Parameter optimization design for automatic cotton assorting based on improved PSO algorithm

In order to further improve the versatility and adaptability of the automatic cotton assorting process,according to the characteristics of cotton with multi constraint conditions,the optimization design of automatic cotton assorting is carried out. This paper puts forward a kind of improved PSO( particle swarm optimization) optimization method to solve automatic cotton assorting parameter optimization. Through establishment of the mathematical model of cotton assorting, it is transformed into the optimization problems with multiple constraints. On the basis of analysis of the standard PSO algorithm shortcomings,the improvement factor of inertia weight and learning strategy are improved. The standard and improved PSO algorithm solve the same cotton assorting in the meantime with parameters collected from cotton spinning enterprises. The results showed that by using inertia weight and learning factor adaptive strategy,optimizing speed,precision,the capacity of local and global optimization and other indicators have been improved,reducing the cotton assorting costs of enterprises,thus this research has a certain practical application value.