A heuristic for two bin partition problem

A. Asadullah, K. Dinesha, P. Bhatt
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Abstract

There are many heuristics to address 2-bin integer partition problem. The range (R) of the values in the data set and the number of element (N) in the data set are 2-parameters which determine the appropriate heuristics. By and large, for large N, Karmarkar-Karp(KK) heuristics offers solutions. For low values of N, Complete Karmarkar-Karp heuristics (CKK), Horowitz and Sahni (HS), Schroeppel and Shamir (SS), Brute-Force search (BF) offers solutions. However, our computations indicate that for R > 1012 and for a specific range of N, depending on R, (R = 1014, N = 60 to 150) the best existing heuristics (CKK) takes long or very long CPU time. We are proposing a different heuristic to address this scenario. The proposed heuristic in the paper uses depth-first (like KK) set differencing till N become 48 and from N = 48 to 1 it performs exhaustive search (like HS). For the above mentioned scenario, we found that this combination of strategies gives better and faster solution compared to CKK.
两个bin分区问题的一种启发式算法
有许多启发式方法来解决2-bin整数分区问题。数据集中值的范围(R)和数据集中元素的数量(N)是两个参数,它们决定了适当的启发式。总的来说,对于较大的N,卡马卡-卡普(KK)启发式提供了解决方案。对于低N值,完全Karmarkar-Karp启发式(CKK), Horowitz and Sahni (HS), Schroeppel and Shamir (SS), Brute-Force search (BF)提供了解决方案。然而,我们的计算表明,对于R bb0 1012和特定的N范围,取决于R, (R = 1014, N = 60到150),现有的最佳启发式(CKK)需要很长或很长的CPU时间。我们提出了一种不同的启发式方法来解决这种情况。本文提出的启发式算法使用深度优先(如KK)集差分直到N变为48,并从N = 48到1执行穷举搜索(如HS)。对于上述场景,我们发现与CKK相比,这种策略组合提供了更好更快的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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