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Optimal design of damping controllers using a new hybrid artificial bee colony algorithm

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期52卷 42-54页

作者： Eslami, Mandiyeh Shareef, Hussain Khajehzadeh, Mohammad Natl Univ Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Islamic Azad Univ Anar Branch Dept Civil Engn Anar Iran

This paper integrates the artificial bee colony (ABC) algorithm with the sequential quadratic programming (SQP) to create the new hybrid optimization algorithm, ABC-SQP, for solving global optimization problems and damping of low frequency oscillations in power system stability analyses. The new algorithm combines the global exploration ability of ABC to converge rapidly to a near optimum solution and the accurate local exploitation ability of SQP to accelerate the search process and find an accurate solution. A set of well-known benchmark optimization problems is used to validate the performance of the ABC-SQP as a global optimization algorithm and to facilitate a comparison with the classical ABC. Numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions. Power system stabilizers and supplementary static VAR compensator controllers are designed for two-area-four-machine and five-area-sixteen-machine systems to illustrate the feasibility and effectiveness of the new method in power systems. The performance of the proposed ABC-SQP algorithm is compared with the classic ABC and the genetic algorithm (GA) through eigenvalue analysis and nonlinear time-domain simulation. The simulation results indicate that the controllers designed by the ABC-SQP perform better than those designed by ABC and GA. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Power system stabilizer Static VAR compensator Low-frequency oscillation Hybrid algorithm artificial bee colony algorithm Sequential quadratic programming

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Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks

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WIRELESS NETWORKS 2022年第4期28卷 1671-1688页

作者： Lu Sixu Wu Muqing Zhao Min Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Networks Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China

With the development of the internet of things, people pay more and more attention to wireless sensor networks. Designing the energy efficient routing is an essential objective for wireless sensor networks. Cluster routing is one of the most popular routing protocols to enhance the network lifetime. However, hotspot problem always exists in cluster-based routing protocol. The task of this study is designing a cluster routing protocol with mobile base station which aims at balancing the energy consumption and prolonging the network lifetime. In this article, we design a particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks. The software defined network architecture is used to reduce the energy overhead and computation overhead in sensor nodes. Particle swarm optimization-based cluster routing algorithm is used to calculate the cluster heads and the sojourn locations of base station. artificial bee colony algorithm-based traversal path algorithm is used to design the move path of the base station. Comparing with relevant protocols, the proposed protocol reduces the energy consumption, enhances the network lifetime and reduces the control overhead.

关键词： Software-defined wireless sensor networks Cluster routing Mobile base station Particle swarm optimization artificial bee colony algorithm

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Reduction of artificial bee colony algorithm for global optimization

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NEUROCOMPUTING 2015年 148卷 70-74页

作者： Maeda, Michiharu Tsuda, Shinya Fukuoka Inst Technol Higashi Ku Fukuoka Japan

This paper presents a reduction of artificial bee colony algorithm for global optimization. artificial bee colony algorithm is an optimization technique which refers to the behavior of honeybee swarms, and a multi-point search approach which finds a best solution using multiple bees. For avoiding local minima, a number of bees are initially prepared and their positions are updated by artificial bee colony algorithm. bees sequentially reduce to reach a predetermined number of them grounded in the evaluation value and artificial bee colony algorithm continues until the termination condition is met. In order to show the effectiveness of the proposed algorithm, we examine the best value by using test functions compared to existing algorithms. Furthermore the influence of best value on the initial number of bees for our algorithm is discussed. (C) 2014 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Global optimization Metaheuristics Reduction

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Research and implementation of parallel artificial bee colony algorithm based on ternary optical computer

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AUTOMATIKA 2019年第4期60卷 422-431页

作者： Li, Shuang Li, Wenjing Zhang, Honghong Wang, Zhehe Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Nanning Normal Univ Sch Logist Management & Engn Nanning Peoples R China

The artificial bee colony (ABC) algorithm is a widely used algorithm in the field of function optimization problems. The traditional ABC algorithm has long search time, slow convergence speed and easy to fall into local optimum at the end of the search. In this paper, the design scheme and method of implementing parallel ABC algorithm are studied, which makes use of the characteristics of many data bits and easy expansion of data bits of the ternary optical computer (TOC). First, by analysing the traditional ABC algorithm, we can find the parallel parts and parallel design. Then the detailed algorithm implementation flow is given and the clock cycle of the algorithm is analysed. Finally, the correctness of the parallel scheme is verified by experiments. Compared with the ABC algorithm and parallel ABC algorithms based on computer (PABC), the ABC algorithm based on TOC (TOC-PABC) effectively shortens the search time, improves the optimization performance of complex multimodal function optimization problems and obtains a higher speedup.

关键词： Ternary optical computer parallel computing swarm intelligence artificial bee colony algorithm

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Implementation of robust estimation in GPS networks using the artificial bee colony algorithm

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EARTH SCIENCE INFORMATICS 2014年第1期7卷 39-46页

作者： Yetkin, Mevlut Berber, Mustafa Izmir Katip Celebi Univ Dept Geomat Engn Fac Engn & Architecture TR-35620 Cigli Izmir Turkey Florida Atlantic Univ Dept Civil Environm & Geomat Engn Boca Raton FL 33431 USA

Geodesy utilizes state of the art data collection techniques such as GPS (Global Positioning System) to acquire locations of points. Traditionally, the coordinates of these points are estimated using the Least Squares (LS) method. Nevertheless, Robust Estimation (RE) yields more accurate results than LS method in the presence of blunders (gross errors) among the data set. For example, the Least Trimmed Squares (LTS) method and the Least Median Squares (LMS) method can be used for this purpose. The first method aims to minimize the sum of the squared residuals by trimming away observations with large residuals. On the other hand, the second method involves the minimization of the median of the squared residuals. Both methods can be implemented using an optimization method, i.e., artificial bee colony (ABC) algorithm. The ABC algorithm is a swarm intelligence (a branch of artificial intelligence) technique that can be used for the solution of minimization or maximization problems. In this paper, using the LTS and LMS methods for GPS data by employing the ABC, a new approach is put forward. Firstly, some discussions about the theoretical principals of RE and ABC are given. Then, a numerical example is used to demonstrate the validity of the proposed approach. Numerical results show that application of the robust estimation to GPS data can easily be carried out by ABC and this approach helps to enhance the reliability of geospatial data for any application of geodesy.

关键词： Metaheuristics GPS Optimization artificial bee colony algorithm Robust estimation

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Characteristic Analysis of artificial bee colony algorithm with Network-Structure

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2017年第3期21卷 496-506页

作者： Imamura, Shunta Kaihara, Toshiya Fujii, Nobutada Kokuryo, Daisuke Kitamura, Akira Kobe Univ Grad Sch Syst Informat Nada Ku 1-1 Rokkodai Cho Kobe Hyogo 6578501 Japan Tottori Univ Grad Sch Engn Koyama Tottori 6808552 Japan

The 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 an efficient exploration of the optimal solutions using three different types of agents for optimization problems with multimodal functions. However, the performance of the conventional ABC algorithm decreases for high-dimensional problems. In this study, we propose an improved algorithm using the network structure of agents to enhance the ability for global search. The efficacy of the proposed algorithm is evaluated by performing computer experiments with high-dimensional benchmark functions.

关键词： artificial bee colony algorithm network-structure swarm intelligence high-dimensional problem

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Improved clustering criterion for image clustering with artificial bee colony algorithm

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PATTERN ANALYSIS AND APPLICATIONS 2015年第3期18卷 587-599页

作者： Ozturk, Celal Hancer, Emrah Karaboga, Dervis Erciyes Univ Fac Engn Dept Comp Engn Kayseri Turkey

In this paper, a new objective function is proposed for image clustering and is applied with the artificial bee colony (ABC) algorithm, the particle swarm optimization algorithm and the genetic algorithm. The performance of the proposed objective function is tested on seven benchmark images by comparing it with the three well-known objective functions in the literature and the K-means algorithm in terms of separateness and compactness which are the main criterions of the clustering problem. Moreover, the Davies-Bouldin Index and the XB Index are also employed to compare the quality of the proposed objective function with the other objective functions. The simulated results show that the ABC-based image clustering method with the improved objective function obtains well-distinguished clusters.

关键词： Image clustering artificial bee colony algorithm Genetic algorithms K-means Particle swarm optimization Validity indexes

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DQB: A novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles

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SAUDI JOURNAL OF BIOLOGICAL SCIENCES 2018年第5期25卷 932-946页

作者： Alshamlan, Hala M. King Saud Univ Informat Technol Dept Riyadh Saudi Arabia MIT Dept Mech Engn Cambridge MA 02139 USA

In the medical domain, it is very significant to develop a rule-based classification model. This is because it has the ability to produce a comprehensible and understandable model that accounts for the predictions. Moreover, it is desirable to know not only the classification decisions but also what leads to these decisions. In this paper, we propose a novel dynamic quantitative rule-based classification model, namely DQB, which integrates quantitative association rule mining and the artificial bee colony (ABC) algorithm to provide users with more convenience in terms of understandability and interpretability via an accurate class quantitative association rule-based classifier model. As far as we know, this is the first attempt to apply the ABC algorithm in mining for quantitative rule-based classifier models. In addition, this is the first attempt to use quantitative rule-based classification models for classifying microarray gene expression profiles. Also, in this research we developed a new dynamic local search strategy named DLS, which is improved the local search for artificial bee colony (ABC) algorithm. The performance of the proposed model has been compared with well-known quantitative-based classification methods and bioinspired meta-heuristic classification algorithms, using six gene expression profiles for binary and multi-class cancer datasets. From the results, it can be concludes that a considerable increase in classification accuracy is obtained for the DQB when compared to other available algorithms in the literature, and it is able to provide an interpretable model for biologists. This confirms the significance of the proposed algorithm in the constructing a classifier rule-based model, and accordingly proofs that these rules obtain a highly qualified and meaningful knowledge extracted from the training set, where all subset of quantitive rules report close to 100% classification accuracy with a minimum number of genes. It is remarkable that apparen

关键词： Classification rule artificial bee colony algorithm Quantitive rule-based classification model Microarray Gene expression profile ABC Cancer gene selection

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Optimizing the material and printing parameters of the additively manufactured fiber-reinforced polymer composites using an artificial neural network model and artificial bee colony algorithm

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STRUCTURES 2022年 46卷 1781-1795页

作者： Alhaddad, Wael He, Minjuan Halabi, Yahia Almajhali, Khalil Yahya Mohammed Tongji Univ Dept Struct Engn Shanghai 200092 Peoples R China Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Sichuan Peoples R China

Fiber-reinforced polymer composite (FRP) has always been attracting the attention of researchers due to its multiple applications as a primary material for manufacturing structural elements and parts or as a reinforcing material for the structural elements. Controlling the quality of the additively manufactured FRP is critical to guarantee an FRP material of high performance for different applications. Therefore, in this research, a machine learning model based on data collected from experimental studies was developed using the artificial neural network (ANN) modeling method to control the quality of FRP manufactured by fused deposition modeling (FDM) printing technology. ANN model predicts the tensile properties, particularly ultimate tensile strength (UTS), of the FRP as a function of 7 material and printing parameters, which are fiber tensile strength, polymer tensile strength, layer thickness, fiber volume fraction, fiber form, infill pattern, and fiber orientation. A parametric study was conducted to confirm the credibility of the developed ANN model in representing the pairwise relationship between each input parameter and the predicted UTS. The UTS of the FRP was maximized by optimizing the printing and material parameters by using the artificial bee colony (ABC) algorithm. Several case studies were conducted to prove the efficiency of the coupled ANN model and optimization algorithm. The ANN modeling method and ABC algorithm were codified and applied using MATLAB software. The results showed that the developed ANN model could accurately predict the UTS of FRP. The parametric study confirmed that the developed ANN model was valid for representing the pairwise relationships between the input parameters and the predicted UTS. Also, it was found that the fiber properties, fiber form, fiber orientation, and fiber volume fraction have the highest influence on the UTS of FRP manufactured by FDM compared with other parameters. Furthermore, the ABC optimization algor

关键词： Fiber-reinforced polymer 3D printing artificial neural network Optimization artificial bee colony algorithm

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Structural, magnetic and electronic properties of CunNi55-n (n=0-55) nanoparticles: Combination artificial bee colony algorithm with DFT

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COMPUTATIONAL AND THEORETICAL CHEMISTRY 2019年 1154卷 11-16页

作者： Sun, Yan Liu, Shanggou Guo, Xiangyu Huang, Shiping Beijing Univ Chem Technol State Key Lab Organ Inorgan Composites Beijing 100029 Peoples R China

The structures of CunNi55-n (n = 0-55) nanoparticles are generated by artificial bee colony algorithm, and the structural, magnetic and electronic properties of the CunNi55-n nanoparticles are performed by the density functional theory calculation. The calculation results show that the core-shell structure Ca42Ni13 is the most stable structure of all CunNi55-n NPs, besides, the Cu atoms prefer to appear on the surface and the Ni atoms prefer to occupy the interior place. As for magnetism, the total magnetic moment decreases with the increase of Cu atom. The results are shown that the average Bader charge of Ni atom and the d-band center of Ni atom have a positive correlation with the excess energy. On the contrary, the d-band center of Cu atom has a negative correlation with the excess energy.

关键词： artificial bee colony algorithm Density functional theory CunNi55-n nanoparticles d-band center

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