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

Inapakurthi, Ravi Kumar (2017) Comparison of Evolutionary and Classical Optimization Techniques for solving Multiobjective Optimal Control Problems. Masters thesis, Indian Institute of Technology Hyderabad.

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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.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Thesis (Masters)
Uncontrolled Keywords: Optimal control, Pareto optimal solutions, Evolutionary algorithms, Classical al- gorithms, Non - D ominated Sorting Genetic Algorithm - II, Multi Objective Evolutionary Algo- rithm - Dominance & Decomposition, Weighted Sum, Normal Boundary Intersection, Control Vector Parameterization, TD944
Subjects: Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Team Library
Date Deposited: 21 Jul 2017 09:32
Last Modified: 21 Jul 2017 09:32
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