Improved Artificial Bee Colony Algorithm and Its Application to Fundus Retinal Blood Vessel Image Binarization

Pan, Xiuqin; Zhang, Qinrui; Pan, Haichuan

doi:10.1109/access.2020.3001299

Cited by 16 publications

(10 citation statements)

References 18 publications

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“…Artificial bee colony algorithm [17] is a swarm intelligent optimization algorithm. According to the ABC algorithm, which models the swarm intelligence formed by the interaction of bees in a hive, there are three types of bees in the hive: employed bees, onlooker bees and scout bees, each responsible for only one stage.…”

Section: Principle Of Babc Algorithmmentioning

confidence: 99%

Blind recognition of RSC based on binary artificial bee colony algorithm

WANG¹,

Zhang²,

Wang³

et al. 2023

Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022)

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In order to overcome the shortcomings of poor fault tolerance and complicated computation in blind identification of recursive systematic convolutional (RSC) codes, a blind identification method based on binary artificial bee colony algorithm is proposed. Firstly, according to the structure of RSC encoder, the product vector of output bit matrix and the coded polynomial vector is obtained. Then, the artificial bee colony algorithm for continuous decision variables is modified to be suitable for binary optimization problems, and the sum of the elements of the product vector is used as the fitness function of the binary artificial bee colony algorithm to optimize the coefficient vector of coded polynomial. Finally, the termination conditions of the algorithm are set according to the fitness value and the number of iterations. Furthermore, the termination condition of the algorithm is improved according to the characteristics of the coding polynomial. Thus, the RSC coded polynomial can be identified while the computation is reduced. The experimental results show that the algorithm can recognize RSC coded polynomials effectively, and the correct rate of recognition is over 98% for coded polynomials with different coding constraints when the bit error rate reaches 0.05.

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Section: Principle Of Babc Algorithmmentioning

confidence: 99%

Blind recognition of RSC based on binary artificial bee colony algorithm

WANG¹,

Zhang²,

Wang³

et al. 2023

Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022)

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“…Famila [24] et al proposed an improved artificial bee colony optimization clustering algorithm combining the advantages of the grenade explosion method and the Cauchy operator, which significantly improved the exploitation and exploration capabilities of the algorithm. Pan [25] et al combined the K-MEANS method in the initialization phase to generate the initial solution. They also proposed a dynamic neighborhood search mechanism based on the number of iterations, which improved the algorithm's local optimization capability and convergence speed.…”

Section: Existing Of Artificial Bee Colony Algorithm and Its Applicationmentioning

confidence: 99%

Numerical function optimization of improved artificial bee colony algorithm and its application in finite element model modification

Tang

Yue

2022

Preprint

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An improved artificial bee colony algorithm is proposed in this paper to improve the problems of strong exploration ability, weak exploitation ability and being easy to fall into local optimum of the basic artificial bee colony algorithm. The quality of the initial solution is improved by adopting the principle of equally spaced random distribution in the initialization phase. In the stage of employed bee and onlooker bee, it introduces the two-way search strategy and the adaptive decision factor γ, which also changes the search step length at the same time to balance algorithm exploration and exploitation capabilities by replacing the original search strategy. fifteen benchmark test functions were used to verify the optimal search ability of the improved artificial bee colony algorithm, and the improved algorithm was successfully applied to the finite element model modification. The results show that the improved algorithm can effectively improve and balance the exploitation and exploration ability of the algorithm, and show excellent global search ability. At the same time, the improved algorithm has higher correction efficiency and better applicability and practicability in the finite element model modification.

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“…The framework finds the relationship between the two vectors and based on the comparability esteem, classifies the cell [12][13] . The variety and quality of the solution change the strategy of honey source [14] . The initialization utilizes the cluster center made by the K-Means strategy as the underlying honey source rather than the initialization in the standard method.…”

Section: Introductionmentioning

confidence: 99%

An enhanced artificial bee colony algorithm using radial bases function neural networks for color image segmentation

Shunmuganathan¹,

Sheshathri²,

Sankarasubramanian³

2022

ijhs

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The aim of segmentation process is to partition the image into homogeneous regions or objects. The proposed segmentation algorithms using adaptive feature weighting to distinguish the significance of various features. To extract effective feature extraction could be used for establishing regions in region based image segmentation. The main goal of this exploration work is to segment objects that separating the foreground and background of the color images. The segmentation for color images is focused on the Enhanced Artificial Bee Colony segmentation algorithm that utilizes the Radial Basis Function (EABCRBF) neural network algorithm. This work arranges RBF neural networks to predict the end limits of the segmentation, which are formed from the artificial bee colony province made in the image histogram topography. The radial basis function initial parameters, for instance, centers and widths are automatically set upon the histogram peaks and minima. The proposed EABCRBF strategy is compared with two existing techniques of Artificial Bee Colony (ABC) and Fuzzy C-Means (FCM). The EABCRBF method gives great outcomes for color image segmentation as it extracts additional information from color images. The experimentation results are shown through Mat Lab R2013a.

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Improved Artificial Bee Colony Algorithm and Its Application to Fundus Retinal Blood Vessel Image Binarization

Cited by 16 publications

References 18 publications

Blind recognition of RSC based on binary artificial bee colony algorithm

Blind recognition of RSC based on binary artificial bee colony algorithm

Numerical function optimization of improved artificial bee colony algorithm and its application in finite element model modification

An enhanced artificial bee colony algorithm using radial bases function neural networks for color image segmentation

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