Abstract: By considering maximum UAV load,track cost,task time deviation and task benefit to construct a task allocation model,this paper proposes an improved particle swarm optimization algorithm based on game theory to solve the multi-UAV cooperative task assignment problem(MTAP).The principle of the algorithm decodes the particles into feasible task sequences by real number encoding and deadlock repair,as well as establishes the mapping between particle vector and task sequences.By involving the evolutionary stability strategy of the evolutionary game theory in the particle swarm optimization,and by game operation,the game equilibrium point is obtained,which is utilized to adaptively adjust the control parameters of the standard particle swarm to balance the global and local search capabilities of the algorithm.This paper also proposes a strategy avoiding stuck in local convergence,by improving the individual optimal position vector of particles to achieve the effect of enhancing social cognition.Upon simulation analysis,as well as comparing with the existing algorithms,the proposed algorithm shows efficiency in the task allocation problem of multiple UAVs.

Key words: Multi-UAV, Task assignment, Evolutionary game theory, Standard particle swarm algorithm, Evolutionary stabilization strategy