In　the　last　decade,　the　decomposition-based　multiobjective　optimization　evolutionary　algorithm　(MOEA/D)　has　displayed　promising　performance　when　dealing　with　multiobjective　optimization　problems　(MOPs).　However,　for　some　complex　MOPs,　the　conventional　MOEA/D　often　leads　to　the　loss　of　population　diversity　in　the　iterative　process,　and　the　convergence　performance　of　the　population　is　weakened　in　the　mean　time.　In　this　paper,　a　novel　MOEA/D,　based　on　improved　multiple　adaptive　dynamic　selection　strategies　and　elite　archive　strategy　(MOEA/D-IMA),　is　proposed　to　improve　the　population　diversity　and　convergence.　First,　a　novel　differential　evolution　(DE)　operator　is　constructed,　which　constitutes　an　operator　pool　with　other　DE　operators.　According　to　the　search　information　of　the　current　population,　an　adaptive　dynamic　selection　strategy　is　proposed,　which　is　used　by　MOEA/D-IMA　to　select　a　suitable　DE　operator　to　replace　the　simulated　binary　crossover　(SBX)　operator.　Second,　a　parameter　adaptive　dynamic　selection　strategy　is　proposed　to　enhance　the　robustness　of　MOEA/D-IMA　by　using　the　information　of　population　evolution　state.　Third,　an　elite　archive　strategy　is　introduced　to　improve　the　convergence　and　diversity　of　the　population　where　mutual　dominance　of　individuals　and　their　aggregation　distance　is　employed.　Finally,　the　proposed　MOEA/D-IMA　is　compared　with　several　state-of-the-art　algorithms　on　three　suits　of　18　test　problems.　Experimental　results　indicate　that　the　proposed　MOEA/D-IMA　can　significantly　improve　the　optimization　performance　when　coping　with　MOPs.　©　2020　Elsevier　Inc.