Evolutionary　multitasking　optimization　(EMTO),　owing　to　its　advantage　of　knowledge　sharing,　is　capable　of　resolving　multiple　optimization　tasks　concurrently.　Considering　the　evolutionary　progresses　between　tasks　may　be　inconsistent,　it　is　necessary　for　EMTO　to　regulate　the　knowledge　transfer　strategy　(KTS),　which　can　alleviate　the　negative　transfer　caused　by　unmatched　knowledge.　Inspired　by　this,　a　multitasking　feedback　optimization　algorithm　is　proposed　with　an　evolutionary　state　estimator　(MTFO-ESE).　First,　a　multi-source　knowledge　acquisition　strategy　(MKA)　is　introduced　to　achieve　inter-task　knowledge,　which　promotes　the　tasks　to　seek　the　optimization　directions　in　the　search　space.　Second,　an　evolutionary　state　estimator　(ESE)　is　established　to　evaluate　the　search　progress　of　each　task　toward　the　optimal　solution.　The　main　idea　is　to　measure　the　evolutionary　pressure　of　the　population　under　the　current　individual　update　strategy　using　prior　and　posterior　observation.　Third,　a　double-feedback　adjustment　mechanism　(DFBA)　is　developed　to　manage　KTS　based　on　ESE.　This　mechanism　contributes　to　alleviating　the　negative　effect　caused　by　unmatched　knowledge　and　eliminating　unnecessary　exploration.　Moreover,　the　convergence　of　the　proposed　MTFO-ESE　is　analyzed　to　ensure　its　effectiveness.　Finally,　the　superior　convergence　and　positive　transfer　ability　of　the　proposed　algorithm　are　verified　through　comparative　experiments,　ablation　analyses,　and　a　practical　application.　IEEE