基于改进PSO算法的自动配棉工艺参数优化设计

为了进一步改善自动配棉的通用性和自适应性，针对配棉工艺多约束条件特点，进行了自动配棉优化设计。提出了一种基于改进的PSO(Particle Swarm Optimization)算法的自动配棉参数优化求解方法。通过配棉数学模型建立，将其转化为多约束条件优化求解问题。分析了标准PSO算法在配棉工艺参数寻优的不足，针对标准PSO算法惯性权重和学习因子策略的不足加以改进。将采集到的棉纺企业工艺参数，用标准PSO和本文提出的改进PSO算法同时对配棉工艺模型求解。结果显示：改进PSO算法采用了惯性权重递减和学习因子自适应策略，寻优速度、精度、局部和全局寻优能力等指标都得到提高，降低了企业配棉成本，具有一定的实际应用价值。

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

According to the characteristics of computer distribution multi constraint conditions, in order to further improve the versatility and adaptability of computer automatic cotton, this paper put forward a kind of improved PSO (Particle Swarm Optimization) optimization method. Through establishment of the mathematical model of cotton blending, we transform it into the optimization problems with multiple constraints. On the basis of analysis of the standard PSO algorithm shortcomings, the inertia weight and learning strategy improvement factor are improved. Improved and the standard PSO algorithm solve the same cotton blending in the meantime with parameters collected from cotton spinning enterprises . The results showed that by using inertia weight and learning factor and adaptive strategy, optimizing speed, precision, the ability of local and global optimization and other indicators have been improved, reducing the cotton distribution costs of enterprises thus has a certain practical application value.