Shape optimisation method based on the surrogate models in the parallel asynchronous environment

[Display omitted] •This paper introduces Parallel Asynchronous Surrogate Model (PASM) method for Engineering Design Optimisation.•The PASM method is enhanced by the use of the Cornering technique (PASM+C), which selects the best performing designs in the corners of the parametric design space.•The p...

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Vydané v:Applied soft computing Ročník 71; s. 1189 - 1203
Hlavní autori: Przysowa, Krzysztof, Łaniewski-Wołłk, Łukasz, Rokicki, Jacek
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.10.2018
Predmet:
Car aerodynamics
Cloud computing
Corners of the optimisation domain
High performance computing
Mixed surrogate models
Parallel optimisation
Cloud computing
High performance computing
Corners of the optimisation domain
Parallel optimisation
Car aerodynamics
Mixed surrogate models
ISSN:1568-4946, 1872-9681
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Shrnutí:[Display omitted] •This paper introduces Parallel Asynchronous Surrogate Model (PASM) method for Engineering Design Optimisation.•The PASM method is enhanced by the use of the Cornering technique (PASM+C), which selects the best performing designs in the corners of the parametric design space.•The performance and other features of PASM and hybrid PASM+C are compared with variety of popular optimisation methods in the test case of car aerodynamics shape optimisation.•This study provides insight into the wallclock time, fault tolerance and computing resources needed for the minimisation of the objective function, important factors when using cloud-based High Performance Computing (HPC) services. This paper proposes a new optimisation method, the Parallel Asynchronous Surrogate Model (PASM) method, which is based on the surrogate models approximation and takes advantage of the asynchronous, parallel processing threads. Additionally, it introduces the Cornering technique (PASM+C), which by using values from the corners of the design space provides a rapid drop of the value of the objective function and a significant reduction of the processing time. An overview and characteristics of the main reference optimisation methods, like Particle Swarm Optimisation PSO and Genetic Algorithm (GA), is presented, together with the results of the computer experiments involving optimisation of the generic car body shape. Significant attention is devoted to the time and computational effort needed for drag minimization by using different optimisation schemes. Finally, the benefits and limitations of the proposed methods are discussed together with their potential impact on the optimisation process workflow.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2018.04.028