Comparison of multi-objective optimization methodologies for engineering applications

Computational models describing the behavior of complex physical systems are often used in the engineering design field to identify better or optimal solutions with respect to previously defined performance criteria. Multi‐objective optimization problems arise and the set of optimal compromise solutions (Pareto front) has to be identified by an effective and complete search procedure in order to let the decision maker, the designer, to carry out the best choice. Four multi‐objective optimization techniques are analyzed by describing their formulation, advantages and disadvantages. The effectiveness of the selected techniques for engineering design purposes is verified by comparing the results obtained by solving a few benchmarks and a real structural engineering problem concerning an engine bracket of a car.