Multiobjective　evolutionary　algorithms　(MOEAs)　have　received　much　attention　in　multiobjective　optimization　in　recent　years　due　to　their　practicality.　With　limited　computational　resources,　most　existing　MOEAs　cannot　efficiently　solve　large-scale　multiobjective　optimization　problems　(LSMOPs)　that　widely　exist　in　the　real　world.　This　paper　innovatively　proposes　a　dual　decomposition　strategy　(DDS)　that　can　be　embedded　into　many　existing　MOEAs　to　improve　their　performance　in　solving　LSMOPs.　Firstly,　the　outer　decomposition　uses　a　sliding　window　to　divide　large-scale　decision　variables　into　overlapped　subsets　of　small-scale　ones.　A　small-scale　multiobjective　optimization　problem　(MOP)　is　generated　every　time　the　sliding　window　slides.　Then,　once　a　small-scale　MOP　is　generated,　the　inner　decomposition　immediately　creates　a　set　of　global　direction　vectors　to　transform　it　into　a　set　of　single-objective　optimization　problems　(SOPs).　At　last,　all　SOPs　are　optimized　by　adopting　a　block　coordinate　descent　strategy,　ensuring　the　solution＇s　integrity　and　improving　the　algorithm＇s　performance　to　some　extent.　Comparative　experiments　on　benchmark　test　problems　with　seven　state-of-the-art　evolutionary　algorithms　and　a　deep　learning-based　algorithm　framework　have　shown　the　remarkable　efficiency　and　solution　quality　of　the　proposed　DDS.　Meanwhile,　experiments　on　two　real-world　problems　show　that　DDS　can　achieve　the　best　performance　beyond　at　least　one　order　of　magnitude　with　up　to　3072　decision　variables.