To　obtain　the　optimal　solutions　of　the　multi-objective　differential　evolution　algorithm,　an　adaptive　multi-objective　differential　evolution(AMODE)　algorithm　based　on　the　dynamic　parameters　adjustment　strategies　is　developed,　in　which　the　adaptive　adjustment　strategies　are　designed　to　select　the　scaling　factor　and　crossover　rate.　Then,　the　suitable　scaling　factor　and　crossover　rate　can　be　calculated　in　the　mutation　and　crossover　processes　to　balance　the　local　search　and　the　global　exploration　abilities　of　the　multi-objective　differential　evolution　algorithm.　Thus,　the　integrity　and　uniformity　optimal　solutions　are　able　to　be　obtained　in　the　evolutionary　process.　The　experimental　results　show　that　this　proposed　AMODE　algorithm　has　a　better　effect　to　improve　the　inverted　generational　distance(IGD)　and　spacing(SP).　©　2017,　Editorial　Office　of　Control　and　Decision.　All　right　reserved.