High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm

Abstract

The employment of conventional optimization procedures that must be repeatedly invoked during the optimization process in real-world engineering applications is hindered despite significant gains in computing power by computationally expensive models. As a result, surrogate models that require far less time and resources to analyze are used in place of these time-consuming analyses. In multi-objective optimization (MOO) problems involving pricey analysis and simulation techniques such as multi-physics modeling and simulation, finite element analysis (FEA), and computational fluid dynamics (CFD), surrogate models are found to be a promising endeavor, particularly for the optimization of complex engineering design problems involving black box functions. In order to reduce the expense of fitness function evaluations and locate the Pareto frontier for MOO problems, the automated multiobjective surrogate based Pareto finder MOO algorithm (AMSP) is proposed. Utilizing data samples taken from the feasible design region, the algorithm creates three surrogate models. The algorithm repeats the process of sampling and updating the Pareto set, by assigning weighting factors to those surrogates in accordance with the values of the root mean squared error, until a Pareto frontier is discovered. AMSP was successfully employed to identify the Pareto set and the Pareto border. Utilizing multi-objective benchmark test functions and engineering design examples such airfoil shape geometry of wind turbine, the unique approach was put to the test. The cost of computing the Pareto optima for test functions and real engineering design problem is reduced, and promising results were obtained.