中文摘要：

针对服装行业排样问题,提出了一种基于压缩因子粒子群的组合排样方法.该方法首先对样片进行预处理,接着通过k-means动态聚类样片,得到具有相似特征的同类样片,再将同类样片进行有选择性的组合,得到组合样片.然后提取利用率高于单个样片的组合样片的外轮廓,将其作为最终排样样片.最后结合有较强的搜索能力的压缩因子粒子群算法进行排样,从而实现服装样片的优化排样.实验表明：所提的组合排样方法能有效提高排样的效率和减少排样所需的时间.

英文摘要：

In order to solve the problem of nesting in garment industry,a new combined nesting method based on the compression factor particle swarm optimization is proposed.Firstly,the samples are pretreated,and the samples are dynamically clustered by k-means to obtain the similar samples with similar characteristics.Then the similar samples are selectively combined to get the combined samples.Then the outline of combined samples whose utilization ratio is higher than single sample＇s is extracted and is taken as the final nesting.Finally,the PSO algorithm with compression factor which has strong ability of searching is used in nesting in order to achieve the optimal sample of garment samples.The experiments show that the proposed method can improve the efficiency of nesting and reduce the time required for nesting.