map construction using text mining and artificial ...

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42nd Annual IEEE-Computer-Society Computers, Software and Applications (COMPSAC) Conference - Staying Smarter in a Smartening World

作者： Tsai, Chieh-Yuan Ji, Wei-Zhong Yuan Ze Univ Dept Ind Engn & Management Taoyuan Taiwan

ISBN: (纸本)9781538626672

With the rapid development of the information technology, information overload becomes a serious problem during the information acquisition process. To relieve this difficulty, knowledge map is a systematic approach to reveal the underlying relationships between abundant knowledge sources. However, few studies focused on how to optimize the coordinates of knowledge items in the map to help users easily understand complicated relatedness among knowledge topics. To bridge this gap, this paper proposes a novel knowledge map construction approach using text mining and artificial bee colony (ABC) algorithm. First, the textural documents related to a certain domain are represented as a term vector in m-dimensional space with the term frequency-inverse document frequency (TF-IDF) analysis. Second, hierarchical clustering is applied to identify important topics. Third, high-dimensional relationships among knowledge items are transformed into a 2-dimensional space optimized by the ABC algorithm. A set of experiments shows that setting appropriate number of clusters is important for visual perception. In addition, a practical example in topic trend analysis using the proposed approach is demonstrated at the end of this paper.

关键词： Knowledge map Text mining Hierarchical clustering artificial bee colony algorithm

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IIR Filter Design Using Multiobjective artificial bee colony algorithm

IIR Filter Design Using Multiobjective Artificial Bee Colony...

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IEEE Canadian Conference on Electrical & Computer Engineering (CCECE)

作者： Raju, Rija Kwan, Hon Keung Univ Windsor Dept Elect & Comp Engn 401 Sunset Ave Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781538624104

In this paper, IIR filters are designed using Gbest-guided Multiobjective artificial bee colony algorithm (GMOABC). artificial bee colony algorithm (ABC) is a stochastic optimization algorithm inspired by the food seeking behavior of honey bee colonies. Even though ABC algorithm can converge to a global optimum for complex problems, the time taken for convergence is longer than classical methods. Gbest guided multiobjective ABC algorithm can reduce the time taken for converging to global optimum and improves the quality of the solutions by tuning the search process towards the global best in each iteration. IIR filter design is a non-convex optimization problem and requires the optimization of both the magnitude and group delay. The results show that, GMOABC can achieve lower passband error and group delay error than the recent results obtained by other methods.

关键词： IIR filters optimization artificial bee colony algorithm multiobjective optimization minimax design digital filters evolutionary algorithms

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Indoor Emergency Evacuation Model Based on artificial bee colony algorithm

Indoor Emergency Evacuation Model Based on Artificial Bee Co...

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IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems

作者： Xinlu Zong Jiayuan Du Wei Liu Lu Zhang Qian Huang College of Computer Science Hubei University of Technology

ISBN: (纸本)9781728140704

In order to quickly and efficiently simulate the crowd movement process under the evacuation scene, so as to reduce the casualties in emergency evacuation, this article proposes an indoor emergency evacuation model based on the improved artificial swarm algorithm. In this article, the cellular automata (CA) model is used to establish the evacuation environment, and then the artificial bee colony (ABC) algorithm is used to simulate the crowd evacuation. However, due to too many obstacles existing in the neighboring cells of some position, the individual in that position have to wait. If the distance equation is used to calculate the fitness, it may cause other individuals to choose these positions as new position and repeat the same mistake above. To reduce the occurrence of such cases, we improve the fitness function of the ABC algorithm. In the fitness function, the factors of attraction and repulsion force in social force model are introduced. And on the basis of the ABC algorithm, we propose the visual employed bee. The visual employed bee leads the onlooker bee to evacuate, so as to improve the efficiency of evacuation. The research results of this article can provide ideas for evacuation modeling and useful guidance for formulating evacuation strategies to reduce evacuation time and disaster losses.

关键词： artificial bee colony algorithm Cellular automaton model Evacuation Visual employed bee Fitness function Evacuation artificial bee colony algorithm Cellular automata health bee Motor Activity repulsion force

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Application research of multi-objective artificial bee colony optimization algorithm for parameters calibration of hydrological model

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NEURAL COMPUTING & APPLICATIONS 2019年第9期31卷 4715-4732页

作者： Huo, Jiuyuan Liu, Liqun Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China Gansu Agr Univ Coll Informat Sci & Technol Lanzhou 730070 Gansu Peoples R China

Parameter optimization methods for hydrological models have an important impact for the hydrological forecasting. To achieve the parameters' optimization and calibration for the distributed, conceptual watershed Xinanjiang model effectively and accurately, a multi-objective artificial bee colony algorithm named RMOABC which adopts the mechanisms of regulation operator and Adaptive Grid is introduced in the paper. In the evolution of the algorithm, the regulation operator mechanism can balance the weights of local search and global search, and the Adaptive Grid mechanism is utilized to evaluate and maintain the Pareto solutions in the external archive. In the experiments, three commonly used multi-objective optimization algorithms, the NSGA-II, the epsilon-MOEA and the SMPSO, with the RMOABC algorithm were applied in Heihe River Basin, and the parameter optimization problem of Xinanjiang hydrological model was taken as the application case for long-term runoff prediction to validate and compare their performance. The experiments results showed the RMOABC algorithm can provide more comprehensive and reliable parameters sets for practical hydrological forecasting in the study area with lower execution time.

关键词： Hydrological model Parameter calibration Multi-objective optimization algorithm artificial bee colony algorithm

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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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Global artificial bee colony Search algorithm for Data Clustering

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2019年第2期10卷 48-59页

作者： Danish, Zeeshan Shah, Habib Tairan, Nasser Ghazali, Rozaida Badshah, Akhtar Univ Malakand Charsadda Pakistan King Khalid Univ Abha Saudi Arabia Univ Tun Hussein Onn Malaysia Parit Raja Malaysia Univ Malakand Dept Software Engn Charsadda Pakistan

Data clustering is a widespread data compression, vector quantization, data analysis, and data mining technique. In this work, a modified form of ABC, i.e. global artificial bee colony search algorithm (GABCS) is applied to data clustering. In GABCS the modification is due to the fact that experienced bees can use past information of quantity of food and position to adjust their movements in a search space. Due to this fact, solution search equations of the canonical ABC are modified in GABCS and applied to three famous real datasets in this work i.e. iris, thyroid, wine, accessed from the UCI database for the purpose of data clustering and results were compared with few other stated algorithms such as K-NM-PSO, TS, ACO, GA, SA and ABC. The results show that while calculating intra-clustering distances and computation time on all three real datasets, the proposed GABCS algorithm gives far better performance than other algorithms whereas calculating computation numbers it performs adequately as compared to typical ABC.

关键词： artificial bee colony algorithm Clustering Global artificial bee colony Search algorithm

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Error Compensation Method of Magnetometer for Attitude Measurement Using Modified artificial bee colony algorithm

Error Compensation Method of Magnetometer for Attitude Measu...

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作者： Yu, Jing Ding, Feng Zhao, Xin Zhou, Fang Information System Important Laboratory 28th Research Institute of China Electronics Technology Group Corporation Nanjing Jiangsu China

ISBN: (纸本)9781538636749

In view that the requirements of miniaturization, low power consumption and low cost for the navigation system, attitude measurement using magnetometer could well meet the above requirements. However, the measurement accuracy of geomagnetic field is severely affected by the external magnetic field, which would increase attitude calculating error. Therefore, it is important to do error compensation for the output of magnetometer. In this work, a novel error compensation method of magnetometer for attitude measurement using modified artificial bee colony(ABC) is proposed. After analyzing the error sources of the sensor an equivalent error model is established. ABC is used to estimate the compensation parameters of the error model. But it’s computing speed is too slow because of the initial food source is randomly generated and not reasonable. Then, a hybrid algorithm for obtaining the compensation parameters is designed, in which the recursive least square algorithm is taken to adjust the initial value of ABC Comparing with the traditional parameter estimation algorithm, the modified ABC has higher accuracy and faster efficiency. In the numerical simulation, the error model coefficients obtained by the modified algorithm are close to the theoretical coefficients. The results show that the magnetic field intensity after correction is basically consistent with the actual geomagnetic field intensity Finally, the modified error compensation method is used to correct the output of magnetometers. As a result, the pitch angle error is reduced from f6 ° to f0.8 °, the roll angle error is reduced from f15 °to f1 ° by the compensation. The measurement accuracy of attitude angle is improved nearly tenfold which can be used for conventional navigation system. © 2017 IEEE.

关键词： artificial bee colony algorithm Error compensation Magnetometer Parameter estimation

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artificial bee colony algorithm Based Automatic Generation Control in Two -Area Non-Reheat Thermal Power System using SMES

Artificial Bee Colony Algorithm Based Automatic Generation C...

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IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (IEEE ICPCSI)

作者： Shanmugasundaram, V Sona Coll Technol EEE Dept Salem Tamil Nadu India

ISBN: (纸本)9781538608142

In modern real world power system is very complex and uncertainties of parameters are controlled is a huge task of the problem due to continuous power demand variations. The proper balance between power demand and generation is much essential of the system. Automatic generation control (AGC) is a vital role to maintain the steady state frequency and tie-line power interchange values under normal and step load perturbations in the system. In this proposed work to design a optimal controllers for AGC to improve the dynamic performance and reliability of the system. The optimum gains of the proportional-integral-derivative (PID) controller are optimized using a artificial bee colony (ABC) algorithm employing an integral time absolute error (ITAE) criterion. Superconducting magnetic energy storage system (SMES) unit is incorporated in the proposed system to further improve the dynamic performance. The superiority of this proposed approach is compared with existing published method such as Loci map based chaotic optimization algorithm (LOCA) tuned PID controller for the system of two area non- reheat thermal power system. It is observed that the ABC tuned PID controller is better performers in terms of settling time, peak overshoot, undershoot in frequency and tie line power deviation as compared to the LOCA based PID controller.

关键词： Automatic generation control superconducting magnetic energy storage artificial bee colony algorithm Area control error PID controller

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