Robust aerodynamic shape optimization using a novel multi-objective evolutionary algorithm coupled with surrogate model
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RESEARCH PAPER
Robust aerodynamic shape optimization using a novel multi-objective evolutionary algorithm coupled with surrogate model Xu Tian 1 & Jie Li 1 Received: 4 September 2019 / Revised: 7 January 2020 / Accepted: 28 March 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm that has exhibited enormous values in engineering optimization. In this study, an effective multi-objective fruit fly optimization algorithm (MOFOA) incorporated with the Pareto dominance is investigated and applied to robust aerodynamic shape optimization considering uncertainties in the design process. In the MOFOA, the fruit flies are required to perform solution explorations within an adaptive search scope by flying around the Pareto non-dominated solutions. An enhanced clustering evolution mechanism of combining the cooperative local search and differential evolution operator is elaborately designed to enrich the exploratory capacities for complex optimization. Paired with the search strategy, the non-dominated sorting technique considering the quality as well as uniform distribution of Pareto solutions is adopted to deal with the multiple objectives. To generate high-quality and uniformly spread-out Pareto non-dominated solutions, the roulette wheel selection (RWS) operator based on crowding distance is employed to guide the selection processes of individuals throughout various stages of the MOFOA. The effectiveness and superiority of MOFOA are demonstrated by the comparative studies on several benchmark functions. Finally, a robust aerodynamic design system for combining the MOFOA optimizer with a Kriging-based surrogate model and full Navier–Stokes computation is advantageously implemented to perform shape optimization on a transonic airfoil using the Taguchi robust design methodology that emphasizes the inherent variability while improving engineering productivity. The design results show that the robust design, compared with the single-point design, is capable of producing a set of robust solutions that are significantly less sensitive to input variations by capturing the Pareto-optimal front with respect to the criteria of aerodynamic performance and its stability. Keywords Fruit fly optimization algorithm . Multi-objective optimization . Cooperative local search . Differential evolution operator . Pareto-optimal solutions . Robust aerodynamic shape optimization
1 Introduction Aerodynamic shape optimization (ASO), using advanced computational fluid dynamics (CFD) technology coupled with a variety of numerical optimization methods, has played
Responsible Editor: Nathalie Bartoli * Jie Li [email protected] Xu Tian [email protected] 1
Department of Fluid Mechanics, School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, People’s Republic of China
an indispensable role in aircraft design over the past few decades (Lee and Eyi 1992; Nemec and Zingg 2002; Buckley and Zingg 2013; Jameson et al.
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