摘要： 针对大规模零件和布料优化排样问题，研究遗传算法在智能排样中的应用及其在智能优化排样中的优缺点。以传统遗传算法优化排样为基础，提出一种改进的基于遗传算法的优化排样算法，利用图形间的相似度对图形群体进行分类，降低遗传算法的时间复杂度。实验结果证明，该方法在时间复杂度上优于传统的遗传算法优化排样，适用于大规模的图形排样系统。

关键词: 排样, 遗传算法, 智能排样, 相似度, 时间复杂度

Abstract: Aiming at problems of large scale spare parts and cloth optimizing, this paper analyzes the application of improved generic algorithm in the irregular graph stock layout, and researches the advantage and disadvantage of generic algorithm in the intelligent optimizing stock layout. Based on conventional generic algorithm, it puts forward an improved algorithm which classifies graph group by their similarity degree in order to simplify time complicity of generic algorithm. Experimental results prove that this means outgoes the conventional one in time complicity, so it is applied to large scale of graph stock layout system.

Key words: stock layout, generic algorithm, intelligent stock layout, similarity degree, time complicity

中图分类号: 

• TP391.7