Task　similarity　is　a　major　requisite　to　trigger　knowledge　sharing　in　evolutionary　multitasking　optimization　(EMTO).　Unfortunately,　most　of　the　existing　EMTO　algorithms　only　focus　on　the　similarity　between　population　distributions　of　tasks,　but　ignore　the　search　behavior　of　populations,　which　may　degrade　the　performance　of　cross-task　knowledge　sharing.　Motivated　by　this,　an　improved　EMTO　algorithm　with　similarity　evaluation　of　search　behavior　(SESB-IEMTO),　employing　the　particle　swarm　optimization　(PSO)　algorithm　as　a　task　solver　for　each　task,　is　proposed.　It　comprises　three　key　elements:　1)　a　dynamic　similarity-based　evaluation　strategy,　2)　a　cross-task　knowledge　adaptation　method,　and　3)　a　search　direction　sharing　mechanism.　Primarily,　the　source　tasks　with　similar　search　behavior　are　discriminated　with　the　dynamic　similarity-based　evaluation　strategy,　where　individuals　can　be　fully　exploited　for　cross-task　evolution.　Then,　the　knowledge　derived　from　these　source　tasks　is　regulated　by　the　cross-task　knowledge　adaption　method　for　alleviating　the　risk　of　negative　transfer　caused　by　the　heterogeneity　between　tasks.　Moreover,　to　further　promote　knowledge　sharing　between　tasks,　the　search　direction　sharing　mechanism　is　developed　to　navigate　tasks　efficiently　searching　for　promising　regions.　Finally,　the　convergence　of　SESB-IEMTO　is　analyzed,　and　the　effectiveness　and　superiority　are　also　verified　with　the　experiments　on　several　benchmark　tests　and　a　real-world　application　study.　IEEE