scholarly journals Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

2019 ◽  
Vol 9 (9) ◽  
pp. 1930 ◽  
Author(s):  
Sibel Arslan ◽  
Celal Ozturk
Keyword(s):  
Artificial Bee Colony ◽  
Local Binary Pattern ◽  
Texture Classification ◽  
Classification Problem ◽  
Automatic Programming ◽  
Abc Algorithm ◽  
Machine Learning Methods ◽  
Bee Colony ◽  
High Level

Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.

Functional Link Neural Network with Modified Artificial Bee Colony for Data Classification

2017 ◽  
Vol 13 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Tutut Herawan ◽  
Yana Mazwin Mohmad Hassim ◽  
Rozaida Ghazali
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Artificial Bee Colony ◽  
Classification Problem ◽  
Functional Link ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Learning Scheme ◽  
Feed Forward Network ◽  
Functional Link Neural Network

Functional Link Neural Network (FLNN) has emerged as an important tool for solving non-linear classification problem and has been successfully applied in many engineering and scientific problems. The FLNN structure is much more modest than ordinary feed forward network like the Multilayer Perceptron (MLP) due to its flat network architecture which employs less tuneable weights for training. However, the standard Backpropagation (BP) learning uses for FLNN training prone to get trap in local minima which affect the FLNN classification performance. To recover the BP-learning drawback, this paper proposes an Artificial Bee Colony (ABC) optimization with modification on bee foraging behaviour (mABC) as an alternative learning scheme for FLNN. This is motivated by good exploration and exploitation capabilities of searching optimal weight parameters exhibit by ABC algorithm. The result of the classification accuracy made by FLNN with mABC (FLNN-mABC) is compared with the original FLNN architecture with standard Backpropagation (BP) (FLNN-BP) and standard ABC algorithm (FLNN-ABC). The FLNN-mABC algorithm provides better learning scheme for the FLNN network with average overall improvement of 4.29% as compared to FLNN-BP and FLNN-ABC.

scholarly journals CLASSIFICATION OF KIDNEY DISEASE USING GENETIC MODIFIED KNN AND ARTIFICIAL BEE COLONY ALGORITHM

SINERGI ◽  
2021 ◽  
Vol 25 (2) ◽  
pp. 177
Author(s):  
Ardina Ariani ◽  
Samsuryadi Samsuryadi
Keyword(s):  
Chronic Kidney Disease ◽  
Feature Selection ◽  
Kidney Disease ◽  
Artificial Bee Colony ◽  
Nearest Neighbor ◽  
Kidney Diseases ◽  
K Nearest Neighbor ◽  
Abc Algorithm ◽  
Bee Colony

The health care system is currently improving with the development of intelligent artificial systems in detecting diseases. Early detection of kidney disease is essential by recognizing symptoms to prevent more severe damages. This study introduces a classification system for kidney diseases using the Artificial Bee Colony (ABC) algorithm and genetically modified K-Nearest Neighbor (KNN). ABC algorithm is used as a feature selection to determine relevant symptoms used in influencing kidney disease and Genetic modified KNN used for classification. This research consists of 3 stages: pre-processing, feature selection, and classification. However, it focuses on the pre-processing stage of chronic kidney disease using 400 records with 24 attributes for the feature selection and classification. Kidney disease data is classified into two classes, namely chronic kidney disease and not chronic kidney disease. Furthermore, the performance of the proposed method is compared with other methods. The result showed that an accuracy of 98.27% was obtained by dividing the dataset into 280 training and 120 test data.

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.

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