An Agent-Based Artificial Bee Colony (ABC) Algorithm for Hyperspectral Image Endmember Extraction in Parallel

2015 ◽  
Vol 8 (10) ◽  
pp. 4657-4664 ◽  
Author(s):  
Lina Yang ◽  
Xu Sun ◽  
Ling Peng ◽  
Xiaojing Yao ◽  
Tianhe Chi
Keyword(s):  
Artificial Bee Colony ◽  
Hyperspectral Image ◽  
Endmember Extraction ◽  
Agent Based ◽  
Abc Algorithm ◽  
Bee Colony

scholarly journals An Efficient Framework for Remote Sensing Parallel Processing: Integrating the Artificial Bee Colony Algorithm and Multiagent Technology

2019 ◽  
Vol 11 (2) ◽  
pp. 152 ◽  
Author(s):  
Lina Yang ◽  
Xu Sun ◽  
Zhenlong Li
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Parallel Computing ◽  
Artificial Bee Colony Algorithm ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Endmember Extraction ◽  
Abc Algorithm ◽  
Computational Burden ◽  
Bee Colony

Remote sensing (RS) image processing can be converted to an optimization problem, which can then be solved by swarm intelligence algorithms, such as the artificial bee colony (ABC) algorithm, to improve the accuracy of the results. However, such optimization algorithms often result in a heavy computational burden. To realize the intrinsic parallel computing ability of ABC to address the computational challenges of RS optimization, an improved multiagent (MA)-based ABC framework with a reduced communication cost among agents is proposed by utilizing MA technology. Two types of agents, massive bee agents and one administration agent, located in multiple computing nodes are designed. Based on the communication and cooperation among agents, RS optimization computing is realized in a distributed and concurrent manner. Using hyperspectral RS clustering and endmember extraction as case studies, experimental results indicate that the proposed MA-based ABC approach can effectively improve the computing efficiency while maintaining optimization accuracy.

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 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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