High-performance computing memetic algorithm (HPCMA) model to process image fingerprint dataset

Priati Assiroj, Harco Leslie Hendric Spits Warnars, Edi Abdurachman, Achmad Imam Kistijantoro, Antoine Doucet

Abstract


The terminology of the memetic refers to “meme”. The meme is a part of natural information of the individual population and this information can be transmitted among the individual of the population. The memetic algorithm (MA) uses the evolutionary concept based on the Genetic Algorithm (GA) and is combined with a local search feature. Thus, as in GA, MA employs the basic steps such as selection, crossover, and mutation with additional components a local search to improve the solution of candidates. The major challenge of this algorithm is how to develop a good local search operator that can do a good exploration of the entire population. Several methods have been proposed and we have studied several parallel methods implemented on MA (PMA) to increase the efficiency of processing time. The parallel method is included in cluster computing, and that is a part of high-performance computing systems. In this work, we proposed the two new models of the memetic algorithm that run in a high-performance computing system (HPCMA) environment by utilizing the multi-threads feature of processor, so-called HPCMA1 and HPCMA2 model, to process fingerprints image dataset. The result shows that the HPCMA1 model runs with high inconsistency data caused by its overlaps in the macro-process and to address the problem from the HPCMA1 model we use the HPCMA2 model.

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Published: 2021-05-19

How to Cite this Article:

Priati Assiroj, Harco Leslie Hendric Spits Warnars, Edi Abdurachman, Achmad Imam Kistijantoro, Antoine Doucet, High-performance computing memetic algorithm (HPCMA) model to process image fingerprint dataset, J. Math. Comput. Sci., 11 (2021), 3813-3828

Copyright © 2021 Priati Assiroj, Harco Leslie Hendric Spits Warnars, Edi Abdurachman, Achmad Imam Kistijantoro, Antoine Doucet. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

 

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