Metamodel—Assisted Evolution Strategies

Emmerich, Michael; Giotis, Alexios P.; Özdemir, Mutlu; Bäck, Thomas; Giannakoglou, Kyriakos C.

doi:10.1007/3-540-45712-7_35

Cited by 151 publications

(99 citation statements)

References 6 publications

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“…An offspring pool, called O, is created as soon as a processing node becomes available. The solutions in O are evaluated by the surrogate, and since the computational cost of surrogate evaluations can be neglected in real-world optimisations (Emmerich et al, 2002), the size of the pool can be large. The surrogate objective values assigned to solutions in O are adjusted to take the imprecision of the surrogate into consideration.…”

Section: Basic Algorithmmentioning

confidence: 99%

Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling

Syberfeldt

John

et al. 2010

European Journal of Operational Research

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Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling. European Journal of Operational AbstractMany real-world optimisation problems approached by evolutionary algorithms are subject to noise. When noise is present, the evolutionary selection process may become unstable and the convergence of the optimisation adversely affected. In this paper, we present a new technique that efficiently deals with noise in multi-objective optimisation. This technique aims at preventing the propagation of inferior solutions in the evolutionary selection due to noisy objective values. This is done by using an iterative resampling procedure that reduces the noise until the likelihood of selecting the correct solution reaches a given confidence level. To achieve an efficient utilisation of resources, the number of samples used per solution varies based on the amount of noise in the present area of the search space. The proposed algorithm is evaluated on the ZDT benchmark problems and two complex real-world problems of manufacturing optimisation. The first realworld problem concerns the optimisation of engine component manufacturing in aviation industry, while the second real-world problem concerns the optimisation of a camshaft machining line in automotive industry. The results from the optimisations indicate that the proposed technique is successful in reducing noise, and it competes successfully with other noise handling techniques.

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Section: Basic Algorithmmentioning

confidence: 99%

Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling

Syberfeldt

John

et al. 2010

European Journal of Operational Research

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“…w is a weight factor. Emmerich et al (2002) use the same merit function for making evolutionary algorithms more efficient. They propose the "Metamodel-Assisted Evolution Strategy" (MAES) that is also included in the comparison in Section 3.…”

Section: Sao-mmfmentioning

confidence: 99%

“…Genetic and evolutionary algorithms look promising because of their tendency to find the global optimum and the possibility for parallel computation. However, they are known to require many function evaluations (Emmerich et al 2002).…”

Section: Introductionmentioning

confidence: 99%

“…The sequential improvement aims at achieving an accurate solution of the global optimum with the lowest possible number of FE simulations. Two advanced methods are considered in Section 2: 'minimising a merit function' according to Emmerich et al (2002) and 'maximum expected improvement' according to Schonlau (1997). Subsequently, their performance is compared to that of other algorithms (two iterative algorithms and an evolutionary strategy) by application to two forging processes in Section 3.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of forging processes using Finite Element simulations

Bonte

Fourment

et al. 2010

Struct Multidisc Optim

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During the last decades, simulation software based on the Finite Element Method (FEM) has significantly contributed to the design of feasible forming processes. Coupling FEM to mathematical optimization algorithms offers a promising opportunity to design optimal metal forming processes rather than just feasible ones. In this paper Sequential Approximate Optimization (SAO) for optimizing forging processes is discussed. The algorithm incorporates time-consuming nonlinear FEM simulations. Three variants of the SAO algorithm-which differ by their sequential improvement strategies-have been investigated and compared to other optimization algorithms by application to two forging processes. The other algorithms taken into account are two iterative algorithms (BFGS and SCPIP) and a Metamodel Assisted Evolutionary Strategy (MAES). It is essential for sequential approximate optimization algorithms to implement an improvement strategy that uses as much information obtained during previous iterations as possible. If such a sequential improvement strategy is used, SAO provides a very efficient algorithm to optimize forging processes using time-consuming FEM simulations.

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“…Furthermore, it is not difficult to calculate sensitivities with them. However, the rather large number of function evaluations that is expected to be necessary using these algorithms is regarded as a serious drawback (Emmerich et al 2002). Several authors have applied genetic and evolutionary algorithms to optimise metal forming processes, see Abedrabbo et al (2004), António et al (2004), Castro et al (2004), Do et al (2004), Fourment et al (2005a, b), Poursina et al (2003), Schenk and Hillmann (2004), Sousa et al (2003), and Weyler et al (2004).…”

Section: Solvingmentioning

confidence: 99%

An optimisation strategy for industrial metal forming processes

Bonte

Boogaard

Huétink

2008

Struct Multidisc Optim

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Product improvement and cost reduction have always been important goals in the metal forming industry. The rise of finite element (FEM) simulations for processes has contributed to these goals in a major way. More recently, coupling FEM simulations to mathematical optimisation techniques has shown the potential to make a further giant contribution to product improvement and cost reduction. Much research on the optimisation of metal forming processes has been published during the last couple of years. Although the results are impressive, the optimisation techniques are generally only applicable to specific optimisation problems for specific products and specific metal forming processes. As a consequence, applying optimisation techniques to other metal forming problems requires a lot of optimisation expertise, which forms a barrier for more general industrial application of these techniques. In this paper, we overcome this barrier by proposing a generally applicable optimisation strategy that makes use of FEM simulations of metal forming processes. It consists of a structured methodology for modelling optimisation problems related to metal forming. Subsequently, screening is applied to reduce the size of the optimisation problem by selecting only the most important design variables. Finally, the reduced optimisation problem is solved by an efficient optimisation algorithm. The strategy is generally applicable in a sense that it is not constrained to a certain type of metal forming problem, product or process. Also, any FEM code may be included in the strategy. the structured approach for modelling and solving optimisation problems should enable non-optimisation specialists to apply optimisation techniques to improve their products and processes. The optimisation strategy has been successfully applied to a hydroforming process, which demonstrates the potential of the optimisation of metal forming processes in general and more specific the proposed optimisation strategy.

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Metamodel—Assisted Evolution Strategies

Cited by 151 publications

References 6 publications

Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling

Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling

Optimization of forging processes using Finite Element simulations

An optimisation strategy for industrial metal forming processes

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