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