Multitask　optimization　(MTO)　mainly　utilizes　knowledge　transfer　among　tasks　to　address　multiple　optimization　problems　in　parallel.　However,　the　decision　space　dimensions　of　different　tasks　often　differ,　which　leads　to　the　failure　of　knowledge　transfer.　Therefore,　it　is　a　challenging　problem　to　transfer　knowledge　among　tasks　with　different　dimensions　to　achieve　parallel　optimization　of　multiple　tasks.　To　address　this　problem,　multitask　particle　swarm　optimization　with　a　dynamic　transformation　strategy　(MTPSO-DTS)　is　proposed　to　improve　the　performance　of　MTO.　First,　an　intertask　similarity　index　based　on　gradient　information　and　location　information　is　designed　to　dynamically　assess　the　degree　of　similarity　among　different　tasks.　Then,　the　intertask　similarity　is　determined　to　assist　the　transformation　of　dimensions.　Second,　a　dynamic　transformation　strategy　based　on　intertask　similarity　is　developed　to　achieve　a　uniform　representation　of　different　dimension　knowledge,　including　dimension　supplementation　and　dimension　reduction.　Then,　the　MTPSO-DTS　algorithm,　which　takes　advantage　of　the　search　characteristics　of　particle　swarm　optimization,　can　facilitate　knowledge　transfer　among　tasks　with　different　dimensions.　Third,　the　convergence　of　the　MTPSO-DTS　algorithm　is　analyzed　to　verify　the　validity　theoretically.　Finally,　numerical　comparison　experiments　on　benchmark　problems　and　a　practical　application　are　carried　out　to　demonstrate　the　effectiveness　of　the　MTPSO-DTS　algorithm.　The　results　indicate　that　the　proposed　MTPSO-DTS　algorithm　can　facilitate　knowledge　transfer　among　tasks　of　different　dimensions　to　promote　parallel　optimization　of　multiple　tasks.