The multidimensional hybrid indices optimization problem is a kind of uncertainty multi-objective optimization problems that is difficult to solve. First

we can get relevant weights by optimizing the main parameters of explicit and implicit indices. According to these weights

multidimensional explicit indices can be reduced to an equivalent-interval fitness

and multidimensional implicit indices can be reduced to an equivalent-fuzzy fitness. Equivalent-interval fitness and equivalent-fuzzy fitness can be synthesized to an equivalent-index body. Then

we select advantage individual on the basis of equivalent-index bodies dominant situation according to adaptive reference point and preference area size. Finally

we adopt an implicit-indices estimation strategy with cluster method to realize interactive evolutionary algorithm within the framework of NSGA-II. The proposed algorithm is applied to two optimization problems with hybrid indices

and the results validate its efficiency and generalization.