Performance Evaluation of Sine-Cosine Optimization Versus Particle Swarm Optimization for Global Sequence Alignment Problem

Pairwise global sequence alignment is a vital process for finding functional and evolutionary similarity between biological sequences. The main usage of it is searching biological databases for finding the origin of unknown sequence. The standard global alignment based on dynamic programming approach which produces the accurate alignment but with extensive execution time. In this chapter, Sine-Cosine optimization algorithm was used for accelerating pairwise global alignment with alignment score near one produced by dynamic programming alignment. The reason for using Sine-Cosine optimization is its excellent exploration of the search space. The developed technique was tested on human and mouse protein sequences and was compared with classical global sequence alignment and alignment using Particle Swarm Optimization method.

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