Summary: Ranking strategies among interval values are more critical for obtaining superior Pareto front with better spread, distribution and approximation. Most current interval evolutionary multi-objective optimizations (EMOs) adopt only one interval ranking method, which is difficult to entirely cover the interval information. Accordingly, an interval EMO with the improved hybrid ranking strategy is proposed, in which two different interval comparison metrics, i.e., \(\mu\) and \(P\) are complements. Then, Pareto dominance for \(\mu\oplus P\) ranking is defined and employed to the powerful NSGA-II algorithm for optimizing interval multi-objective problems. The proposed algorithm is applied to benchmark functions and then further to RFID location in underground mine circumstances, and its outstanding performance is experimentally demonstrated.