Integrating Controlled Random Search into the Line-Up Competition Algorithm To Solve Unsteady Operation Problems

In this work, a line-up competition algorithm (LCA) is applied to solve the dynamic optimization problems derived from unsteady chemical systems. The problems are first converted ito nonlinear programming problems using the concept of control vector parametrization. The parameters embedded in the converted problems then are selected by LCA. To improve numerical accuracy, the normal (Gaussian) sampling policy is introduced to replace the uniform sampling policy used in basic LCA. Variable step input (VSI) and variable ramp input (VRI) are respectively considered to rebuild the control policy in solutions. Some typical examples are provided to demonstrate the robustness and efficiency of this modification.