High-fidelity surrogate based multi-objective optimization algorithm
Date
2022
Authors
Younis, Adel
Dong, Zuomin
Journal Title
Journal ISSN
Volume Title
Publisher
Algorithms
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.
Description
Keywords
multi-objective optimization, mixed surrogates, Pareto frontier, wind turbine airfoil geometry
Citation
Younis, A. & Dong, Z. (2022). “High-fidelity surrogate based multi-objective optimization algorithm.” Algorithms, 15(8), 279. https://doi.org/10.3390/a15080279