摘要
针对目前工业生产中存在的矩形件排样优化问题,采用交叉概率和变异概率自适应改变的自适应遗传算法,并在遗传算法主要环节中采用改进的、性能较优的算子对排样序列进行求解,提出一种基于集中剩余矩形区域策略的解码方法并将其运用到求解过程中,以提高排样的板材利用率。经实验结果分析,所提出的排样方法在寻优能力和求解的稳定性方面均有较明显的提高,可获得较高的板材利用率,适用于生产实践中。
Focused on the rectangular packing optimization problems among the craft production of current industries, this pa- per applied adaptive genetic algorithm（AGA） in which the crossover probability and mutation probability could adaptively ad- just and the improved operators with better performance to solve the packing problem. This paper presented a decoding method based on concentrated surplus rectangle area strategy and applied it into the solving process to improve the utilization of sheet. According to the experiment results, the proposed packing method obviously improves the optimization capacity and stability of solution. A higher utilization of sheet is acquired according to the proposed packing method which is fit for productive practice.
出处
《计算机应用研究》
CSCD
北大核心
2016年第11期3235-3239,共5页
Application Research of Computers
基金
广西自然科学基金资助项目(2014GXNSFAA118382)
广西大学博士启动基金资助项目(XBZ140491)
上海市教育委员会科研创新项目(14ZZ167)
国家自然科学基金资助项目(71463003)
关键词
自适应遗传算法
矩形件排样
最低水平线算法
剩余矩形
板材利用率
adaptive genetic algorithm
rectangular packing
lowest horizontal line algorithm
surplus rectangle
utilization of sheet
