The　problem　of　cooperative　task　allocation　for　multi-UAVs　in　rescue　scenarios　is　studied.　Considering　the　different　types　of　assistance　required　by　survivors,　a　more　practical　combinatorial　optimization　model　is　established,　and　an　adaptive　genetic　learning　particle　swarm　optimization　(AGLPSO)　algorithm　is　proposed　for　this　model.　Firstly,　according　to　the　rescue　relationship　between　UAVs　and　survivors,　a　real　vector　coding　mechanism　is　adopted　to　deal　with　the　constraints　of　decision　variables　to　simplify　the　solution　of　the　model.　Then,　the　search　ability　of　the　algorithm　is　improved　through　the　two　cascading　layers.　In　the　first　layer,　the　genetic　learning　strategy　is　used　to　generate　elite　particles　with　high　quality,　and　the　evolutionary　stagnation　particles　are　updated　by　the　elite　learning　strategy　to　jump　out　of　local　optimum.　In　the　second　layer,　the　search　direction　of　the　population　is　guided　by　the　elite　particles,　and　according　to　the　evolution　speed　of　particle　swarm　and　aggregation　degree　of　particles,　the　adaptive　evolution　strategy　is　used　to　improve　the　searching　ability　of　the　algorithm　in　different　evolutionary　periods.　The　simulation　results　show　that　the　proposed　AGLPSO　algorithm　can　quickly　and　effectively　find　a　reasonable　rescue　allocation　scheme.　©　2023　Northeast　University.　All　rights　reserved.