Several algorithms/techniques have been proposed and studied to solve such problems. With this context, these algorithms are assessed in one of two ways, viz. theoretical, and empirical analysis. In theoretical analysis, a principled methodology is carried out to derive an analytical bound of the (run-time) solution quality. After t evaluations/steps, the quality of the returned solution is evaluated by a loss/regret measure.

Alternatively, empirical analysis employs experimental simulations of the algorithm on complex problems, gaining an insight on the algorithm’s practicality/applicability on real-world problems. With this regard, most of the time, methods proposed to solve MOPs are benchmarked on a different set of problems under arbitrary budgets of function evaluation. We are interested in empirically assessing published/novel multi-objective optimization algorithms in a unified (constantly updated) framework.