Artificial bee colony algorithm for inverter complex wave reduction under line-load variations

2017 ◽  
Vol 40 (5) ◽  
pp. 1593-1607 ◽  
Author(s):  
NM Spencer Prathap Singh ◽  
N Kesavan Nair
Keyword(s):  
Artificial Bee Colony ◽  
Single Phase ◽  
Fuzzy Logic Controller ◽  
Sine Wave ◽  
Pi Controller ◽  
Voltage Source ◽  
Line Load ◽  
Abc Algorithm ◽  
Load Variations ◽  
Bee Colony

This paper describes the minimization of total harmonics in a single-phase sine wave voltage source inverter using a proportional–integral (PI) controller by estimating the optimized values of PI constants using an artificial bee colony (ABC) algorithm under line-load variations. The single-phase inverter is a non-linear load using power electronic components causing distortions in the load voltage and current wave patterns from the sinusoidal waveforms due to harmonics. A state variable analysis for the single-input–single-output (SISO) model of an inverter was developed by considering the switching sequence of a voltage source full bridge sine wave inverter. The ABC algorithm calculates the optimized values of the constants for the PI controller, thereby tuning the controller for reducing the total harmonics of an inverter. The MATLAB/Simulink tool and an experimental set-up were implemented, and their total harmonic distortion (THD) values were estimated. The outcomes of the proposed ABC scheme were compared with the previous results such as the PI algorithm, fuzzy logic controller, neuro-fuzzy controller, particle swarm optimization (PSO) and the bat algorithm. A practical example of the ABC algorithm is considered in the present harmonic reduction problem. From the simulation and experimental results using the ABC algorithm, it was observed that its harmonics levels were reduced considerably compared with IEEE and IEC standards.

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.

A quasi-affine transformation artificial bee colony algorithm for global optimization

2021 ◽  
pp. 1-18
Author(s):  
Baohua Zhao ◽  
Tien-Wen Sung ◽  
Xin Zhang
Keyword(s):  
Affine Transformation ◽  
Artificial Bee Colony ◽  
Triangular Matrix ◽  
Convergence Speed ◽  
Test Set ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Search Mechanism ◽  
Multiple Dimensions ◽  
Collaborative Search

The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updating, the convergence speed is accelerated by updating multiple dimensions instead of one dimension. Finally, in the overall search framework, a collaborative search matrix is introduced to update the position of particles. The collaborative search matrix is transformed from the lower triangular matrix, which not only ensures the randomness of the search, but also ensures its balance and integrity. To evaluate the performance of the QABC algorithm, CEC2013 test set and CEC2014 test set are used in the experiment. After comparing with the conventional ABC algorithm and some famous ABC variants, QABC algorithm is proved to be superior in efficiency, development ability, and robustness.

scholarly journals A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1211
Author(s):  
Ivona Brajević
Keyword(s):  
Integer Programming ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Minimax Problems ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Effective Performance ◽  
Complex Optimization ◽  
Overall Performance ◽  
Integer Programming Problems

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems since its solution search equation is more of an exploration than exploitation operator. This paper presents an improved ABC algorithm for solving integer programming and minimax problems. The proposed approach employs a modified ABC search operator, which exploits the useful information of the current best solution in the onlooker phase with the intention of improving its exploitation tendency. Furthermore, the shuffle mutation operator is applied to the created solutions in both bee phases to help the search achieve a better balance between the global exploration and local exploitation abilities and to provide a valuable convergence speed. The experimental results, obtained by testing on seven integer programming problems and ten minimax problems, show that the overall performance of the proposed approach is superior to the ABC. Additionally, it obtains competitive results compared with other state-of-the-art algorithms.

scholarly journals Sizing and implementation of Photovoltaic Water Pumping System for Irrigation

2018 ◽  
Vol 7 (1) ◽  
pp. 54
Author(s):  
Santosh S. Raghuwanshi ◽  
Vikas Khare
Keyword(s):  
Fuzzy Logic Controller ◽  
Setting Time ◽  
Weather Conditions ◽  
Pi Controller ◽  
Voltage Source ◽  
Solar Photovoltaic ◽  
Pump System ◽  
Pumping System ◽  
Water Pumping ◽  
Perturb And Observe

<p>Solar photovoltaic systems convert energy of light directly into electrical<br />energy. This work presents, a process to compute the required size of the<br />stand-alone solar photovoltaic generator based water pumping system<br />for an existing area. In addition solar photovoltaic generator is<br />connecting voltage source inverter fed vector controlled induction<br />motor-pump system. Perturb and observe are used for harvesting<br />maximum power of PV generator in between buck-boost DC converter<br />and inverter system. In this paper system result is validated by fuzzy<br />logic system and compare with variable frequency drives based PI<br />controllers, driving motor-pump system. The operational performance<br />at 60 m head, VFD based controllers in terms overshoot and setting time<br />and also analysis performance of motor-pump set under different<br />weather conditions. By assessment of system we find that speed and<br />torque variation, overshoot and settling time is more with PI controller,<br />Fuzzy logic controller (FLC) performance have dominance to VFD<br />based PI controller.</p>

scholarly journals MODIFIKASI BARU ALGORITMA KOLONI LEBAH BUATAN UNTUK MASALAH OPTIMASI GLOBAL

2018 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Nursyiva Irsalinda ◽  
Sugiyarto Surono
Keyword(s):  
Food Source ◽  
Artificial Bee Colony ◽  
Selection Process ◽  
Optimization Technique ◽  
Food Sources ◽  
Abc Algorithm ◽  
Average Value ◽  
Bee Colony ◽  
Objective Function Values ◽  
Better Than

Artificial Bee Colony (ABC) algorithm is one of metaheuristic optimization technique based on population. This algorithm mimicking honey bee swarm to find the best food source. ABC algorithm consist of four phases: initialization phase, employed bee phase, onlooker bee phase and scout bee phase. This study modify the onlooker bee phase in selection process to find the neighborhood food source. Not all food sources obtained are randomly sought the neighborhood as in ABC algorithm. Food sources are selected by comparing their objective function values. The food sources that have value lower than average value in that iteration will be chosen by onlooker bee to get the better food source. In this study the modification of this algorithm is called New Modification of Artificial Bee Colony Algorithm (MB-ABC). MB-ABC was applied to 4 Benchmark functions. The results show that MB-ABC algorithm better than ABC algorithm

scholarly journals Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 347 ◽  
Author(s):  
Mohanad Aljanabi ◽  
Yasa Özok ◽  
Javad Rahebi ◽  
Ahmad Abdullah
Keyword(s):  
Artificial Bee Colony ◽  
Survival Rates ◽  
High Specificity ◽  
Ground Truth ◽  
Threshold Value ◽  
Abc Algorithm ◽  
Melanoma Detection ◽  
Bee Colony ◽  
Average Accuracy ◽  
Different Sources

The occurrence rates of melanoma are rising rapidly, which are resulting in higher death rates. However, if the melanoma is diagnosed in Phase I, the survival rates increase. The segmentation of the melanoma is one of the largest tasks to undertake and achieve when considering both beneath and over the segmentation. In this work, a new approach based on the artificial bee colony (ABC) algorithm is proposed for the detection of melanoma from digital images. This method is simple, fast, flexible, and requires fewer parameters compared with other algorithms. The proposed approach is applied on the PH2, ISBI 2016 challenge, the ISBI 2017 challenge, and Dermis datasets. These bases contained images are affected by different abnormalities. The formation of the databases consists of images collected from different sources; they are bases with different types of resolution, lighting, etc., so in the first step, the noise was removed from the images by using morphological filtering. In the next step, the ABC algorithm is used to find the optimum threshold value for the melanoma detection. The proposed approach achieved good results in the conditions of high specificity. The experimental results suggest that the proposed method accomplished higher performance compared to the ground truth images supported by a Dermatologist. For the melanoma detection, the method achieved an average accuracy and Jaccard’s coefficient in the range of 95.24–97.61%, and 83.56–85.25% in these four databases. To show the robustness of this work, the results were compared to existing methods in the literature for melanoma detection. High values for estimation performance confirmed that the proposed melanoma detection is better than other algorithms, which demonstrates the highly differential power of the newly introduced features.

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