Research And Application Of Particle Swarm Optimization Based On Sine And Cosine Strategy

With the continuous emergence of more and more optimization problems in social production and real life,there are many complex optimization problems that cannot be solved by traditional optimization methods,such as multi-peak function optimization problems,discontinuous function optimization problems,and so on.Micro function optimization problems,combinatorial optimization problems and large-scale optimization problems.Therefore,in order to solve such complex optimization problems,a large number of new heuristic optimization algorithms have been proposed continuously,among which there are genetic algorithms,ant colony algorithms,differential evolution and particle swarm optimization algorithms.These heuristic optimization algorithms are generally obtained by simulating and modeling natural phenomena,physical phenomena or social phenomena.Particle Swarm Optimization(PSO)is one of the best algorithms.Particle Swarm Optimization(PSO)algorithm is a group-based intelligent optimization algorithm.It is simple and easy to implement with simple rules,fast convergence,and requires few parameters to be known.Due to these excellent performances of the particle swarm optimization algorithm,it is very easy to apply other fields,such as function optimization,combinatorial optimization,data mining and biological information and other fields.However,PSO algorithm still has some deficiencies,such as easy to fall into local optimum.This paper aims at this shortcoming,improves the PSO algorithm,and applies the improved algorithm to the local sequence alignment problem in bioinformatics.The main contributions of this article are as follows:(1)A new Sine Cosine Particle Swarm Optimization(SC-PSO)algorithm based on sine cosine is proposed.The algorithm uses the sine cosine strategy to automatically adjust the coefficients(cognitive component coefficient and social composition coefficient)in the particle swarm optimization algorithm to achieve the escape of local optimums and to better regulate the relationship between exploration and development.Can improve the algorithm's convergence speed and convergence accuracy.To verify the proposed algorithm's performance,it is compared with three famous intelligent optimization algorithms on 20 benchmark functions.The comparison experiments show that the proposed algorithm has better performance.(2)Sequence alignment is one of the basic tasks and main procedures inbioinformatics.It is mainly used to measure the similarity between biological sequences to provide relevant instructions on evolutionary and functional relationships between RNA,DNA and protein sequences..Sequence alignments compare sequences by matching their bases(the amino acids of the protein and the nucleotides of the DNA)to produce the best alignment that represents the highest degree of similarity.Protein secondary structure predictions and analyses use alignments to improve prediction quality.In this paper,the application of the SC-SPO algorithm to the local sequence alignment was explored to embed it into the local sequence alignment method.And compared with other methods to verify the performance of the method.