论文标题
用于解决工业过程计划问题的多目标无参数人群金字塔
Multi-Objective Parameter-less Population Pyramid for Solving Industrial Process Planning Problems
论文作者
论文摘要
进化方法是在解决严格的实际问题时获得高质量结果的有效工具。连锁学习可能会提高其有效性。采用连锁学习的最先进方法之一是无参数的人群金字塔（P3）。 P3致力于解决离散域中的单目标问题。最近的研究表明，在解决所谓的重叠块问题时，P3具有很高的竞争力，这对于实际问题而言是典型的。在本文中，我们考虑了一个多目标工业过程计划问题，该问题是由实践引起的，并且是NP-HARD。为了处理它，我们提出了P3的多目标版本。广泛的研究表明，我们的命题优于所考虑的实际问题和典型的多目标基准的竞争方法。
Evolutionary methods are effective tools for obtaining high-quality results when solving hard practical problems. Linkage learning may increase their effectiveness. One of the state-of-the-art methods that employ linkage learning is the Parameter-less Population Pyramid (P3). P3 is dedicated to solving single-objective problems in discrete domains. Recent research shows that P3 is highly competitive when addressing problems with so-called overlapping blocks, which are typical for practical problems. In this paper, we consider a multi-objective industrial process planning problem that arises from practice and is NP-hard. To handle it, we propose a multi-objective version of P3. The extensive research shows that our proposition outperforms the competing methods for the considered practical problem and typical multi-objective benchmarks.
