Parallel Bat Algorithm Using MapReduce Model

Sharma, Kapil; Girotra, Sanchi

doi:10.5815/ijitcs.2017.11.08

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…The bat algorithm (BA) was first presented in [22]. BA has also been successfully applied and used in many fields especially as optimization problem [24,25,26], clustering problem [27,28] and many other problems [29,30,31,32].…”

Section: Bat Algorithmmentioning

confidence: 99%

Bat-Genetic Encryption Technique

Mousa¹

2019

IJISA

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Nowadays, the security of confidential data is the vital issue in the digital world. Information security becomes even more essential in storing and transmitting data while online. For protecting digital data and achieving security and confidentiality over an insecure internet, the iterative Bat-Genetic Encryption Technique (B-GET) is proposed. The main stages of B-GET are preprocessing, encryption process, bat algorithm steps, and genetic processes. B-GET also comprises an arithmetic and logical operators that increase encryption quality. Empirical results show that the reconstructed data is a copy of the original. It also demonstrates that B-GET technique has a large space key and several defensive stages that resist many attacks and it has strong security based on multiple steps, multiple variables, and the main stages of the B-GET technique. Encrypted data is nearly random and does not contain any indication to secret data.

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Section: Bat Algorithmmentioning

confidence: 99%

Bat-Genetic Encryption Technique

Mousa¹

2019

IJISA

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“…Kapil Sharma proposed a parallel bat algorithm by using MapReduce model. [24] Zheng introduced a masked method optimized by artificial immune algorithm (AIA) [11], Wang used Gravitational search algorithm (GSA) to achieve the best tuned mask [12] etc. On the whole, evolutionary algorithms or swarm intelligence algorithms performs well on the problem.…”

Section: Introductionmentioning

confidence: 99%

Application of Bat Algorithm for Texture Image Classification

Ye¹,

Hou²,

Xu³

et al. 2018

IJISA

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Textural feature extraction of image is a basic work for image analysis. A number of approaches have been put forward to describe texture features quantitatively, such as gray level co-occurrence matrix, fractal wavelet, Gabor wavelet and local binary pattern etc, among them texture feature extracted based on "tuned" mask will not suffer from rotation and scale of images. However, it needs to take a lot of time to learn the tuned mask with the traditional methods and could not acquire the satisfying mask sometimes. In essence, it is a very hard combinational optimization problem and easy to fall into the local optimum with mountain climbing method. Bat algorithm is a newly proposed meta-heuristic optimization, which is employed to tune the optimal mask in the paper. The procedure of bat algorithm to learn the tuned mask is detailed. Experiments results testifies that the proposed method is propitious to draw texture features, its performance is better than the simple particle swarm optimization and genetic algorithm based mask tuning scheme, which is a robust approach for texture image analysis.

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