Dynamic Multi Objective Optimization of Chemical Processes Using Constrained Bare Bones MOPSO Algorithm

Most of chemical processes are dynamic and require optimization of multiple targets, which yields the problem of constrained dynamic multi objective optimization. Aiming at the above problem, this work proposes a constrained bare bones MOPSO algorithm, which adopts double external archives by integrating Pareto domination principle and ε constrained domination principle. To avoid premature convergence, a hybrid mutation operator is introduced. Meanwhile, an adaptive sampling distribution strategy is used to enhance the exploratory ability. Thus, an approach combining bare bones MOPSO and control vector parameterization is proposed to solve the dynamic optimization problems. Finally, the comparison with NSGA II and SADE εCD algorithm is made to verify the advantageous performance of the proposed constrained bare bones MOPSO algorithm in this work.