Keywords : Parallel Genetic Algorithm (PGA)


Hybrid System: Parallel Neural -Genetic Algorithm Algorithm for Compacting Fractal Images Using Multiple Computers

Shahla A. Abdel-Qader; Suzan K. Ibrahim; Omia Gh. Abdel-Jabbar

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 4, Pages 85-102
DOI: 10.33899/csmj.2013.163558

Recently, effective technologies in Fractal Image Coding (FIC) were used to reduce the complexity of search for the matching between the Range blocks and the  Domain blocks which reduces the time needed for calculation. The aim of this research is to propose a Hybird Parallel Neural -Genetic Algorithm (HPNGA) using  the technique  of  (Manager/Worker) in  multiple computers in order to obtain the fastest   and best compression through extracting the features of the  gray and colored images to attenuate the problem of dimensions in them .The NN enabled to train separate images from the test images to reduce the calculation time. The NN able to adapt itself with the training data to reduce the complexity and having more data and is merged with the parallel GA to reach optimum values of weights with their biases. The optimum weights obtained will classify the correct search domains with the least deviation ,which, in turn ,helps decompress the images using the fractal method with the minimum time and with high resolution through multiple computers. The results showed that the proposed hybrid system  is  faster  than the  standard algorithm ,the NN and GA in decompressing the FIC and they  are flexible and effective to reach the optimum solution with high speed and resolution .The search method used for compression and de-compression has a vital role in improving the ratio  and the quality of image compression which  reached 15s .The  ratio of compression reached to  90.68% and the image improvement after decompression   reached  to 34.71db  when  compared to other  methods of  (FIC), which didn't exceed 90.41% and image quality of 32.41db and the execution speed  was only  21s.
 

Measurement of the Efficiency of Parallel Genetic Algorithm for Compress and Decompression of Fractal Imaging Using Multiple Computers

Shahla A. Abdul Qadir

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 2, Pages 219-232
DOI: 10.33899/csmj.2013.163496

Efficient technologies have  been recently used in Fractal Image Coding (FIC) to reduce the complexity of searching for matching between Range block and Domain block. The research aims at using the Parallel Genetic Algorithm (PGA) by the technology of the (Manager/Worker) in parallel computers to obtain  best and quickest compress for images by  coding the site of the searching domain block with a Gray code and a fitness function that minimizes the space  between the matching of the current range block with the searching  domain block  in order  to choose  a protection strategy and compress of high accuracy of  images . Results showed that PGA is  quicker than standard algorithm in FIC  and  is more flexible and efficient in reaching the optimum solution in higher speed and efficiency through using the Gray code. The searching method used for the parallel algorithm for compression and decompression , the method of choosing GA's coefficients, (selection, crossover  and mutation) were of a  significant role in improving the image compression ratio and quality for images in high speed that has reached 15s , compression ratio has reached  91.68% , while the image quality was improved after decompression  and has  reached  roughly 34.81  compared to traditional method of  fractal image coding (FIC) where the compression ratio has reached 83.87% and image quality 31.79 with algorithm implementation speed reached 28s.