Intelligent optimization methods are playing an increasing role in dynamic optimization in chemical engineering due to their simplicity and good global search ability. Unfortunately

traditional intelligent optimization methods often suffer from a relatively slow convergence rate. An iteratively adaptive particle swarm optimization (IAPSO) approach for dealing with general chemical dynamic optimization problems was proposed. A dynamic optimization problem was first converted into a nonlinear programming (NLP) problem through control parameterization (CP); then the proposed IAPSO approach was used to solve the NLP. The proposed IAPSO approach had a faster convergence rate than the conventional particle swarm optimization approach mainly for two reasons: IAPSO updated parameters adaptively according to population distribution characteristics; IAPSO executed adaptive particle swarm optimization algorithm iteratively by reducing search space to get a more accurate solution. Several benchmark dynamic optimization problems were explored as illustration and the results showed that the proposed IAPSO approach was simple

efficient

and considerably outperformed the conventional PSO method in terms of convergence rate.