## 2 Background  
### An Overview of GDM-MOP Literature  
In the GDM-MOP field, some approaches obtain a representative Pareto sample and then apply a method to aggregate individual preferences in a model of collective preference, which is used to find a final solution (e.g., [22; 23]). Other popular approaches propose an integration of group preference handling with interactive procedures of multi-objective optimization (e.g., [24; 25; 26]). In some methods, the interaction is performed during the optimization process. In other methods, the interaction is performed once a Pareto sample has been generated (e.g., [27]). [PERSON] et al. in [28] developed a decision support system for e-democracy inspired on Pareto frontier visualization, goal identification, and arbitration. Collective intelligence methods to aggregate reference points from different DMs have been proposed in ([29; 30; 31; 32]) to approach the region of the Pareto frontier that is more preferred by the DMs. [PERSON] et al. (2013) proposed a negotiation support system that includes the DMs' preferences through reference points; priorities on the set of DMs are characterized by weights; the output of a negotiation round is a single group reference point. [PERSON] et al. (2013) introduced fuzzy reference points; this approach contributes to improving the robustness of the final solution dealing with imprecise and changing preferences. [PERSON] and [PERSON] in [33] search for good consensus solutions by maximizing, (respectively, minimizing), the number of group members who are satisfied (resp. dissatisfied) with the current solution in the decision variable space. To solve the bi-objective optimization problem, they used the NSGA2 evolutionary algorithm. The DMs are declared as (dis)satisfied by comparing the current solution with their best compromise. The NEMO-GROUP, a set of interactive evolutionary multi-objective optimization (MOO) methods, was developed by [PERSON] and [PERSON] in [34]. In these approaches, an evolutionary algorithm is modified with the introduction of pairwise comparisons of several DMs. Solutions are evaluated using utilitarian and egalitarian additive group value functions; the evolutionary algorithms accept weights assigned to the DMs. [PERSON] and [PERSON] in [35] presented a two-step placement algorithm, which combines MOO and GDM. First, MOO is used to identify a set of design alternatives for object placement. In the second step, business intelligence and group decision making are used to evaluate design alternatives. A particular application of MOO is the distributed engineering design, the complex systems built in this area are getting more popularity (e.g., [36]). These systems are often designed by a group of DMs; the design of each subsystem is in charge of a DM. The design teams only have partial information about the global design, and negotiations are relevant to reach a consensus. [PERSON] and [PERSON] in [37] developed a mathematical model of the problem considering the aspect of distribution and decomposition. The solution of the model is based on Lagrangian relaxation. An important case of GDM-MOPs is group multi-objective project portfolio optimization. Project portfolio selection is one of the most important problems faced by top-level managers in large enterprises and public organizations. Often, projects are described by several (or many) conflicting criteria, and the selection of the \"best\" portfolio should be made by a collective entity. This group may be composed of experts in different/complementary fields or members of the top level management. The group members share an interest in finding the portfolio that could bring the best results for the organization, but they have different value systems, beliefs, preferences on conflicting criteria, and judgments about resource consumption and availability. Individual preferences and beliefs are typically modeled by certain parameters and values in a decision model; they can significantly differ from different DMs.