Comparison of Evolutionary and Classical Optimization Techniques for solving Multiobjective Optimal Control Problems

Abstract

Multiobjective o ptimal control problems are ubiquitous in chemical industries. They are char- acterized by two or more confli cting objectives for a system with (in)equality constraints for which solutions (known as Pareto - optimal solutions) may or may not exist . This work presents two classes of optimization algorithms which can be used to solve the multiobjective optimal contro l problems. The first ones are the Evolutionary Algorithms which try to mimic the na- ture’s evolution process and the second ones are the classical techniques which make use of the differential calculus in locating the optimum solution. T he current study pr esents a com- prehensive comparison between some of the state of art algorithms from both the domains. Non - Dominated Sorting Genetic Algorithm - II and Multi Objective Evolutionary Algorithm - Dominance & Decomposition on the evolutionary side and the Weighted S um, Normal Bound- ary Intersection and Control Vector Parameterization on the classical algorithms side are con- sidered for this study . Comparison between the two class of algorithms is made with a bench- mark multiobjective optimal control problem taken from l iterature w hich aims at design ing a plug flow reactor hosting irreversible exothermal reaction with conflicting energy and conver- sion costs . The comparison study presented in the current work results in the conclusion that for the given problem, the evolut ionary algorithms proved to be better than their classical coun- terparts in determining a better approximation to the desired Pareto Optimal front