Evolutionary multi-objective optimization (EMO) algorithms generate solution sets representing trade-offs between conflicting objectives. The comparison between two EMO algorithm performances is challenging for real-world problems lacking reference Pareto fronts (PFs). Most performance indicators require a reference PF or point to compare the Pareto-optimal solutions. This study uses Dominance Move (DoM), formulated as a Mixed Integer Programming (MIP), as it compares sets without any reference PF or points and avoids information loss. The GD+, IGD+, HV, \(\epsilon \) , and MIP-DoM indicators have been used to compare solution sets from two popular EMO algorithms—NSGA-II and MOPSO. EMO algorithms are used to minimize power consumption and maximize stability for a 25-DOF humanoid robot gait optimization problem. The results show that NSGA-II outperforms MOPSO on this highly constrained problem. The MIP-DoM exhibits the strongest correlation with the IGD+ indicator, whereas weaker correlations are seen for the Hypervolume and Epsilon indicators. The EMO performance has also been tracked over generations using IGD+, which provides additional insight into algorithm dynamics. The proposed techniques could be extended to other real-world optimization problems.