Diversity Conserved Chaotic Artificial Bee Colony Algorithm based Brightness Preserved Histogram Equalization and Contrast Stretching Method

2015 ◽  
Vol 5 (4) ◽  
pp. 45-73 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Sanjoy Das
Keyword(s):  
Artificial Bee Colony ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Population Diversity ◽  
Objective Functions ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Optimal Values ◽  
Occurrence Matrix ◽  
Contrast Stretching

This study is organized into two parts. The first part introduces two image enhancement methods with the ability to preserve the original brightness of the image. These two methods are: optimal ranged brightness preserved contrast stretching (ORBPCS) method and weighted thresholded histogram equalization (WTHE) method. The efficiency of these two methods crucially depends on the method's associated parameters. To find the optimal values of the parameters Artificial Bee Colony (ABC) algorithm and a novel objective function have been employed in this study. The second part of this study mainly concentrates on the efficiency increment of ABC algorithm and to develop the proper objective functions to preserve the original brightness of the image. Some new mechanisms like population diversity measurement technique, use of chaotic sequence etc. are also introduced to enhance the efficiency of traditional ABC algorithm. The objective functions have been developed by using co-occurrence matrix and peak-signal to noise ratio (PSNR).

An Improved Cuckoo Search based Optimal Ranged Brightness Preserved Histogram Equalization and Contrast Stretching Method

2017 ◽  
Vol 8 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Md. Iqbal Quraishi ◽  
Sanjoy Das
Keyword(s):  
Objective Function ◽  
Optimization Problems ◽  
Signal To Noise Ratio ◽  
Cuckoo Search ◽  
Histogram Equalization ◽  
Chaotic Sequence ◽  
Population Diversity ◽  
Optimal Range ◽  
Novel Variant ◽  
Contrast Stretching

This paper is organized into two main parts. In the first part, two methods have been discussed to preserve the original brightness of the image which are Parameterized transformation function and a novel variant of modified Histogram Equalization (HE) method. In this study both methods have been formulated as optimization problems to increase the efficiency of the corresponding methods within reasonable time. In the second part, a novel modified version of Cuckoo Search (CS) algorithm has been devised by using chaotic sequence, population diversity information etc to solve those formulated optimization problems. A new Co-occurrence matrix's features based objective function is also devised to preserve the original brightness. Peak-signal to noise ratio (PSNR) acts as objective function to find optimal range of enhanced images. Experimental results prove the supremacy of the proposed CS over traditional CS algorithm.

scholarly journals A Modified Artificial Bee Colony Algorithm Based on Search Space Division and Disruptive Selection Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Zhen-an He ◽  
Caiwen Ma ◽  
Xianhong Wang ◽  
Lei Li ◽  
Ying Wang ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Search Space ◽  
Poor Quality ◽  
Population Diversity ◽  
Disruptive Selection ◽  
Selection Strategy ◽  
Abc Algorithm ◽  
Space Division ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has attracted much attention and has been applied to many scientific and engineering applications in recent years. However, there are still some insufficiencies in ABC algorithm such as poor quality of initial solution, slow convergence, premature, and low precision, which hamper the further development and application of ABC. In order to further improve the performance of ABC, we first proposed a novel initialization method called search space division (SSD), which provided high quality of initial solutions. And then, a disruptive selection strategy was used to improve population diversity. Moreover, in order to accelerate convergence rate, we changed the definition of the scout bee phase. In addition, we designed two types of experiments to testify our proposed algorithm. On the one hand, we conducted experiments to make sure how much each modification makes contribution to improving the performance of ABC. On the other hand, comprehensive experiments were performed to prove the superiority of our proposed algorithm. The experimental results indicate that SDABC significantly outperforms other ABCs, contributing to higher solution accuracy, faster convergence speed, and stronger algorithm stability.

scholarly journals A New Emigrant Creation Strategy with Randomized Sources for Parallel Artificial Bee Colony Algorithm

2018 ◽  
pp. 81-86
Author(s):  
Selcuk Aslan ◽  
Dervis Karaboga ◽  
Alperen Aksoy
Keyword(s):  
Artificial Bee Colony ◽  
Parallel Implementation ◽  
Experimental Studies ◽  
Population Diversity ◽  
Computational Power ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Convergence Performance ◽  
Neighborhood Topology ◽  
Current Systems

Dividing the whole population into subpopulations or subcolonies then evaluating them simultaneously is one of the most commonly used parallelization approaches to utilize the computational power of the current systems. However, this type of parallelization strategy decreases the population diversity because of the division of the entire population and needs migrations between subpopulations to maintain the solution diversity until the end of the iterations. In this study, we proposed a new emigrant creation strategy in which the parameters of the best food source being migrated to the neighbor subpopulation is modified with the more appropriate parameters of the randomly determined solution or solutions and investigated its effect on the performance of the parallel Artificial Bee Colony (ABC) algorithm. Experimental studies showed that newly proposed emigrant creation strategy based on randomized solutions significantly improved the convergence performance and solution qualities of parallel ABC algorithm compared to the its standard serial and ring neighborhood topology based parallel implementation for which the best solutions are directly used as emigrants.

Multi-Thresholded Histogram Equalization Based on Parameterless Artificial Bee Colony

2018 ◽  
pp. 108-126 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Mandira Sen ◽  
Swarnajit Ray ◽  
Sanjoy Das
Keyword(s):  
Artificial Bee Colony ◽  
Histogram Equalization ◽  
Consumer Electronics ◽  
Experimental Results ◽  
Computational Time ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Histopathological Image ◽  
Novel Variant ◽  
Multi Level

This chapter presents a novel variant of histogram equalization (HE) method called multi-thresholded histogram equalization (MTHE), depending on entropy-based multi-level thresholding-based segmentation. It is reported that proper segmentation of the histogram significantly assists the HE variants to maintain the original brightness of the image, which is one of the main criterion of the consumer electronics field. Multi-separation-based HE variants are also very effective for multi-modal histogram-based images. But, proper multi-seaparation of the histogram increases the computational time of the corresponding HE variants. In order to overcome that problem, one novel parameterless artifical bee colony (ABC) algorithm is employed to solve the multi-level thresholding problem. Experimental results prove that proposed parameterless ABC helps to reduce the computational time significantly and the proposed MTHE outperforms several existing HE varints in brightness preserving histopathological image enhancement domain.

Design optimization of rotary regenerator using artificial bee colony algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1088-1098 ◽  
Author(s):  
R V Rao ◽  
V Patel
Keyword(s):  
Design Optimization ◽  
Artificial Bee Colony ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Design Variables ◽  
Fitness Value ◽  
Single Objective

This study explores the use of artificial bee colony (ABC) algorithm for the design optimization of rotary regenerator. Maximization of regenerator effectiveness and minimization of regenerator pressure drop are considered as objective functions and are treated individually and then simultaneously for single-objective and multi-objective optimization, respectively. Seven design variables such as regenerator frontal area, matrix rotational speed, matrix rod diameter, matrix thickness, porosity, and split are considered for optimization. A case study is also presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using ABC algorithm are validated by comparing with those obtained using genetic algorithm for the same case study. The effect of variation of ABC algorithm parameters on convergence and fitness value of the objective function has also been presented.

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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