Goal-constraint: Incorporating preferences through an evolutionary ε-constraint based method

This paper presents the goal-constraint method for incorporating preferences in multiobjective optimization. The preferences are provided in the form of a vector of goals, which is familiar for decision makers and operations researchers. The portion of the Pareto front to be generated is totally defined by the vector of goals, regardless if such a vector is feasible or not. Once defined, it is feasible to experiment on many objective problems, because of the reduced cost of producing less points. The experimental results show good convergence properties, and the graphs illustrate the way the portion of front produced is related to the vector of goals.