In addition to randomly chosen candidates for control, we examine the effect of also including deterministic control candidates in iterative dynamic programming (IDP) to improve the chance of achieving the global optimal solution. Two types of deterministic control candidates (shifting and smoothing candidates) are chosen on the basis of the control policy obtained in the previous iteration. The search for the optimal value for control in the subsequent iteration is then made on the combined set of control candidates chosen randomly and deterministically. Three highly nonlinear and multimodal chemical engineering optimal control problems are used to illustrate the advantages of this procedure in obtaining the global optimum.