基于近似骨干的下一次发布问题的多层算法
Approximate Backbone Based Multilevel Algorithm for Next Release Problem
摘要：
下一版本问题（NRP）旨在有效地选择软件需求，以获得最大的客户利润。作为软件需求工程中的NP难问题，NRP缺乏针对大规模实例的有效近似算法。主干网是近年来出现的一种解决大规模NP难问题的新工具。在本文中，我们采用骨干设计高性能的近似算法的大规模NRP的实例。首先，我们证明了NRP的骨架是NP-难的。然后，我们说明了适应度景观分析的骨干可以很好地近似局部最优解的共享公共部分。因此，我们提出了一个近似骨干的多级算法（ABMA），以解决大规模的NRP实例。该算法通过多级约简和精化迭代地探索搜索空间。实验结果表明，ABMA优于现有的算法在大型实例的解决方案的质量和运行时间。
英文摘要：
The next release problem (NRP) aims to effectively select software requirements in order to acquire maximum customer profits. As an NP-hard problem in software requirement engineering, NRP lacks efficient approximate algorithms for large scale instances. The backbone is a new tool for tackling large scale NP-hard problems in recent years. In this paper, we employ the backbone to design high performance approximate algorithms for large scale NRP instances. Firstly we show that it is NP-hard to obtain the backbone of NRP. Then, we illustrate by fitness landscape analysis that the backbone can be well approximated by the shared common parts of local optimal solutions. Therefore, we propose an approximate backbone based multilevel algorithm (ABMA) to solve large scale NRP instances. This algorithm iteratively explores the search spaces by multilevel reductions and refinements. Experimental results demonstrate that ABMA outperforms existing algorithms on large instances in terms of solution quality and running time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
