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首頁> 外文期刊>Archives of Computational Methods in Engineering >Multi-level Hybridized Optimization Methods Coupling Local Search Deterministic and Global Search Evolutionary Algorithms
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Multi-level Hybridized Optimization Methods Coupling Local Search Deterministic and Global Search Evolutionary Algorithms

機譯：多級雜交優(yōu)化方法耦合本地搜索確定性和全局搜索進化算法

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Efficient optimization methods coupling a stochastic evolutionary algorithm with a gradient based deterministic method are presented in this paper. Two kinds of hybridization are compared: one is a stochastic/deterministic alternate algorithm, the other is a stochastic/deterministic embedded algorithm. In the alternating algorithm, stochastic and deterministic optimizers are performed alternately as follows: some individuals are selected from the previous population, and sent to the deterministic algorithms for further optimization, then the improved individuals are inserted into the above population to form a new one for the stochastic algorithm. In the embedded hybridized algorithm, stochastic and deterministic optimization software are run in parallel and independently, the coupling between them is that the deterministic optimizer operates on a randomly selected individual (or the best individual) from the non evaluated population of the stochastic algorithm, then its outcome (new individual) is re-injected into the evaluated population. Moreover, a multilevel approximation (e.g. variable fidelity modeling, analysis and hierarchical approximated parameterization) is introduced in the algorithm, via a low fidelity modeling and rough parameterization to perform a search on large population at lower level, and a high fidelity modeling with detailed parameterization used at higher level. After a theoretical validation of the methods on mathematical test cases, the hybridized methods are successfully applied to the aerodynamic shape optimization of a fore-body of an hypersonic air breathing vehicle, providing both a significant acceleration in terms of parallel HPC performance and improved quality of the design.
機譯：本文介紹了高效優(yōu)化方法耦合具有梯度的確定性方法的隨機進化算法。比較兩種雜交：一個是一種隨機/確定性替代算法，另一個是隨機/確定性嵌入算法。在交替算法中，隨機和確定性優(yōu)化器交替地執(zhí)行如下：一些個體選自以前的群體，并發(fā)送到確定性算法以進一步優(yōu)化，然后將改進的個體插入上述人口中以形成新的群體以形成新的人群以形成新的人群以形成新的人群以形成新的人群以形成新的人群隨機算法。在嵌入式雜交算法中，隨機和確定性優(yōu)化軟件并行且獨立地運行，它們之間的耦合是確定性優(yōu)化器在來自隨機算法的非評估群體的隨機選擇的單獨（或最佳個體）上操作其結(jié)果（新個人）重新注入評估的人口。此外，通過低保真建模和粗略參數(shù)化在算法中引入了多級近似（例如，可變保真性建模，分析和分析和分級近似參數(shù)化），以在較低級別下對大型群體進行搜索，以及具有詳細(xì)參數(shù)化的高保真建模在較高級別使用。在對數(shù)學(xué)測試用例的方法進行理論驗證之后，將雜交的方法成功應(yīng)用于超聲波空氣呼吸車前體的空氣動力學(xué)優(yōu)化，在平行HPC性能方面提供了顯著的加速度和提高的質(zhì)量該設(shè)計。

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    Tang Z.; Hu X.; Periaux J.;

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    Nanjing Univ Aeronaut & Astronaut NUAA Coll Aerosp Engn Nanjing 210016 Peoples R China;

    Nanjing Univ Aeronaut & Astronaut NUAA Coll Aerosp Engn Nanjing 210016 Peoples R China;

    Univ Politecn Cataluna Int Ctr Numer Methods Engn CIMNE Barcelona Spain|Univ Jyvaskyla Fac Informat Technol Jyvaskyla Finland;

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