Nature Inspired Algorithms for Genome Subsequence Assembly in Hadoop

Raja, G.; Reddy, U. Srinivasulu

doi:10.1109/iccic.2017.8523835

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…DNA Subsequence Assembly using Probabilistic Edge Recombination (PER) has also been proposed with Particle Swarm Optimization [23] to solve this problem. In the past few years, algorithms such as Cuckoo Search have been used too [24,25,26]. A hybrid approach using PSO and Grey Wolf Optimization was proposed by Xinming Zhang et al [27].…”

Section: Consensus Stagementioning

confidence: 99%

Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly

Jain¹,

Bharti²

2021

Preprint

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With the advent of Genome Sequencing, the field of Personalized Medicine has been revolutionized. From drug testing and studying diseases and mutations to clan genomics, studying the genome is required. However, genome sequence assembly is a very complex combinatorial optimization problem of computational biology. PSO is a popular metaheuristic swarm intelligence optimization algorithm, used to solve combinatorial optimization problems. In this paper, we propose a new variant of PSO to address this permutation-optimization problem. PSO is integrated with the Chaos and Levy Flight (A random walk algorithm) to effectively balance the exploration and exploitation capability of the algorithm. Empirical experiments are conducted to evaluate the performance of the proposed method in comparison to the other variants of the PSO proposed in the literature. The analysis is conducted on four DNA coverage datasets. The conducted analysis demonstrates that the proposed model attain a better performance with better reliability and consistency in comparison to others competitive methods in all cases.

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