Particle swarm optimization(PSO)algorithm is often precocious in solving task assignment problems and cannot converge to the global optimal solution.In order to solve the above problems,a simulated annealing particle swarm algorithm with adaptive weight is proposed.An adaptive adjustment strategy of inertia weight changing with particle position is designed,and the learning mode of the particle is changed,which increases the chance of learning from the optimal position of other particles.Furthermore,simulated annealing is introduced into the particle swarm optimization speed updating formula to change the updating method of the global optimum,improve the diversity of the population,and make PSO jump out of local optimum quickly.By solving the cooperative reconnais-sance task allocation problem of multiple unmanned aerial vehicles,the results show that compared to the classical particle swarm optimization algorithm,the improved particle swarm optimization algorithm has faster convergence speed and stronger optimization ability,and is suitable for large-scale task allocation problem,and has a strong practi-cal value.
维普
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