Dynamic　multi-objective　problems　(DMOPs)　have　aroused　extensive　attention　in　recent　years.　Prediction-based　methods　have　been　proven　to　be　effective.　However,　most　existing　methods　assume　the　linear　relationships　between　historical　solutions.　For　real-life　systems,　ignoring　the　complex　nonlinear　relationships　between　historical　environments　may　result　in　low　prediction　accuracy.　To　solve　this　problem,　the　echo　state　network　(ESN)　based　prediction　approach　is　proposed　for　DMOPs.　First,　the　reservoir　of　ESN　is　used　to　express　the　input　dynamics　of　the　historical　solutions　to　explore　the　linear　or　nonlinear　relationships　among　historical　solutions.　Then,　a　fractal　interpolation　technique　(FIT)　is　introduced　to　enrich　the　training　data　while　preserving　the　original　time　series　features　as　much　as　possible.　The　final　experimental　results　show　that　the　designed　algorithm　can　solve　the　dynamic　multi-objective　optimization　problems　effectively.　©　2022　IEEE.