Subspace　clustering　algorithms　have　shown　their　advantage　in　handling　high-dimensional　data　by　optimizing　a　linear　combination　of　clustering　criteria.　However,　setting　the　coefficients　of　these　criteria　items　without　prior　knowledge　will　lead　to　inaccurate　and　poor　robust　clustering　results.　To　address　this　problem,　in　this　paper,　we　propose　to　optimize　the　multiple　clustering　criteria　simultaneously　without　any　predefined　coefficients　by　a　multi-objective　evolutionary　algorithm.　Furthermore,　to　accelerate　the　convergence　of　the　algorithm,　we　provide　a　novel　local　search　method.　In　it,　the　multi-objective　clustering　problem　is　decomposed　into　many　localized　scalarizing　sub-problems　by　reference　vectors.　Solutions　are　then　locally　searched　around　their　associated　sub-problems.　Thirdly,　we　develop　a　knee-pruning　fuzzy　ensemble　method　for　selecting　the　final　solution.　This　method　applies　clustering　ensemble　in　solutions　selected　from　knee　regions　to　get　robust　results.　Experiments　on　UCI　benchmarks　and　gene　expression　datasets　show　that　our　proposed　algorithm　can　efficiently　handle　high-dimensional　clustering　problems　without　any　user-defined　coefficients.　(C)　2019　Elsevier　B.V.　All　rights　reserved.