An Adaptive Particle Swarm Optimization Algorithm for Solving DNA Fragment Assembly Problem

ISSN: 2212-392X (Online)
ISSN: 1574-8936 (Print)

Volume 10, 5 Issues, 2015

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Alessandro Giuliani
Istituto Superiore di Sanitá (Italian NIH) Environment and Health Dept

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An Adaptive Particle Swarm Optimization Algorithm for Solving DNA Fragment Assembly Problem

Author(s): Indumathy Rajagopal and Uma Maheswari Sankareswaran

Affiliation: Department of Electronics and Communication Engineering, Coimbatore Institute of Technology, Coimbatore - 641014, Tamilnadu, India.


This paper proposes an efficient method to solve the DNA fragment assembly problem using Adaptive Particle Swarm Optimization (APSO). The DNA fragment assembly for shotgun sequencing has been under study with great significance and complexity. It refers to the arrangement of the fragments in an accurate sequence. This fragment assembly problem is an NP-hard combinatorial optimization problem. In this paper, three different methods namely Constant Inertia Weight (CIW), Dynamically Varying Inertia Weight (DVIW) and An Adaptive Particle Swarm Optimization (APSO) with Smallest Position Value (SPV) rule are proposed to solve the DNA fragment assembly problem. The objective of the proposed method is to obtain the maximum overlapping score by assembling the fragments. Particle swarm optimization algorithm is used to analyze the impact of inertia weight, the cognitive and social components. The PSO algorithm was simulated for each of the methods individually. The experimental results are obvious that the proposed APSO method yields better overlap score when tested with different sized benchmark instances. The proposed APSO method is effective and efficient in assembling the fragments and getting the maximum overlap score when compared to other heuristic techniques.

Keywords: Adaptive particle swarm optimization, bioinformatics, DNA fragment assembly, inertia weight, smallest position value.

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Article Details

Volume: 10
Issue Number: 1
First Page: 97
Last Page: 105
Page Count: 9
DOI: 10.2174/1574893609666140301001642

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