摘要：In order to address the weakness of particle swarm optimization’s tendency to easily fall into local optimum in solving large scale combinational optimization problem, considering the balance that inertia can control between local search ability and global search ability, the paper proposed an improved hybrid particle swarm optimization algorithm (PSO) by adopting the self-adaptive inertia weight model and local search strategy of simulated annealing algorithm. Not only increases the variety of particles according to their distance to global optimum, but also enhances the local search ability of the algorithm. The Traveling Salesman Problem (TSP) is adopted to validate the efficiency of the proposed algorithm. By comparing with inertia weight linear decreasing particle swarm optimization, adaptive inertia weight particle swarm optimization and simulated annealing (SA) algorithm, experiments demonstrate that our method has a more promising results, proves it a more efficient modified algorithm
