Multi-objective　evolutionary　algorithms　(MOEAs)　have　shown　good　performance　in　many　complex　mathematics　benchmarks　and　real　applications　problems.　However,　MOEAs　still　have　some　challenges　in　the　big　data　optimization　problem　with　thousands　of　variables.　In　previous　studies,　an　adaptive　neighbourhood　adjustment　strategy　on　MOEA/D　had　a　good　optimization　performance　in　CEC2009　competition　test　instances,　in　which　variables　had　high　dimensions.　In　this　paper,　an　enhanced　adaptive　neighbourhood　adjustment　strategy　on　MOEA/D,　called　MOEA/D-EANA,　has　been　proposed　for　2015　EEG　signal　decomposition-based　big　data　optimization.　It　combines　fitness-rate-rank　based　multi-armed　bandit　operators　(FRRMAB)　selection　strategies　with　the　advantages　of　adaptive　neighbourhood　adjustment　strategies.　These　operators　enhance　the　diversity　of　solutions　in　our　previous　algorithm.　A　set　of　big　data　optimization　problems,　including　six　single　objective　problems　and　six　multi-objective　problems,　are　tested　in　the　experiments.　Computational　results　show　that　our　proposed　algorithm　achieves　promising　performance　on　all　test　problems.　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.