论文标题
ETO满足调度：学习关键知识从单目标问题到多目标问题
ETO Meets Scheduling: Learning Key Knowledge from Single-Objective Problems to Multi-Objective Problem
论文作者
论文摘要
进化转移优化（ETO）是“进化计算研究中的新领域”，该研究将避免在传统进化计算中解决的经验和知识零重复使用。在通过ETO进行调度应用程序时，它们之间的竞争性“会议”框架可能构成智能调度和绿色调度，尤其是在中国背景下的碳中立性。据我们所知，当多目标问题“遇到”组合案例中的单目标问题（不是多任务优化）时，我们在此处进行的研究是ETO进行复杂优化的第一项工作。更具体地说，可以学习并转移置换流程调度问题（PFSP）之类的关键知识，例如位置构建块，可以学习并转移。关于良好基准的实证研究证明了我们提出的ETO-PFSP框架的相对牢固的有效性和巨大的潜力。
Evolutionary transfer optimization(ETO) serves as "a new frontier in evolutionary computation research", which will avoid zero reuse of experience and knowledge from solved problems in traditional evolutionary computation. In scheduling applications via ETO, a highly competitive "meeting" framework between them could be constituted towards both intelligent scheduling and green scheduling, especially for carbon neutrality within the context of China. To the best of our knowledge, our study on scheduling here, is the 1st work of ETO for complex optimization when multiobjective problem "meets" single-objective problems in combinatorial case (not multitasking optimization). More specifically, key knowledge like positional building blocks clustered, could be learned and transferred for permutation flow shop scheduling problem (PFSP). Empirical studies on well-studied benchmarks validate relatively firm effectiveness and great potential of our proposed ETO-PFSP framework.
