scholarly journals The Performance of Binary Artificial Bee Colony (BABC) in Structure Selection of Polynomial NARX and NARMAX Models

2017 ◽  
Vol 7 (2) ◽  
pp. 373
Author(s):  
Azlee Zabidi ◽  
Nooritawati Md Tahir ◽  
Ihsan Mohd Yassin ◽  
Zairi Ismael Rizman
Keyword(s):  
Artificial Bee Colony ◽  
Structure Selection ◽  
Bee Colony ◽  
Selection Of

Deterministic Parameter Selection of Artificial Bee Colony Based on Diagonalization

2019 ◽  
pp. 85-95
Author(s):  
Marco Antonio Florenzano Mollinetti ◽  
Mario Tasso Ribeiro Serra Neto ◽  
Takahito Kuno
Keyword(s):  
Artificial Bee Colony ◽  
Parameter Selection ◽  
Bee Colony ◽  
Selection Of

Logistic step size based artificial bee colony algorithm for optimum induction motor speed control

2019 ◽  
Vol 12 ◽  
Author(s):  
Fani Bhushan Sharma ◽  
Shashi Raj Kapoor
Keyword(s):  
Induction Motor ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Pi Controller ◽  
Motor Speed ◽  
Step Size ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Pi Controllers ◽  
Selection Of

: The disruption in the parameters of induction motor (IM) are quite frequent. In case of substantial parameter disruption the IM becomes unreliable. To deal with this complication of parameter disruption the proportional integral (PI) controllers are utilized. The selection of PI controller parameters is process dependent and any inappropriate selection of controller setting may diverge to instability. In recent years swarm intelligence (SI) based algorithms are executing well to solve real-world optimization problems. Now a days, the tuning of PI controller parameters for IM is executed using a significant SI algorithm namely, artificial bee colony (ABC) algorithm. Further, the ABC algorithm is amended based on inspiration from mathematical logistic formula. The propound algorithm is titled as logistic ABC algorithm and is applied for PI controller tuning of IM. The outcomes reveals that the propound algorithm performs better as compared to other state- of-art strategies available in the literature.

scholarly journals Dynamic Relay Satellite Scheduling Based on ABC-TOPSIS Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Shufeng Zhuang ◽  
Zhendong Yin ◽  
Zhilu Wu ◽  
Xiaoguang Chen
Keyword(s):  
Artificial Bee Colony ◽  
Dynamic Scheduling ◽  
Research Field ◽  
Scheduling Problem ◽  
Ideal Solution ◽  
Bee Colony ◽  
Order Preference ◽  
Simulation Results ◽  
Satellite Scheduling ◽  
Selection Of

Tracking and Data Relay Satellite System (TDRSS) is a space-based telemetry, tracking, and command system, which represents a research field of the international communication. The issue of the dynamic relay satellite scheduling, which focuses on assigning time resource to user tasks, has been an important concern in the TDRSS system. In this paper, the focus of study is on the dynamic relay satellite scheduling, whose detailed process consists of two steps: the initial relay satellite scheduling and the selection of dynamic scheduling schemes. To solve the dynamic scheduling problem, a new scheduling algorithm ABC-TOPSIS is proposed, which combines artificial bee colony (ABC) and technique for order preference by similarity to ideal solution (TOPSIS). The artificial bee colony algorithm is performed to solve the initial relay satellite scheduling. In addition, the technique for order preference by similarity to ideal solution is adopted for the selection of dynamic scheduling schemes. Plenty of simulation results are presented. The simulation results demonstrate that the proposed method provides better performance in solving the dynamic relay satellite scheduling problem in the TDRSS system.

scholarly journals An Improved ABC Algorithm and Its Application in Bearing Fault Diagnosis with EEMD

Algorithms ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 72 ◽  
Author(s):  
Weijia Chen ◽  
Yancai Xiao
Keyword(s):  
Fault Diagnosis ◽  
Artificial Bee Colony ◽  
The Other ◽  
Abc Algorithm ◽  
Original Algorithm ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Bee Colony ◽  
Selection Of ◽  
Improved Algorithm

The Ensemble Empirical Mode Decomposition (EEMD) algorithm has been used in bearing fault diagnosis. In order to overcome the blindness in the selection of white noise amplitude coefficient e in EEMD, an improved artificial bee colony algorithm (IABC) is proposed to obtain it adaptively, which providing a new idea for the selection of EEMD parameters. In the improved algorithm, chaos initialization is introduced in the artificial bee colony (ABC) algorithm to insure the diversity of the population and the ergodicity of the population search process. On the other hand, the collecting bees are divided into two parts in the improved algorithm, one part collects the optimal information of the region according to the original algorithm, the other does Levy flight around the current global best solution to improve its global search capabilities. Four standard test functions are used to show the superiority of the proposed method. The application of the IABC and EEMD algorithm in bearing fault diagnosis proves its effectiveness.

scholarly journals Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification

2019 ◽  
Vol 9 (6) ◽  
pp. 5253
Author(s):  
Fthi M. Albkosh ◽  
Muhammad Suzuri Hitam ◽  
Wan Nural Jawahir Hj Wan Yussof ◽  
Abdul Aziz K Abdul Hamid ◽  
Rozniza Ali
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Texture Classification ◽  
Optimization Technique ◽  
Image Texture ◽  
Discrete Wavelet ◽  
Bee Colony ◽  
Selection Of

Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform.  The multi-layered perceptron neural network is employed as an image texture classifier.  The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance.

scholarly journals The Dataset of the Experimental Evaluation of Software Components for Application Design Selection Directed by the Artificial Bee Colony Algorithm

Data ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 59
Author(s):  
Alexander Gusev ◽  
Dmitry Ilin ◽  
Evgeny Nikulchev
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Operating Conditions ◽  
Software Components ◽  
Bee Colony ◽  
Component Selection ◽  
Effective Selection ◽  
Intelligence Approach ◽  
Selection Of

The paper presents the swarm intelligence approach to the selection of a set of software components based on computational experiments simulating the desired operating conditions of the software system being developed. A mathematical model is constructed, aimed at the effective selection of components from the available alternative options using the artificial bee colony algorithm. The model and process of component selection are introduced and applied to the case of selecting Node.js components for the development of a digital platform. The aim of the development of the platform is to facilitate countrywide simultaneous online psychological surveys in schools in the conditions of unstable internet connection and the large variety of desktop and mobile client devices, running different operating systems and browsers. The module whose development is considered in the paper should provide functionality for the archiving and checksum verification of the survey forms and graphical data. With the swarm intelligence approach proposed in the paper, the effective set of components was identified through a directional search based on fuzzy assessment of the three experimental quality indicators. To simulate the desired operating conditions and to guarantee the reproducibility of the experiments, the virtual infrastructure was configured. The application of swarm intelligence led to reproducible results for component selection after 312 experiments instead of the 1080 experiments needed by the exhaustive search algorithm. The suggested approach can be widely used for the effective selection of software components for distributed systems operating in the given conditions at this stage of their development.

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