Systematic framework for solving real-world problems with multiple objectives

A systematic framework for solving multiobjective optimization problems (MOPs) is proposed. A physical problem with multiple objectives is first transformed into an MOP. Multiobjective evolutionary algorithms are then applied. By solving the MOP, a set of Pareto optimal solutions and an approximate Pareto front can be obtained. To select a final solution, multicriteria decision making approaches are employed. This procedure contributes to a systematic approach that can address real-world problems with multiple objectives.