To solve the problems that it is diffcult to obtain multiple alternative unmanned aerial vehicle(UAV)path planning schemes in complex obstacle environments

the construction of a multi-objective UAV path planning model and its solving algorithm are explored.In terms of model design

the flight distance and threat cost of the UAV are taken as the performance indicators

and the flight space range

steering angle

and climb angle are regarded as constraints.Subsequently

to address the problems of poor uniformity of initial population distribution and failure to effectively balance global search and local exploitation in the multi-objective salp swarm algorithm(MSSA)

the Hammersley sequence is used to enhance the uniformity of the initial population distribution.At the same time

a new leader-follower update strategy and an adaptive hybrid strategy are established to enhance the population optimization capability

thus obtaining an improved MSSA(IMSSA).Experiments show that the proposed algorithm has obvious advantages in solution performance and can effectively obtain multiple alternative path planning schemes for UAVs.