基于改进PSO算法的自动配棉工艺参数优化设计  

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

作　　者：陈怀忠[1,2] 何仁初[3] 史桂丽[2] 

机构地区：[1]浙江工业职业技术学院电气学院,浙江绍兴312000 [2]浙江理工大学自动化研究所,浙江杭州310018 [3]上海大学机电工程与自动化学院,上海200072

出　　处：《纺织学报》2014年第6期142-147,共6页Journal of Textile Research

基　　金：浙江省自然科学基金资助项目(Q12F030056)

摘　　要：为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,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和本文改进的PSO算法同时对配棉工艺模型求解。结果显示:改进的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,具有一定的实用价值。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 （particleswarm 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.

关 键 词：配棉 改进PSO 动态权重 学习因子 

分 类 号：TS193.32[轻工技术与工程—纺织化学与染整工程]