Advanced Fluid Information. Advances in Hierarchical, Parallel Evolutionary Algorithms for Aerodynamic Shape Optimisation.

Whitney, Eric; Sefrioui, M.; Srinivas, K.; Périaux, Jacques

doi:10.1299/jsmeb.45.23

Cited by 18 publications

(9 citation statements)

References 3 publications

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“…In this research we used and extended the Hierarchical Asynchronous Parallel Evolutionary Algorithm (HAPEA) approach developed by Whitney 27,28 . The foundation of this algorithm lies on traditional evolution strategies and incorporate the concepts of multi-criteria optimisation, hierarchical topology, parallel computing and asynchronous evaluation.…”

Section: B Optimisation Methodsmentioning

confidence: 99%

“…The framework has been used to evaluate several real world problems including inverse and direct problems for aerofoil, high-lift aircraft system, multidisciplinary and multi-criteria wing and aircraft design and optimization problems [27][28][29][30] . In the following we illustrate the application of the method for three real world examples; two test cases related to UAV aerofoil design and one test case related to UAV wing design.…”

Section: Applicationsmentioning

confidence: 99%

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A Generic Framework for the Design Optimisation of Multidisciplinary UAV Intelligent Systems Using Evolutionary Computing

González

Périaux

Srinivas

et al. 2006

44th AIAA Aerospace Sciences Meeting and Exhibit

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This paper describes the formulation and application of a design framework that supports the complex task of multidisciplinary design optimisation of Unmanned Aerial Vehicles (UAVs). The framework includes a Graphical User Interface (GUI), a robust Evolutionary Algorithm optimiser, several design modules, mesh generators and post-processing capabilities in an integrated platform. Traditional deterministic optimisation techniques for MDO are effective when applied to specific problems and within a specified range. A new class of optimisation techniques named Hierarchical Asynchronous Parallel Evolutionary Algorithms (HAPEAs) have shown to be robust as they require no derivatives or gradients of the objective function, have the capability of finding globally optimum solutions amongst many local optima, can be executed asynchronously in parallel and adapted easily to arbitrary solver codes without major modifications. The application of the methodology is illustrated on multi-criteria and multidisciplinary design problems. Results indicate the practicality and robustness of the method in finding optimal solutions and Pareto trade-offs between the disciplinary analyses and producing a set of non dominated individuals.= thickness-to-chord ratio Cm = pitching moment coefficient W SC = wing structural weight AR = wing aspect ratio λ br = taper ratio root to break λ bt = taper ratio break to tip Λ = wing 1/4 chord sweep b l = break location

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Section: B Optimisation Methodsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

A Generic Framework for the Design Optimisation of Multidisciplinary UAV Intelligent Systems Using Evolutionary Computing

González

Périaux

Srinivas

et al. 2006

44th AIAA Aerospace Sciences Meeting and Exhibit

Self Cite

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“…In this work we use a robust multi-criteria optimisation software tool; a hierarchical asynchronous parallel evolutionary algorithm (HAPEA) developed by Whitney [16,17] with some extensions for multidisciplinary and multi-criteria analysis [18].…”

Section: Hierarchical Asynchronous Parallel Multi-objective Evolutionmentioning

confidence: 99%

Robust design optimisation using multi-objective evolutionary algorithms

et al. 2008

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“…The first method HAPEA couples EA with several aerodynamic analysis tools and incorporates the concepts of Covariance Matrix Adaptation (CMA) [8,9], a hierarchical topology [10], asynchronous evaluation and a Pareto tournament selection [11,12]. The hierarchical topology can provide different models including precise, intermediate and approximate models.…”

Section: Methodsmentioning

confidence: 99%

Fast reconstruction of aerodynamic shapes using evolutionary algorithms and virtual nash strategies in a CFD design environment

Périaux

Lee

Gonzalez³

et al. 2009

Journal of Computational and Applied Mathematics

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a b s t r a c tThis paper compares the performances of two different optimisation techniques for solving inverse problems; the first one deals with the Hierarchical Asynchronous Parallel Evolutionary Algorithms software (HAPEA) and the second is implemented with a game strategy named Nash-EA. The HAPEA software is based on a hierarchical topology and asynchronous parallel computation. The Nash-EA methodology is introduced as a distributed virtual game and consists of splitting the wing design variables -aerofoil sections -supervised by players optimising their own strategy. The HAPEA and Nash-EA software methodologies are applied to a single objective aerodynamic ONERA M6 wing reconstruction. Numerical results from the two approaches are compared in terms of the quality of model and computational expense and demonstrate the superiority of the distributed Nash-EA methodology in a parallel environment for a similar design quality.Crown

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Advanced Fluid Information. Advances in Hierarchical, Parallel Evolutionary Algorithms for Aerodynamic Shape Optimisation.

Cited by 18 publications

References 3 publications

A Generic Framework for the Design Optimisation of Multidisciplinary UAV Intelligent Systems Using Evolutionary Computing

A Generic Framework for the Design Optimisation of Multidisciplinary UAV Intelligent Systems Using Evolutionary Computing

Robust design optimisation using multi-objective evolutionary algorithms

Fast reconstruction of aerodynamic shapes using evolutionary algorithms and virtual nash strategies in a CFD design environment

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