Reference point-based search scheme for multiobjective evolutionary algorithm

In multiobjective optimization problems, it is important to derive solutions with high accuracy, uniform distribution, and broadness. Here, we propose a two-phase search process to improve the accuracy and broadness of solutions. In the first phase, the accuracy of the solutions is improved, and a reference point specified by a decision maker is set and utilized for the search. In the second phase, the solutions are broadened using the Distributed Cooperation Scheme. The numerical results indicated that the proposed search scheme was capable of deriving broader solutions than the conventional multiobjective evolutionary algorithm without deterioration of accuracy.