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Hybrid Approach to Optimal Packing Using Genetic Algorithm and Coulomb Potential Algorithm

机译:遗传算法和库仑势算法的混合优化装箱方法

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摘要

It is difficult and computationally time-consuming to find the best possible solutions for blank packing problems, because they include a lot of underlying combinational conditions. This paper presents two approaches for packing two-dimensional irregular-shaped polygonal elements-a real-encoded genetic algorithm and a hybrid algorithm using a real-encoded genetic algorithm and a local optimization algorithm. The local optimization algorithm presented is a novel one utilizing the Coulomb potential technique. In the hybrid approach, the real-encoded genetic algorithm generates the order of the polygons while the coulomb potential algorithm determines the embodiment layout under the fixed combinations so as to minimize the scrap. The hybrid genetic algorithm is found to give better results for problems of larger size although it takes more computational time.
机译:找到空白包装问题的最佳可能解决方案是困难且计算耗时的,因为它们包括许多潜在的组合条件。本文提出了两种打包二维不规则形状多边形元素的方法-实编码遗传算法和使用实编码遗传算法和局部优化算法的混合算法。提出的局部优化算法是一种利用库仑势技术的新颖算法。在混合方法中,实编码遗传算法生成多边形的顺序,而库仑势算法确定固定组合下的实施例布局,以使废品最小化。发现混合遗传算法尽管需要更多的计算时间,但对于较大的问题却能提供更好的结果。

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