Hybrid Particle Swarm Optimization with Iterative Local Search for DNA Sequence Assembly

Title:Hybrid Particle Swarm Optimization with Iterative Local Search for DNA Sequence Assembly

Volume: 10 Issue: 4

Author(s): Indumathy Rajagopal and Uma Maheswari Sankareswaran

Affiliation:

Keywords: Evolutionary algorithms, genome sequence assembly, inertia weight, local search; metaheuristics, optimization, particle swarm optimization, smallest position value rule, semi-global alignment.

Abstract: This paper proposes a novel hybrid approach to solve the DNA sequence assembly problem by combining particle swarm optimization and iterative local search algorithms. One of the vital challenges in DNA sequence assembly is to arrange a long genome sequence that consists of millions of fragments in accurate order. This is an NP- hard combinatorial optimization problem. The prominence of this paper is to demonstrate how this hybrid algorithms scheme can improve the performance of fragment assembly process. Incorporating iterative local search heuristics in particle swarm optimization algorithm efficiently assembles the fragments by maximizing the overlap score. The performances of the proposed hybrid algorithm were compared with the variants of Particle Swarm Optimization algorithms and other known methodologies. The experimental results show that the proposed hybrid approach produces better results than the other techniques when tested with different sized well-known benchmark instances.

Cite this article as:

Rajagopal Indumathy and Sankareswaran Maheswari Uma, Hybrid Particle Swarm Optimization with Iterative Local Search for DNA Sequence Assembly, Current Bioinformatics 2015; 10 (4) . https://dx.doi.org/10.2174/157489361004150922132228