  Transfer Optimization has gained a remarkable attention from the Swarm and
Evolutionary Computation community in the recent years. It is undeniable that
the concepts underlying Transfer Optimization are formulated on solid grounds.
However, evidences observed in recent contributions confirm that there are
critical aspects that are not properly addressed to date. This short
communication aims to engage the readership around a reflection on these
issues, and to provide rationale why they remain unsolved. Specifically, we
emphasize on three critical points of Evolutionary Multitasking Optimization:
i) the plausibility and practical applicability of this paradigm; ii) the
novelty of some proposed multitasking methods; and iii) the methodologies used
for evaluating newly proposed multitasking algorithms. As a result of this
research, we conclude that some important efforts should be directed by the
community in order to keep the future of this promising field on the right
track. Our ultimate purpose is to unveil gaps in the current literature, so
that prospective works can attempt to fix these gaps, avoiding to stumble on
the same stones and eventually achieve valuable advances in the area.
