Approximate Quality Criteria for Difficult Multi-Objective Optimization Problems

This paper introduces approximate analytic quality criteria useful in assessing the efficiency of evolutionary multi-objective optimization (EMO) procedures. We present a summary of extensive research into computing. In the performed comparative study we take into account the various approaches of the state-of-the-art, in order to objectively assess the EMO performance in highly dimensional spaces; where some executive criteria, such as those based on the true Pareto front, are difficult to calculate. Whereas, on the other hand, the proposed approximated quality criteria are easy to implement, computationally inexpensive, and sufficiently effective.