Dna Sequence Alignment Based On Intelligent Computing Research

Bioinformatics is an emerging multi-discipline of mathematics, information, biotechnology,computers and other disciplines. The focus of its research is to use computer databases andcomputer algorithms to analyze the structure and function of the gene and protein sequences.Bioinformatics expect the use of a computer program to reveal the structure and function ofbiological molecules, genetic diseases and the occurrence and evolution of the basic law of therelevant biological problems. Sequence alignment can be found the relationship betweenbiological sequences in the aspect of structure, function and evolution. It is one of the most corebiological data processing methods.And the sequence of research has become increasinglyin-depth applications.This paper introduces the basic knowledge of bioinformatics, the basic principles andsignificance of DNA sequence alignment problem and the basic idea of particle swarmoptimization, genetic algorithms and simulated annealing algorithm. First, it introduced the basicprinciples of DNA sequence alignment problem, including basic concepts, scoring matrix, gappenalty and the objective function, describes DNA sequence alignment problem mathematicaldescription, and then describes the current development of the sequence alignment. For pairwisesequence alignment algorithms, it includes dot matrix method, the Needleman-Wunsch algorithm,FASTA algorithm. For the multiple sequence alignment algorithms, it includes the accuratealignment algorithm, iterative algorithm, heuristic algorithms. For multi-sequence problem, thispaper’s metheds improve particle swarm optimization, and prove the feasibility of the algorithmby experiments. At the same time, the simulated annealing algorithm is introduced to the processof genetic algorithm and the algorithm used in the multiple sequence alignment that proved thatthe algorithm has significantly improved in terms of computing speed and the accuracy of theresults by experiments.In the third chapter, the first study is about the idea of particle swarm optimizationalgorithm, algorithms models and features. The main operating parameters in the algorithm aredescribed.And then for the shortcomings of PSO that is easy to fall into the local minima thePSO has been improved. Design based on improved particle swarm algorithm with the corresponding DNA sequence algorithm PSO-MSA, a detailed description of the concreterealization of the various parameters and algorithms process. Finally, the experiment confirmedthe feasibility of this algorithm.In the fourth chapter, we study the idea of genetic algorithms, simulated annealingalgorithm, their algorithms processes, advantages and disadvantages. And then because of a poorlocal search ability of genetic algorithm and the premature defects, genetic algorithms andsimulated annealing algorithm combined with the idea, the design of the Metropolis criterion toaccept the new solution of DNA multiple sequence alignment algorithm, so that the searchefficiency greatly improve the simulated annealing algorithm is in the process of selection,crossover and mutation. This chapter, the proposed algorithm has significantly improved in termsof computing speed and the accuracy of the results for multi-sequence problem by experiments.