“…As a way to cope with constrained MOPs, many methods have been proposed ranging from the transformation of constraints into additional objectives to the incorporation of local search techniques and heuristics within EMOAs [10,11,12,13,14,15,16,17,18]. Nevertheless, many result in the inclusion of additional conditions for the problem to be solvable, by computing internal optimization problems or the calculation of gradients to obtain feasible solutions [14,19]. Moreover, in many situations, the existing methodologies and algorithms cannot achieve a full set of feasible solutions, some of them attaining no more than a 50% success rate [20].…”