Sparrow　search　algorithm　has　the　problem　of　redundancy　of　convergence　speed　due　to　its　fast　convergence　speed,　and　it　is　easy　to　fall　into　local　optimum　in　multimodal　environment.　To　solve　the　above　problem,　this　paper　presents　a　personalized　multipopulation　sparrow　search　algorithm　(MPSSA).　By　introducing　multiple　population　mechanisms　to　reduce　the　probability　of　falling　into　the　local　optimum　due　to　single-population　search,　by　using　a　personalized　subpopulation　strategy　to　improve　the　personalized　differences　of　subpopulations　and　balance　the　exploratory　ability　of　algorithm　development,　then　by　using　weighted　center-of-gravity　communication　strategy　to　improve　the　quality　of　communication　between　populations,　and　finally　by　using　dimension　by　dimension　dynamic　reverse　learning　to　improve　the　accuracy　of　search.　The　superiority　of　MPSSA　is　validated　by　comparing　the　benchmark　function　and　CEC2017.　Finally,　the　algorithm　solves　the　problem　of　poor　quality　due　to　the　dimension　increase　of　the　UAV　cooperative　track.　MPSSA　helps　the　UAV　to　quickly　plan　a　better　and　stable　track　group　to　ensure　the　UAV　to　complete　the　cooperative　task　safely　and　stably.