Abstract: In the classical particle swarm optimization algorithm,a constant or linearly decreasing inertia weight was used for solving the optimization problem,but it could not solve the phenomenon of stagnation.A diversity-based inertia weight strategy and the activation of swarm in the particle swarm optimization were proposed.In each iteration process,the inertia weight and activation of swarm were changed dynamically,which benefits to the algorithm converging quickly to global optimal solution.According to the experimental results using six typical functions,the activation approach for the particle swarm optimization improves exploitation and exploration ability,but still keeps a rapid convergence and fine precision,and the nonlinear strategy for decreasing inertia weight of the particle swarm optimization is the most obvious.