Approximate Criteria for the Evaluation of Truly Multi-Dimensional Optimization Problems

In this paper we propose new improved approximate quality criteria useful in assessing the efficiency of evolutionary multi-objective optimization (EMO). In the performed comparative study we take into account the various EMO algorithms of the state-of-the-art, in order to objectively assess the EMO performance in highly dimensional spaces. It is well known that useful executive criteria, such as those based on the true Pareto front in highly multidimensional spaces, can be tedious or even impossible to calculate. On the other hand, the proposed synthetic quality criteria are easy to implement, computationally inexpensive, and sufficiently informative and effective.