Many-task　differential　evolutionary　algorithm　is　an　effective　way　to　optimize　multiple　tasks　simultaneously.　The　optimization　performance　of　the　algorithm　decreases　due　to　the　negative　transfer　when　the　number　of　tasks　is　large.　To　address　this　problem,　a　many-task　differential　evolutionary　algorithm　based　on　bi-space　similarity　(MaTDE-BSS)　is　proposed　to　improve　the　positive　transfer.　First,　the　bi-space　similarity　metric　is　designed　to　characterize　inter-task　similarity　quantitatively.　The　decision　space　similarity　and　objective　space　similarity　are　considered　simultaneously　in　the　bi-space　similarity　metric.　Second,　a　task　selection　strategy　based　on　evolutionary　state　is　proposed　to　select　the　optimal　source　task　from　the　source　task　library　accurately.　The　source　task　library　based　on　bi-space　similarity　metric　is　built　for　storing　source　tasks.　Finally,　a　dynamic　knowledge　transfer　strategy　is　proposed　to　improve　the　efficiency　of　knowledge　positive　transfer　in　the　many-task　optimization.　Parameters　of　the　knowledge　transfer　strategy　are　adjusted　according　to　bi-space　similarity　metric　adaptively.　In　addition,　the　experimental　results　show　that　MaTDE-BSS　is　able　to　evaluate　the　inter-task　similarity　more　comprehensively.　And　MaTDE-BSS　is　more　competitive　compared　to　other　many-task　evolutionary　algorithms.　IEEE