Research of Genomic Problems Based on Improved Particle Swarm Optimization

Aiming at the problems of the poor convergence performance of particle swarm optimization algorithm and falling into local optimum in complex optimization problems, a design method of the intelligent fusion algorithm combined with branch and bound algorithm to solve genomic problems is proposed. Particle swarm optimization and artificial bee colony algorithm as high-efficiency heuristic algorithms have high application value in solving combinatorial optimization problems. By effectively combining the two algorithms, they can make use of their respective advantages to make up for their respective shortcomings. At the same time, the improved particle swarm optimization algorithm is introduced into the branch and bound method, which can make up for the limitations of the branch and bound method, reduce the useless search of the algorithm, and improve the efficiency of the algorithm. The experimental results show that the proposed method improves the efficiency of solving genome problems, which lays a foundation for the further application of genome problems in practice.