  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.
