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Pe-DFA: Penguins Search Optimisation Algorithm for DNA Fragment Assembly

Pe-DFA: Penguins Search Optimisation Algorithm for DNA Fragment Assembly

Youcef Gheraibia, Abdelouahab Moussaoui, Sohag Kabir, Smaine Mazouzi
Copyright: © 2016 |Volume: 7 |Issue: 2 |Pages: 13
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781466691216|DOI: 10.4018/IJAMC.2016040104
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MLA

Gheraibia, Youcef, et al. "Pe-DFA: Penguins Search Optimisation Algorithm for DNA Fragment Assembly." IJAMC vol.7, no.2 2016: pp.58-70. http://doi.org/10.4018/IJAMC.2016040104

APA

Gheraibia, Y., Moussaoui, A., Kabir, S., & Mazouzi, S. (2016). Pe-DFA: Penguins Search Optimisation Algorithm for DNA Fragment Assembly. International Journal of Applied Metaheuristic Computing (IJAMC), 7(2), 58-70. http://doi.org/10.4018/IJAMC.2016040104

Chicago

Gheraibia, Youcef, et al. "Pe-DFA: Penguins Search Optimisation Algorithm for DNA Fragment Assembly," International Journal of Applied Metaheuristic Computing (IJAMC) 7, no.2: 58-70. http://doi.org/10.4018/IJAMC.2016040104

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Abstract

DNA Fragment Assembly (DFA) is a process of finding the best order and orientation of a set of DNA fragments to reconstruct the original DNA sequence from them. As it has to consider all possible combinations among the DNA fragments, it is considered as a combinatorial optimisation problem. This paper presents a method showing the use of Penguins Search Optimisation Algorithm (PeSOA) for DNA fragment assembly problem. Penguins search optimisation is a nature inspired metaheuristic algorithm based on the collaborative hunting strategy of penguins. The approach starts its operation by generating a set of random population. After that, the population is divided into several groups, and each group contains a set of active fragments in which the penguins concentrate on the search process. The search process of the penguin optimisation algorithm is controlled by the oxygen reserve of penguins. During the search process each penguin shares its best found solution with other penguins to quickly converge to the global optimum. In this paper, the authors adapted the original PeSOA algorithm to obtain a new algorithm structure for DNA assembly problem. The effectiveness of the proposed approach has been verified by applying it on the well-known benchmarks for the DNA assembly problem. The results show that the proposed method performed well compared to the most used DNA fragment assembly methods.

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