  Experienced users often have useful knowledge and intuition in solving
real-world optimization problems. User knowledge can be formulated as
inter-variable relationships to assist an optimization algorithm in finding
good solutions faster. Such inter-variable interactions can also be
automatically learned from high-performing solutions discovered at intermediate
iterations in an optimization run - a process called innovization. These
relations, if vetted by the users, can be enforced among newly generated
solutions to steer the optimization algorithm towards practically promising
regions in the search space. Challenges arise for large-scale problems where
the number of such variable relationships may be high. This paper proposes an
interactive knowledge-based evolutionary multi-objective optimization (IK-EMO)
framework that extracts hidden variable-wise relationships as knowledge from
evolving high-performing solutions, shares them with users to receive feedback,
and applies them back to the optimization process to improve its effectiveness.
The knowledge extraction process uses a systematic and elegant graph analysis
method which scales well with number of variables. The working of the proposed
IK-EMO is demonstrated on three large-scale real-world engineering design
problems. The simplicity and elegance of the proposed knowledge extraction
process and achievement of high-performing solutions quickly indicate the power
of the proposed framework. The results presented should motivate further such
interaction-based optimization studies for their routine use in practice.
