Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence

By Kyriakos C. Giannakoglou. 2002

294 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b7143dbb51d2ccb8c602157ec891345a188256ff

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Citations in the corpus

Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System (2016, background): “Most existing work on data-driven evolutionary optimization has actually been carried out in the area of surrogate-assisted evolutionary optimization, where fitness evaluations reply on very expensive physical experiments or highly time-consuming computer simulations, such as in drug design [3] and ”
Aerodynamic Shape Optimization Using First and Second Order Adjoint and Direct Approaches (2008, background): “The price to pay is the higher CPU cost which can be reduced only through the smart use of surrogate analysis tools [12–15].”
Ieee Transactions on Systems, Man and Cybernetics 1 Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization (unknown): “evolutionary optimization algorithms that were recently introduced in the literature [1], [3].”
Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction (2011, methodology): “In one approach, a sample of solutions are used to generate a meta-model (approximate model of the original objectives and constraints) and then efforts have been made to find the optimum of the meta-model, assuming that the optimal solutions of both the meta-model and the original problem are simil”
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels (2006, methodology): “Multilayer perceptrons [12] or exactly interpolating radial basis function (RBF) networks [6] can be used, either in their standard forms or by incorporating add-on features such as measures for the relative importance of input variables (cf. Giannakoglou [6], [13]).”
Multi-objective optimization using evolutionary algorithms (2001, methodology): “In one approach, a sample of solutions are used to generate a meta-model (approximate model of the original objectives and constraints) and then efforts have been made to find the optimum of the meta-model, assuming that the optimal solutions of both the meta-model and the original problem are simil”
Model-based methods for continuous and discrete global optimization (2017, background): “Giannakoglou [36] discusses an aerodynamic shape design problem.”
ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems (2006, methodology): “Although problems exhibiting (many of) these features include some familiar ones from engineering design optimization (e.g., [29]), there are also less well-known combinatorial biochemistry and materials science applications [14], [24], [58], as well as instrument configuration problems [54], [55],…”
Optimization and Computational Fluid Dynamics (2008, methodology): “On the other hand, evolutionary algorithms have some other advantages [8, 20].”
Memetic algorithms and memetic computing optimization: A literature review (2012, background): “In order to face this difficulty, in [60, 87] the Inexact Pre-Evaluation (IPE) framework is proposed.”
High-fidelity aerodynamic shape optimization using efficient orthogonal modal design variables with a constrained global optimizer (2017, background): “Examples of gradient-based [43, 44, 45, 46] and global search [47, 16, 17] optimization algorithms within the ASO environment can be found.”