Analytical target cascading using ensemble of surrogates for engineering design problems

Jiang, Zheng; Qiu, Haobo; Zhao, Ming; Zhang, Shizhan; Gao, Liang

doi:10.1108/ec-11-2014-0242

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…The results show that an ensemble of multiple metamodels seems to be able to avoid a misleading optimum by using a single metamodel. Jiang et al (2015) also applied the ensemble model combined with the analytical target cascading strategy in the multidisciplinary design optimization of a super-heavy vertical lathe, and similar ensemble methods can be found in Acar (2010), Zhou et al (2011) and Shi et al (2016). Gu et al (2012) proposed a hybrid and adaptive metamodel (HAM)-based global optimizing method, which can automatically select appropriate metamodels during the search process to improve search efficiency.…”

Section: Multiple Metamodels 71mentioning

confidence: 99%

A hybrid global optimization method based on multiple metamodels

Cai

Qiu

Gao

et al. 2018

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Purpose This paper aims to propose hybrid global optimization based on multiple metamodels for improving the efficiency of global optimization. Design/methodology/approach The method has fully utilized the information provided by different metamodels in the optimization process. It not only imparts the expected improvement criterion of kriging into other metamodels but also intelligently selects appropriate metamodeling techniques to guide the search direction, thus making the search process very efficient. Besides, the corresponding local search strategies are also put forward to further improve the optimizing efficiency. Findings To validate the method, it is tested by several numerical benchmark problems and applied in two engineering design optimization problems. Moreover, an overall comparison between the proposed method and several other typical global optimization methods has been made. Results show that the global optimization efficiency of the proposed method is higher than that of the other methods for most situations. Originality/value The proposed method sufficiently utilizes multiple metamodels in the optimizing process. Thus, good optimizing results are obtained, showing great applicability in engineering design optimization problems which involve costly simulations.

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Section: Multiple Metamodels 71mentioning

confidence: 99%

A hybrid global optimization method based on multiple metamodels

Cai

Qiu

Gao

et al. 2018

Self Cite

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“…A promising way to make RO approaches efficiently is to adopt metamodel (or surrogate), which can mimic the original system at a considerably reduced computational cost. There are a lot of commonly used metamodels, such as polynomial response surface models (Eddy et al, 2015), Kriging metamodels (Kleijnen, 2009;Zhou et al, 2016), neural networks models (Chang et al, 2016;Wang et al, 2016), radial basis function models (Jiang et al, 2015) and support vector regression models (Zhou et al, 2015b;Xiao et al, 2015). Among these metamodeling techniques, Kriging metamodel is the most intensively On-line Kriging metamodel investigated metamodel for improving the computational efficiency of RO because it presents several interesting difference compared with other metamodels (Kleijnen, 2009;Gao et al, 2016).…”

Section: (2)mentioning

confidence: 99%

An on-line Kriging metamodel assisted robust optimization approach under interval uncertainty

Zhou

Jiang

Shao

et al. 2017

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Purpose Uncertainty is inevitable in real-world engineering optimization. With an outer-inner optimization structure, most previous robust optimization (RO) approaches under interval uncertainty can become computationally intractable because the inner level must perform robust evaluation for each design alternative delivered from the outer level. This paper aims to propose an on-line Kriging metamodel-assisted variable adjustment robust optimization (OLK-VARO) to ease the computational burden of previous VARO approach. Design/methodology/approach In OLK-VARO, Kriging metamodels are constructed for replacing robust evaluations of the design alternative delivered from the outer level, reducing the nested optimization structure of previous VARO approach into a single loop optimization structure. An on-line updating mechanism is introduced in OLK-VARO to exploit the obtained data from previous iterations. Findings One nonlinear numerical example and two engineering cases have been used to demonstrate the applicability and efficiency of the proposed OLK-VARO approach. Results illustrate that OLK-VARO is able to obtain comparable robust optimums as to that obtained by previous VARO, while at the same time significantly reducing computational cost. Practical implications The proposed approach exhibits great capability for practical engineering design optimization problems under interval uncertainty. Originality/value The main contribution of this paper lies in the following: an OLK-VARO approach under interval uncertainty is proposed, which can significantly ease the computational burden of previous VARO approach.

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“…On the contrary, FEM, which is a numerical method, is the best for compliant mechanism modeling. More recently, surrogate-based approaches, computational intelligence and machine learning are known as powerful approaches to model complex systems (Yüksel and Sezgin, 2010; Jiang et al , 2015; Guo et al , 2016; Amrit and Leifsson, 2019).…”

Section: Introductionmentioning

confidence: 99%

A hybrid computational method for optimization design of bistable compliant mechanism

Chau

Tran

Dao

2020

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Purpose Compliant mechanism has been receiving a great interest in precision engineering. However, analytical methods involving their behavior analysis is still a challenge because there are unclear kinematic behaviors. Especially, design optimization for compliant mechanisms becomes an important task when the problem is more and more complex. Therefore, the purpose of this study is to design a new hybrid computational method. The hybridized method is an integration of statistics, numerical method, computational intelligence and optimization. Design/methodology/approach A tensural bistable compliant mechanism is used to clarify the efficiency of the developed method. A pseudo model of the mechanism is designed and simulations are planned to retrieve the data sets. Main contributions of design variables are analyzed by analysis of variance to initialize several new populations. Next, objective functions are transformed into the desirability, which are inputs of the fuzzy inference system (FIS). The FIS modeling is aimed to initialize a single-combined objective function (SCOF). Subsequently, adaptive neuro-fuzzy inference system is developed to modeling a relation of the main geometrical parameters and the SCOF. Finally, the SCOF is maximized by lightning attachment procedure optimization algorithm to yield a global optimality. Findings The results prove that the present method is better than a combination of fuzzy logic and Taguchi. The present method is also superior to other algorithms by conducting non-parameter tests. The proposed computational method is a usefully systematic method that can be applied to compliant mechanisms with complex structures and multiple-constrained optimization problems. Originality/value The novelty of this work is to make a new approach by combining statistical techniques, numerical method, computational intelligence and metaheuristic algorithm. The feasibility of the method is capable of solving a multi-objective optimization problem for compliant mechanisms with nonlinear complexity.

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Analytical target cascading using ensemble of surrogates for engineering design problems

Cited by 11 publications

References 33 publications

A hybrid global optimization method based on multiple metamodels

A hybrid global optimization method based on multiple metamodels

An on-line Kriging metamodel assisted robust optimization approach under interval uncertainty

A hybrid computational method for optimization design of bistable compliant mechanism

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