摘要
利用遗传算法对待排零件进行编码,将矩形件正交排样问题转化为排列问题。然后采用一种新的解码排样算法——基于最低水平线的改进算法,将每一个体编码转化为排样图,进行适应度评价,以驱动遗传进化,最终寻找出最优排样图。对遗传算法进行了并行性改进,较好地维持了种群的多样性,增强了算法的搜索效率。对文献中的两个算例进行了求解,结果表明该算法是有效的。
Using an improved parallel genetic algorithms to encode the rectangle parts,convert the orthogonal rectangular packing problem into the problem of permutation.A new decoding algorithm for nesting—the improved Lowest Horizontal Line Algorithm—is proposed for decoding every individual permutation into a packing pattern for fitness evaluation,so as to drive genetic evolution,and eventually finding out the optimal packing pattern.In order to maintain the diversity of population and increase its search efficiency,making a parallel improve for genetic algorithm.Solutions to two examples show the validity and efficiency of the algorithm.
出处
《组合机床与自动化加工技术》
北大核心
2011年第3期78-82,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
福建省科技计划重点项目(2009H0032)
关键词
矩形件
排样优化
并行遗传算法
rectangle parts
packing optimization
parallel genetic algorithms（PGA）
