A control parameterization-based particle swarm optimization (CP-PSO) approach is presented which combines control parameterization with particle swarm optimization to solve dynamic optimization problems in chemical engineering. To improve search efficiency and convergence rate, a control parameterization-based adaptive particle swarm optimization (CP-APSO) approach is proposed, in which inertia weight and acceleration coefficients are updated according to population distribution characteristics. Three benchmark chemical dynamic optimization problems are explored as illustration. The results demonstrate that CP-APSO is efficient for solving a general class of chemical dynamic optimization problems and CP-APSO largely outperforms CP-PSO on the convergence rate.