ETO 遇见调度：从单目标问题学习关键知识到多目标问题
ETO Meets Scheduling: Learning Key Knowledge from Single-Objective Problems to Multi-Objective Problem
AI总结：
本研究首次将进化迁移优化（ETO）应用于组合情形下的多目标与单目标调度问题，通过学习并迁移位置构建块等关键知识解决置换流水车间调度问题，提出ETO-PFSP框架并在基准上验证了其有效性与潜力。
摘要：
进化迁移优化（ETO）是“进化计算研究的新前沿”，能避免传统进化计算对已解决问题经验和知识的零复用。通过ETO的调度应用，可构建极具竞争力的“相遇”框架，以实现智能调度与绿色调度，尤其适用于中国背景下的碳中和。据我们所知，本研究是组合情形下（非多任务优化）多目标问题“遇见”单目标问题时ETO用于复杂优化的首项工作。具体而言，可学习并迁移聚类位置构建块等关键知识，以解决置换流水车间调度问题（PFSP）。在充分研究的基准上的实证研究，验证了所提ETO-PFSP框架相对稳健的有效性与巨大潜力。
英文摘要：
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.
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。
