A novel optimal approach named invasive weed optimization-control vector parameterization (IWO-CVP) for chemical dynamic optimization problems is proposed where CVP is used to transform the problem into a nonlinear programming (NLP) problem and an IWO algorithm is then applied to tackle the NLP problem. To improve efficiency, a new adaptive dispersion IWO-based approach (ADIWO-CVP) is further suggested to maintain the exploration ability of the algorithm throughout the entire searching procedure. Several classic chemical dynamic optimization problems are tested and detailed comparisons are carried out among ADIWO-CVP, IWO-CVP, and other methods. The research results demonstrate that ADIWO-CVP not only is efficient, but also outperforms IWO-CVP in terms of both accuracy and convergence speed.