CMA evolution strategy assisted by kriging model and approximate ranking

Huang, Changwu; Radi, Bouchaïb; Hami, Abdelkhalak El; Bai, Hao

doi:10.1007/s10489-018-1193-3

Cited by 22 publications

(15 citation statements)

References 34 publications

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“…Pour rendre CMA-ES plus efficace on a développé un couplage le modèle de krigeage et la méthode de CMA-ES, ce couplage va permettre au d'évaluer la fonction objectif à partir du métamodèle sans avoir besoin d'appeler le modèle éléments finis à chaque itération. Le couplage entre la méthode CMA-ES et le métamodèle de krigeage donne naissance à une nouvelle approche qu'on appellera CMA-ES assisté par le krigeage (KA-CMA-ES), cette approche s'exécute suivant un algorithme bien précis [21] [26].…”

Section: La Méthode Cma_es Assistée Par Le Krigeageunclassified

Optimisation du comportement thermique du transistor à haute mobilité d’électron (HEMT) par la méthode KA-CMA-ES

Amar¹,

Radi²,

Abdelkhalak³

2020

IncertFia

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Section: La Méthode Cma_es Assistée Par Le Krigeageunclassified

Optimisation du comportement thermique du transistor à haute mobilité d’électron (HEMT) par la méthode KA-CMA-ES

Amar¹,

Radi²,

Abdelkhalak³

2020

IncertFia

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“…Nevertheless in the case of high-dimensional or multimodal problems, the population size l is large, the amount of information added during each iteration can result in unimportant changes, even in the case of a metamodel with poor ranking predictions. To overcome this deficiency, Huang et al [26] propose some modifications for the original approximate ranking procedure.…”

Section: Metamodel Quality Measurementioning

confidence: 99%

“…To comprehensively evaluate an algorithm, experimental studies are important to validate and compare the suggested algorithm with other existing algorithms over a good range of test functions. In [26], the proposed KA-CMA-ES using pre-selection (PS), KA-CMA-ES using ARP and fixed generation-based control (FGC) were studied and compared to the CMA-ES algorithm to show the efficiency of the proposed algorithm.…”

Section: Numerical Testmentioning

confidence: 99%

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Optimization of solder joints in embedded mechatronic systems via Kriging-assisted CMA-ES algorithm

Hamdani

Radi

Hami

2019

Int. J. Simul. Multidisci. Des. Optim.

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In power electronics applications, embedded mechatronic systems (MSs) must meet the severe operating conditions and high levels of thermomechanical stress. The thermal fatigue of the solder joints remains the main mechanism leading to the rupture and a malfunction of the complete MS. It is the main failure to which the lifetime of embedded MS is often linked. Consequently, robust and inexpensive design optimization is needed to increase the number of life cycles of solder joints. This paper proposes an application of metamodel-assisted evolution strategy (MA-ES) which significantly reduces the computational cost of ES induced by the expensive finite element simulation, which is the objective function in optimization problems. The proposed method aims to couple the Kriging metamodel with the covariance matrix adaptation evolution strategy (CMA-ES). Kriging metamodel is used to replace the finite element simulation in order to overcome the computational cost of fitness function evaluations (finite element model). Kriging is used together with CMA-ES and sequentially updated and its fidelity (quality) is measured according to its ability in ranking of the population through approximate ranking procedure (ARP). The application of this method in the optimization of MS proves its efficiency and ability to avoid the problem of computational cost.

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“…Common surrogate models in SAEAs include radial basis functions (RBFs) (Liu et al 2016;Sun et al 2017), polynomial regression (PR) (Wu et al 2018;Si et al 2011), the Kriging model (Fu et al 2020;Huang et al 2018), artificial neural networks (ANNs) (Pan et al 2019), and ensemble surrogate models (Wang et al 2017;Li et al 2019) composed of multiple independent surrogate models. Among these models, RBFs have high prediction accuracy and good global and local approximation capabilities when the number of samples is not sufficient (Jia et al 2020).…”

Section: Introductionmentioning

confidence: 99%

An adaptive surrogate-assisted particle swarm optimization for expensive problems

2021

Soft Comput

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To solve engineering problems with evolutionary algorithms, many expensive objective function evaluations (FEs) are required. To alleviate this difficulty, the surrogateassisted evolutionary algorithm (SAEA) has attracted increasingly more attention in both academia and industry. The existing SAEAs depend on the quantity and quality of the original samples, and it is difficult for them to yield satisfactory solutions within the limited number of FEs. Moreover, these methods easily fall into local optima as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization (ASAPSO) algorithm. In the proposed algorithm, an adaptive surrogate selection method that depends on the comparison between the best existing solution and the latest obtained solution is suggested to ensure the effectiveness of the optimization operations and improve the computational efficiency. Additionally, a model output criterion based on the standard deviation is suggested to improve the robustness and stability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within the limited number of FEs. The performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process.

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CMA evolution strategy assisted by kriging model and approximate ranking

Cited by 22 publications

References 34 publications

Optimisation du comportement thermique du transistor à haute mobilité d’électron (HEMT) par la méthode KA-CMA-ES

Optimisation du comportement thermique du transistor à haute mobilité d’électron (HEMT) par la méthode KA-CMA-ES

Optimization of solder joints in embedded mechatronic systems via Kriging-assisted CMA-ES algorithm

An adaptive surrogate-assisted particle swarm optimization for expensive problems

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