  Dominance move (DoM) is a binary quality indicator that can be used in
multi-objective and many-objective optimization to compare two solution sets
obtained from different algorithms. The DoM indicator can differentiate the
sets for certain important features, such as convergence, spread, uniformity,
and cardinality. DoM does not use any reference, and it has an intuitive and
physical meaning, similar to the $\epsilon$-indicator, and calculates the
minimum total move of members of one set so that all elements in another set
are to be dominated or identical to at least one member of the first set.
Despite the aforementioned properties, DoM is hard to calculate, particularly
in higher dimensions. There is an efficient and exact method to calculate it in
a bi-objective case only. This work proposes a novel approach to calculate DoM
using a mixed integer programming (MIP) approach, which can handle sets with
three or more objectives and is shown to overcome the $\epsilon$-indicator's
information loss. Experiments, in the bi-objective space, are done to verify
the model's correctness. Furthermore, other experiments, using 3, 5, 10, 15,
20, 25 and 30-objective problems are performed to show how the model behaves in
higher-dimensional cases. Algorithms, such as IBEA, MOEA/D, NSGA-III, NSGA-II,
and SPEA2 are used to generate the solution sets (however any other algorithms
can also be used with the proposed MIP-DoM indicator). Further extensions are
discussed to handle certain idiosyncrasies with some solution sets and also to
improve the quality indicator and its use for other situations.
