Optimization Of Polypeptides Separation Condition Based On Improved Particle Swarm Optimization Algorithm

With the development of modern biotechnology, the unique absorption, high activity and diversity of polypeptides arouse people's interest in application, demonstrated its tremendous value. But for industrial production of polypeptides, it is particularly important of its separation and purification techniques or mass production. It is significant to study these parathion and analyses of polypeptides so as to contribute to industrialization.By use of the principle and feature of polypeptides separation and purification techniques, in this paper, particle swarm optimization (PSO )algorithm and its using in polypeptides separation and purification is researched deeply, optimization of ideas and methods based on the PSO algorithm are presented. The theory of the PSO algorithm is analyzed based on mathematical theory, and analyzes the standards PSO algorithm convergence and diversity. To maintain the diversity of groups in particles iterative optimization process, analyzes the particle swarm iterative optimization process, and presents the changed PSO algorithm based on a dynamic inertia weight (DWPSO). By the tested of several multidimensional functions, it has been proved that DWPSO algorithm can improve the global convergence velocity and global search ability, greatly enhance the rate of global convergence and overcome the shortcoming of basic PSO algorithm, which is easily plunging into the local minimum's basin.Combining DWPSO algorithm and multi-objective optimization theory, DWPSO algorithm has been applied in optimization of mobile phase composition in high performance liquid chromatograph. So a kind of intelligent optimization strategy for the optimization of separation condition has been provided. The valid strategy is confirmed by its application to the optimization of the separation condition for nine species of small peptides ,and the optimization results were satisfactory.