  To solve complex real-world problems, heuristics and concept-based approaches
can be used in order to incorporate information into the problem. In this
study, a concept-based approach called variable functioning Fx is introduced to
reduce the optimization variables and narrow down the search space. In this
method, the relationships among one or more subset of variables are defined
with functions using information prior to optimization; thus, instead of
modifying the variables in the search process, the function variables are
optimized. By using problem structure analysis technique and engineering expert
knowledge, the $Fx$ method is used to enhance the steel frame design
optimization process as a complex real-world problem. The proposed approach is
coupled with particle swarm optimization and differential evolution algorithms
and used for three case studies. The algorithms are applied to optimize the
case studies by considering the relationships among column cross-section areas.
The results show that $Fx$ can significantly improve both the convergence rate
and the final design of a frame structure, even if it is only used for seeding.
