Exploring Constraint Handling Techniques in Real-world Problems on
  MOEA/D with Limited Budget of Evaluations
  Finding good solutions for Multi-objective Optimization (MOPs) Problems is
considered a hard problem, especially when considering MOPs with constraints.
Thus, most of the works in the context of MOPs do not explore in-depth how
different constraints affect the performance of MOP solvers. Here, we focus on
exploring the effects of different Constraint Handling Techniques (CHTs) on
MOEA/D, a commonly used MOP solver when solving complex real-world MOPs.
Moreover, we introduce a simple and effective CHT focusing on the exploration
of the decision space, the Three Stage Penalty. We explore each of these CHTs
in MOEA/D on two simulated MOPs and six analytic MOPs (eight in total). The
results of this work indicate that while the best CHT is problem-dependent, our
new proposed Three Stage Penalty achieves competitive results and remarkable
performance in terms of hypervolume values in the hard simulated car design
MOP.
