A multilevel approach to single- and multiobjective aerodynamic optimization

Kampolis, Ioannis C.; Giannakoglou, Kyriakos C.

doi:10.1016/j.cma.2008.01.015

Cited by 59 publications

(51 citation statements)

References 26 publications

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“…Taking this into consideration, herein a generalized (µ, λ)EA (with µ parents and λ offspring) implementing standard evolution operators, will be used as the core search engine. Structuring the evolutionary search in hierarchical manner is a means to reduce the overall CPU cost for carrying out the optimization, combining different tools (Kampolis et al 2007, Kampolis andGiannakoglou 2008). The gain from using hierarchical search is superimposed to that expected from the use of a "better" EA, "better" evolution operators and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…The gain from using hierarchical search is superimposed to that expected from the use of a "better" EA, "better" evolution operators and so forth. According to the literature survey, existing hierarchical schemes can be classified to algorithms relying on different evaluation methods to computer the fitness or cost function of candidate solutions (with different fidelity and CPU cost) (Eby et al 1998, Herrera et al 1999, Sefrioui and Périaux 2000, Karakasis et al 2007, Kampolis et al 2007, Kampolis and Giannakoglou 2008, different search techniques (Muyl et al 2004, Poloni et al 2000, Knowles and Corne 2000 and different chromosome sizes (Lin et al 1994) or numbers of design variables (Désidéri andJanka 2003, Duvigneau et al 2006). The present paper is concerned with the first class of hierarchical methods, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…In (Karakasis et al 2007, Kampolis et al 2007, Kampolis and Giannakoglou 2008, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 F o r P e e r R e v i e w O n l y the distributed search of optimal solutions is structured in levels, each of which employs an EA relying on surrogate evaluation models, i.e. the so-called metamodels.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are significant differences between these three works and the present one. The former deal with hierarchical (or multilevel) optimization algorithms where each level is supported by its own single-or multi-population EA or, depending on the configuration, a deterministic search method, see (Kampolis and Giannakoglou 2008). To make it as fast as possible, all EAs are assisted by surrogate models (metamodels) which are trained locally on previously evaluated (on the level's model) individuals.…”

Section: Introductionmentioning

confidence: 99%

“…To contrast methods presented in (Karakasis et al 2007, Kampolis et al 2007, Kampolis and Giannakoglou 2008 with the present paper, the former will be referred to as hierarchical distributed metamodel-assisted EAs whereas the newly proposed method is a distributed hierarchical EA in which properly trained metamodels are used for the evaluation of candidate solutions during the lower pass. In the present method, the hierarchical search is carried out within each deme (this is why this is called distributed hierarchical, rather than the other way round).…”

Section: Introductionmentioning

confidence: 99%

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Distributed evolutionary algorithms with hierarchical evaluation

Kampolis

Giannakoglou

2009

Engineering Optimization

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Distributed evolutionary algorithms with hierarchical evaluation

Kampolis

Giannakoglou

2009

Engineering Optimization

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An improved genetic algorithm and its applications to the optimisation design of an aspirated compressor profile

Zhang

Zhou

2015

Numerical Methods in Fluids

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SUMMARYGenetic algorithm (GA) is a widely used method for numerical optimisation owing to their good global search ability; however, their local search ability has an obvious shortcoming. To improve local search ability, this paper introduces a simplex method and combines it with a GA to form an improved genetic algorithm (IGA). In the IGA, at each generation of the original GA, high-fitness individuals are selected as vertices of a simplex, and then a one-dimensional search within the simplex is conducted to obtain the most-fit individuals while replacing the inferior ones. Typical test functions show that the IGA can effectively improve the optimisation effect over that of the original GA. To further verify the IGA's practicability, an aspirated compressor profile is optimised with profile, suction flow rate and suction flow location as coupled design parameters. The results again show that the IGA has a better optimising effect than the GA. In addition, it is also verified that coupling the profile and suction flow parameters results in a design that outperforms the uncoupled design; therefore, designing an aspirated compressor blade by arranging suction flow on a conventional blade without considering suction flow is not a good method.

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Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

Allmendinger

Emmerich

Hakanen

et al. 2017

J. Multi-Crit. Decis. Anal.

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Complexity in solving real-world multicriteria optimization problems often stems from the fact that complex, expensive, and/or time-consuming simulation tools or physical experiments are used to evaluate solutions to a problem. In such settings, it is common to use efficient computational models, often known as surrogates or metamodels, to approximate the outcome (objective or constraint function value) of a simulation or physical experiment. The presence of multiple objective functions poses an additional layer of complexity for surrogate-assisted optimization.For example, complexities may relate to the appropriate selection of metamodels for the individual objective functions, extensive training time of surrogate models, or the optimal use of many-core computers to approximate efficiently multiple objectives simultaneously. Thinking out of the box, complexity can also be shifted from approximating the individual objective functions to approximating the entire Pareto front. This leads to further complexities, namely, how to validate statistically and apply the techniques developed to real-world problems. In this paper, we discuss emerging complexity-related topics in surrogate-assisted multicriteria optimization that may not be prevalent in nonsurrogate-assisted single-objective optimization. These complexities are motivated using several real-world problems in which the authors were involved. We then discuss several promising future research directions and prospective solutions to tackle emerging complexities in surrogate-assisted multicriteria optimization. Finally, we provide insights from an industrial point of view into how surrogate-assisted multicriteria optimization techniques can be developed and applied within a collaborative business environment to tackle real-world problems.

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A multilevel approach to single- and multiobjective aerodynamic optimization

Cited by 59 publications

References 26 publications

Distributed evolutionary algorithms with hierarchical evaluation

Distributed evolutionary algorithms with hierarchical evaluation

An improved genetic algorithm and its applications to the optimisation design of an aspirated compressor profile

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

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