Evolutionary　multitasking　optimization　(EMTO)　has　capability　of　performing　a　population　of　individuals　together　by　sharing　their　intrinsic　knowledge.　However,　the　existed　methods　of　EMTO　mainly　focus　on　improving　its　convergence　using　parallelism　knowledge　belonging　to　different　tasks.　This　fact　may　lead　to　the　problem　of　local　optimization　in　EMTO　due　to　unexploited　knowledge　on　behalf　of　the　diversity.　To　address　this　problem,　in　this　article,　a　diversified　knowledge　transfer　strategy　is　proposed　for　multitasking　particle　swarm　optimization　algorithm　(DKT-MTPSO).　First,　according　to　the　state　of　population　evolution,　an　adaptive　task　selection　mechanism　is　introduced　to　manage　the　source　tasks　that　contribute　to　the　target　tasks.　Second,　a　diversified　knowledge　reasoning　strategy　is　designed　to　capture　the　knowledge　of　convergence,　as　well　as　the　knowledge　associated　with　diversity.　Third,　a　diversified　knowledge　transfer　method　is　developed　to　expand　the　region　of　generated　solutions　guided　by　acquired　knowledge　with　different　transfer　patterns　so　that　the　search　space　of　tasks　can　be　explored　comprehensively,　which　is　favor　of　EMTO　alleviating　local　optimization.　Finally,　the　performance　of　the　proposed　algorithm　is　evaluated　in　comparison　with　some　other　state-of-the-art　EMTO　algorithms　on　multiobjective　multitasking　benchmark　test　suits,　and　the　practicality　of　the　algorithm　is　verified　in　a　real-world　application　study.　The　results　of　experiments　demonstrate　the　superiority　of　DKT-MTPSO　compared　to　other　algorithms.