Optimisation of a Stator Blade Used in a Transonic Compressor Cascade with Evolution Strategies

Olhofer, Markus; Sendhoff, Bernhard; Arima, Toshiyuki; Sonoda, Toyotaka

doi:10.1007/978-1-4471-0519-0_4

Cited by 27 publications

(14 citation statements)

References 7 publications

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“…Evolutionary Algorithm(EA), a stochastic computational model inspired by the neo-Darwinian theory of evolution, has been used as the major optimization framework in various complex real-world optimization problems, such as in the designs of aerodynamic shape [1], transonic civil transport aircraft wing [2], stator blade [3], hard disk drive servo control [4], and biomedical applications [5]. However, as a population-based algorithm, thousands of calls to the analysis codes are often required to locate a near optimal solution in most conventional EA.…”

Section: Introductionmentioning

confidence: 99%

A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation

Lim

Ong

Jin

et al. 2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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Surrogate-Assisted Memetic Algorithm(SAMA) is a hybrid evolutionary algorithm, particularly a memetic algorithm that employs surrogate models in the optimization search. Since most of the objective function evaluations in SAMA are approximated, the search performance of SAMA is likely to be affected by the characteristics of the models used. In this paper, we study the search performance of using different metamodeling techniques, ensembles, and multisurrogates in SAMA. In particular, we consider the SAMA-TRF, a SAMA model management framework that incorporates a trust region scheme for interleaving use of exact objective function with computationally cheap local metamodels during local searches. Four different metamodels, namely Gaussian Process (GP), Radial Basis Function (RBF), Polynomial Regression (PR), and Extreme Learning Machine (ELM) neural network are used in the study. Empirical results obtained show that while some metamodeling techniques perform best on particular benchmark problems, ensemble of metamodels and multi-surrogates yield robust and improved solution quality on the benchmark problems in general, for the same computational budget.

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Section: Introductionmentioning

confidence: 99%

A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation

Lim

Ong

Jin

et al. 2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

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“…Evolutionary algorithms have proven to be very promising for the optimization of these complex shapes [8]. However, thousands of performance evaluations are usually needed before a satisfactory solution can be obtained.…”

Section: Application and Resultsmentioning

confidence: 99%

Structure optimization of neural networks for evolutionary design optimization

2003

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We study the use of neural networks (NN) as approximate models for fitness evaluation in evolutionary computation. To improve the quality of the NN models, structure optimization of these NNs is applied with respect to two different criteria: One is the commonly used approximation error, and the other is the ability of the NNs to learn different problems of a common class of problems. Simulation results from turbine blade optimization using the structurally optimized NN models are presented to show that the performance of the model can be improved significantly through structure optimization.

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“…In recent years, evolutionary algorithms have successfully been applied to single and multiobjective aerodynamic optimization (Obayashi et al, 2000;Olhofer et al, 2000;Hasenjäger et al, 2005). Despite the success that has been achieved in evolutionary aerodynamic optimization, several issues must be carefully addressed.…”

Section: Problem Descriptionmentioning

confidence: 99%

Real-World Applications of Multiobjective Optimization

Stewart

Bandte

Braun

et al. 2008

Multiobjective Optimization

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Abstract. This chapter presents a number of illustrative case studies of a wide range of applications of multiobjective optimization methods, in areas ranging from engineering design to medical treatments. The methods used include both conventional mathematical programming and evolutionary optimization, and in one case an integration of the two approaches. Although not a comprehensive review, the case studies provide evidence of the extent of the potential for using classical and modern multiobjective optimization in practice, and opens many opportunities for further research. IntroductionThe intention with this chapter is to provide illustrations of real applications of multiobjective optimization, covering both conventional mathematical programming approaches and evolutionary multiobjective optimization. These illustrations do cover a broad range of application, but do not attempt to provide a comprehensive review of applications.

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Optimisation of a Stator Blade Used in a Transonic Compressor Cascade with Evolution Strategies

Cited by 27 publications

References 7 publications

A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation

A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation

Structure optimization of neural networks for evolutionary design optimization

Real-World Applications of Multiobjective Optimization

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