  Evolutionary transfer multiobjective optimization (ETMO) has been becoming a
hot research topic in the field of evolutionary computation, which is based on
the fact that knowledge learning and transfer across the related optimization
exercises can improve the efficiency of others. Besides, the potential for
transfer optimization is deemed invaluable from the standpoint of human-like
problem-solving capabilities where knowledge gather and reuse are instinctive.
To promote the research on ETMO, benchmark problems are of great importance to
ETMO algorithm analysis, which helps designers or practitioners to understand
the merit and demerit better of ETMO algorithms. Therefore, a total number of
40 benchmark functions are proposed in this report, covering diverse types and
properties in the case of knowledge transfer, such as various formulation
models, various PS geometries and PF shapes, large-scale of variables,
dynamically changed environment, and so on. All the benchmark functions have
been implemented in JAVA code, which can be downloaded on the following
website: https://github.com/songbai-liu/etmo.
