摘要
矩形排样优化属于NPC问题,在工业界有着广泛的应用,如布料切割、金属下料和新闻组版等。提出了一种基于环形交叉算子和环形变异算子的自适应遗传算法,并将改进的自适应遗传算法和IBL启发式布局算法相结合,有效地解决了矩形排样优化问题。对比实验结果表明,环形交叉算子和环形变异算子对遗传算法是有效的,所提出的改进混合自适应遗传算法能够在一个较短的时间内找到满意解。
The packing of rectangles is a NP-Complete problem and possesses widespread applications in the industry,such as the cutting of clothing,metal and composition of news.In this paper,circular-based crossover operator and circular-based mutation operator are adopted in the proposed adaptive genetic algorithm.With the combination of Improved Adaptive Genetic Algorithm（I- AGA） and Improved Bottom-Left heuristic（IBL） algorithm,the packing of rectangles can be effectively solved.The comparison results show that genetic algorithm is more effective by using circular-based crossover operator and circular-based mutation operator and the satisfactory solution can be obtained in a reasonably short period of time by using hybrid IAGA.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第22期244-248,共5页
Computer Engineering and Applications
关键词
自适应遗传算法
矩形排样优化
启发式布局算法
环形交叉算子
环形变异算子
Adaptive Genetic Algorithm （AGA）
packing of rectangles
heuristic placement algorithm
circular-based crossover operator
circular-based mutation operator
