摘要
为提高矩形件排样算法的利用率与时间效率,提出将遗传算法和模拟退火算法融合优化的矩形排样算法。采用带符号的十进制编码,依据矩形件长宽比和面积而生成基因序列用于建立初始种群,以随机产生若干排样顺序与排样尺寸不一的个体,并以利用率为适应度函数,修改后的最低水平线搜索算法作为排样策略,保证较优个体得以保留,减少闲置区域的产生。采用10组随机产生的矩形数据将本算法与现有文献提出的GA算法进行对比实验,实验结果显示:该算法有效地提升了排样结果的利用率与时间效率。
Based on the integration of the genetic algorithm(GA)and the simulated annealing algorithm,an improved lowest horizontal line(ILHL)algorithm is presented in order to improve utilization and stability of the rectangular packing algorithm.In this algorithm,a signed decimal encoding is utilized to generate the gene sequence in accordance with the length-width ratio and the area of the rectangle,which is employed to establish the initial population.The improved lowest horizontal line algorithm adopts the best individuals from a number of random sequences with different nesting orders and layout sizes,uses utilization rate as the fitness function and reduces the idle area.In this paper,a contrast experiment is operated to compare ten groups of rectangular data randomly generated by ILHL with those generated by GA proposed in the current literature.The experiment results show that our algorithm(ILHL)can effectively improve the utilization rate and time efficiency of the packing results.
作者
周家智
尹令
张素敏
ZHOU Jiazhi;YIN Ling;ZHANG Sumin(School of Mathematics and Informatics,South China Agricultural University,Guangdong Guangzhou 510642,China)
出处
《图学学报》
CSCD
北大核心
2018年第3期567-572,共6页
Journal of Graphics
关键词
矩形件排样
遗传算法
模拟退火算法
最低水平线改进算法
rectangular packing
genetic algorithm
simulated annealing algorithm
improved lowest horizontal line
作者简介
周家智(1994-),男,广东清远人,本科生。主要研究方向为布局优化、机器学习。E-mail:524841091@qq.com。
