一种自适应t分布扰动策略的粒子群算法OA

Aiming at the shortcomings that the basic particle swarm optimization algorithm is easy to fall into local optimiza-tion and the convergence speed is too slow in the process of optimization iteration,a particle swarm optimization algorithm with adap-tive t-distribution perturbation strategy is designed.Firstly,the inertia weight is adaptively adjusted according to the number of iter-ations and particle fitness,and the asymmetric learning factor is used to adjust the particle search process.Then,the adaptive t-dis-tribution perturbation strategy is introduced to perturb the position of the optimal solution to produce a new solution,so as to im-prove the diversity of the search space.Simulation results show that the improved particle swarm optimization algorithm has im-proved the convergence speed,convergence accuracy and the ability to get out of local optimization,and it has good robustness.