In order to efficiently solve the dynamic and continuous optimization problems, a hierarchical PSO algorithm is
proposed. The solution space of a dynamic optimization problem is divided into ?? subspaces. One particle swarm is assigned
to each subspace to search independently as the first layer. Then the best particles in the ?? particle swarms as the second
layer search the global domain so as to guide the other particles in each subspace. Moreover, an environment detection and
response strategy is proposed. Numerical experiments on the classical dynamic function demonstrate that the algorithm can
adapt dynamic environment and track a changing optimum quickly.