Particle　swarm　optimization　algorithm　has　become　a　promising　approach　in　solving　multitask　optimization　(MTO)　problems　since　it　can　transfer　knowledge　with　easy　implementation　and　high　searching　efficiency.　However,　in　the　process　of　knowledge　transfer,　negative　transfer　is　common　because　it　is　difficult　to　evaluate　whether　knowledge　is　effective　for　population　evolution.　Therefore,　how　to　obtain　and　transfer　the　effective　knowledge　to　curb　the　negative　transfer　is　a　challenging　problem　in　MTO.　To　deal　with　this　problem,　a　self-adjusting　multitask　particle　swarm　optimization　(SA-MTPSO)　algorithm　is　designed　to　improve　the　convergence　performance　in　this　article.　First,　a　knowledge　estimation　metric,　combining　the　decision　space　knowledge　and　the　target　space　knowledge　for　each　task,　is　designed　to　describe　the　effectiveness　of　knowledge.　Then,　the　effective　knowledge　is　obtained　to　promote　the　knowledge　transfer　process.　Second,　a　self-adjusting　knowledge　transfer　mechanism,　based　on　the　effective　knowledge　and　the　self-adjusting　transfer　method,　is　developed　to　achieve　effective　knowledge　transfer.　Then,　the　ineffective　knowledge　is　removed　to　solve　the　negative　transfer　problem.　Third,　the　convergence　analysis　is　given　to　guarantee　the　effectiveness　of　the　SA-MTPSO　algorithm　theoretically.　Finally,　the　proposed　algorithm　is　compared　with　some　existing　MTO　algorithms.　The　results　show　that　the　performance　of　the　proposed　algorithm　is　superior　to　most　algorithms　on　negative　transfer　suppression　and　convergence.