Optimal Control Vector Parameterization Approach with a Hybrid Intelligent Algorithm for Nonlinear Chemical Dynamic Optimization Problems

Abstract

A control vector parameterization (CVP)-based hybrid algorithm, HAPSODSA-CVP, is proposed to solve the nonlinear chemical dynamic optimization problems, where adaptive particle swarm optimization (APSO) is applied to enhance the global search ability, while differential search algorithm (DSA) is used to improve the local exploitation ability. Three well-known classic nonlinear chemical dynamic optimization problems are tested as illustration, and detailed comparisons are carried out among PSO-CVP, APSO-CVP, and HAPSODSA-CVP approaches. The research results not only demonstrate the efficiency of the HAPSODSA-CVP approach for this kind of dynamic optimization problems, but also its superiority to both APSO-CVP and PSO-CVP approaches in terms of accuracy as well as convergence rate.