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Multi-Objective Parameter-less Population Pyramid for Solving Industrial Process Planning Problems
arXiv - CS - Neural and Evolutionary Computing Pub Date : 2020-09-10 , DOI: arxiv-2009.08929
Michal Witold Przewozniczek, Piotr Dziurzanski, Shuai Zhao, Leandro Soares Indrusiak

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.

中文翻译:

解决工业过程规划问题的多目标无参数人口金字塔

在解决实际困难的问题时,进化方法是获得高质量结果的有效工具。链接学习可能会提高其有效性。采用链接学习的最先进方法之一是无参数人口金字塔 (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 具有很强的竞争力,这在实际问题中很常见。在本文中,我们考虑了一个来自实践的多目标工业过程规划问题,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,对于所考虑的实际问题和典型的多目标基准,我们的提议优于竞争方法。
更新日期:2020-09-21
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