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Article
Publication date: 3 October 2016

Jun Zheng, Zilong Li, Liang Gao and Guosheng Jiang

The purpose of this paper is to efficiently use as few sample points as possible to get a sufficiently explored design space and an accurate optimum for adaptive…

Abstract

Purpose

The purpose of this paper is to efficiently use as few sample points as possible to get a sufficiently explored design space and an accurate optimum for adaptive metamodel-based design optimization (AMBDO).

Design/methodology/approach

A parameterized lower confidence bounding (PLCB) scheme is proposed in which a cooling strategy is introduced to guarantee the balance between exploitation and exploration by varying weights of the predicting error and optimum of a metamodel. The proposed scheme is investigated by a set of test functions and a structural optimization problem, in which PLCB with four kinds of cooling control functions are studied. Moreover, other infill criteria (such as expected improvement and its extension versions) are taken into comparison.

Findings

Results show that the proposed PLCB (especially PLCB with the first cooling control function) based AMBDO method can find the optimum with fewer evaluations and maintain good accuracy, which means the proposed PLCB contributes to the excellent efficiency and accuracy in finding global optimal solutions.

Originality/value

The parameterized version of the lower confidence bound metric is proposed for AMBDO, typically used in the context of adaptive sampling in efficient global optimization.

Details

Engineering Computations, vol. 33 no. 7
Type: Research Article
ISSN: 0264-4401

Keywords

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Article
Publication date: 6 November 2017

Leshi Shu, Ping Jiang, Li Wan, Qi Zhou, Xinyu Shao and Yahui Zhang

Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a…

Abstract

Purpose

Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel sequential sampling strategy (weighted accumulative error sampling, WAES) to obtain accurate metamodels and apply it to improve the quality of global optimization.

Design/methodology/approach

A sequential single objective formulation is constructed to adaptively select new sample points. In this formulation, the optimization objective is to select a sample point with the maximum weighted accumulative predicted error obtained by analyzing data from previous iterations, and a space-filling criterion is introduced and treated as a constraint to avoid generating clustered sample points. Based on the proposed sequential sampling strategy, a two-step global optimization approach is developed.

Findings

The proposed WAES approach and the global optimization approach are tested in several cases. A comparison has been made between the proposed approach and other existing approaches. Results illustrate that WAES approach performs the best in improving metamodel accuracy and the two-step global optimization approach has a great ability to avoid local optimum.

Originality/value

The proposed WAES approach overcomes the shortcomings of some existing approaches. Besides, the two-step global optimization approach can be used for improving the optimization results.

Details

Engineering Computations, vol. 34 no. 8
Type: Research Article
ISSN: 0264-4401

Keywords

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Article
Publication date: 10 October 2008

Ming Zhao, Zhengdong Huang and Liping Chen

The purpose of this paper is to introduce a new method to carry out the design optimization of the tool head system in the heavy‐duty machine tool where closed hydrostatic…

Abstract

Purpose

The purpose of this paper is to introduce a new method to carry out the design optimization of the tool head system in the heavy‐duty machine tool where closed hydrostatic guideways with multiple pockets are employed.

Design/methodology/approach

A more accurate method of pressure calculation for closed hydrostatic guideways is introduced. Then, a multidisciplinary design optimization (MDO) model is formulated for design of a tool head system, which minimizes the highest pocket pressure under some constraints from machining accuracy and vibration resistance requirements as well as constraints from ballscrew design specifications. Finally, a metamodel‐based design space alternation (DSA) strategy is proposed to solve the optimization problem.

Findings

The results show that the maximum pocket pressure in a tool head system can be reduced over 47 percent with a proper design while all the constraints are satisfied. As a consequence, the tool head system can safely work under the maximum output pressure of oil supply system.

Originality/value

This paper introduces a more accurate method of pressure calculation for multi‐pocket closed hydrostatic guideways, develops a metamodel‐based MDO model for the tool head system, and proposes a DSA strategy to solve the MDO problem.

Details

Engineering Computations, vol. 25 no. 7
Type: Research Article
ISSN: 0264-4401

Keywords

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Article
Publication date: 25 March 2019

Ji Cheng, Ping Jiang, Qi Zhou, Jiexiang Hu, Tao Yu, Leshi Shu and Xinyu Shao

Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational…

Abstract

Purpose

Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the adaptive metamodel-based design optimization (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, a lower confidence bounding approach based on the coefficient of variation (CV-LCB) approach, to balance the exploration and exploitation objectively for obtaining a global optimum under limited computational budget.

Design/methodology/approach

In the proposed CV-LCB approach, the coefficient of variation (CV) of predicted values is introduced to indicate the degree of dispersion of objective function values, while the CV of predicting errors is introduced to represent the accuracy of the established metamodel. Then, a weighted formula, which takes the degree of dispersion and the prediction accuracy into consideration, is defined based on the already-acquired CV information to adaptively update the metamodel during the optimization process.

Findings

Ten numerical examples with different degrees of complexity and an AIAA aerodynamic design optimization problem are used to demonstrate the effectiveness of the proposed CV-LCB approach. The comparisons between the proposed approach and four existing approaches regarding the computational efficiency and robustness are made. Results illustrate the merits of the proposed CV-LCB approach in computational efficiency and robustness.

Practical implications

The proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations.

Originality/value

CV-LCB approach can balance the exploration and exploitation objectively.

Details

Engineering Computations, vol. 36 no. 3
Type: Research Article
ISSN: 0264-4401

Keywords

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Article
Publication date: 4 July 2016

Ping Jiang, Qi Zhou, Xinyu Shao, Ren Long and Hui Zhou

The purpose of this paper is to present a modified bi-level integrated system collaborative optimization (BLISCO) to avoid the non-separability of the original BLISCO…

Abstract

Purpose

The purpose of this paper is to present a modified bi-level integrated system collaborative optimization (BLISCO) to avoid the non-separability of the original BLISCO. Besides, to mitigate the computational burden caused by expensive simulation codes and employ both efficiently simplified and expensively detailed information in multidisciplinary design optimization (MDO), an effective framework combining variable fidelity metamodels (VFM) and modified BLISCO (MBLISCO) (VFM-MBLISCO) is proposed.

Design/methodology/approach

The concept of the quasi-separable MDO problems is introduced to limit range of applicability about the BLISCO method and then based on the quasi-separable MDO form, the modification of BLISCO method without any derivatives is presented to solve the problems of BLISCO. Besides, an effective framework combining VFM-MBLISCO is presented.

Findings

One mathematical problem conforms to the quasi-separable MDO form is tested and the overall results illustrate the feasibility and robustness of the MBLISCO. The design of a Small Waterplane Area Twin Hull catamaran demonstrates that the proposed VFM-MBLISCO framework is a feasible and efficient design methodology in support of design of engineering products.

Practical implications

The proposed approach exhibits great capability for MDO problems with tremendous computational costs.

Originality/value

A MBLISCO is proposed which can avoid the non-separability of the original BLISCO and an effective framework combining VFM-MBLISCO is presented to efficiently integrate the different fidelities information in MDO.

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Article
Publication date: 20 July 2010

F.A. DiazDelaO and S. Adhikari

In the dynamical analysis of engineering systems, running a detailed high‐resolution finite element model can be expensive even for obtaining the dynamic response at few…

Abstract

Purpose

In the dynamical analysis of engineering systems, running a detailed high‐resolution finite element model can be expensive even for obtaining the dynamic response at few frequency points. To address this problem, this paper aims to investigate the possibility of representing the output of an expensive computer code as a Gaussian stochastic process.

Design/methodology/approach

The Gaussian process emulator method is discussed and then applied to both simulated and experimentally measured data from the frequency response of a cantilever plate excited by a harmonic force. The dynamic response over a frequency range is approximated using only a small number of response values, obtained both by running a finite element model at carefully selected frequency points and from experimental measurements. The results are then validated applying some adequacy diagnostics.

Findings

It is shown that the Gaussian process emulator method can be an effective predictive tool for medium and high‐frequency vibration problems, whenever the data are expensive to obtain, either from a computer‐intensive code or a resource‐consuming experiment.

Originality/value

Although Gaussian process emulators have been used in other disciplines, there is no knowledge of it having been implemented for structural dynamic analyses and it has good potential for this area of engineering.

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Article
Publication date: 5 March 2018

Xiwen Cai, Haobo Qiu, Liang Gao, Xiaoke Li and Xinyu Shao

This paper aims to propose hybrid global optimization based on multiple metamodels for improving the efficiency of global optimization.

Abstract

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.

Details

Engineering Computations, vol. 35 no. 1
Type: Research Article
ISSN: 0264-4401

Keywords

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Article
Publication date: 11 November 2013

Pietro Marco Congedo, Gianluca Geraci, Rémi Abgrall, Valentino Pediroda and Lucia Parussini

– This paper aims to deal with an efficient strategy for robust optimization when a large number of uncertainties are taken into account.

Abstract

Purpose

This paper aims to deal with an efficient strategy for robust optimization when a large number of uncertainties are taken into account.

Design/methodology/approach

ANOVA analysis is used in order to perform a variance-based decomposition and to reduce stochastic dimension based on an appropriate criterion. A massive use of metamodels allows reconstructing response surfaces for sensitivity indexes in the design variables plan. To validate the proposed approach, a simplified configuration, an inverse problem on a 1D nozzle flow, is solved and the performances compared to an exact Monte Carlo reference solution. Then, the same approach is applied to the robust optimization of a turbine cascade for thermodynamically complex flows.

Findings

First, when the stochastic dimension is reduced, the error on the variance between the reduced and the complete problem was found to be roughly estimated by the quantity (1− TSI )×100, where TSI is the summation of TSI concerning the variables respecting the TSI criterion. Second, the proposed strategy allowed obtaining a converged Pareto front with a strong reduction of computational cost by preserving the same accuracy.

Originality/value

Several articles exist in literature concerning robust optimization but very few dealing with a global approach for solving optimization problem affected by a large number of uncertainties. Here, a practical and efficient approach is proposed that could be applied also to realistic problems in engineering field.

Details

Engineering Computations, vol. 30 no. 8
Type: Research Article
ISSN: 0264-4401

Keywords

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Article
Publication date: 21 June 2019

Milad Yousefi and Moslem Yousefi

The complexity and interdisciplinarity of healthcare industry problems make this industry one of the attention centers of computer-based simulation studies to provide a…

Abstract

Purpose

The complexity and interdisciplinarity of healthcare industry problems make this industry one of the attention centers of computer-based simulation studies to provide a proper tool for interaction between decision-makers and experts. The purpose of this study is to present a metamodel-based simulation optimization in an emergency department (ED) to allocate human resources in the best way to minimize door to doctor time subject to the problem constraints which are capacity and budget.

Design/methodology/approach

To obtain the objective of this research, first the data are collected from a public hospital ED in Brazil, and then an agent-based simulation is designed and constructed. Afterwards, three machine-learning approaches, namely, adaptive neuro-fuzzy inference system (ANFIS), feed forward neural network (FNN) and recurrent neural network (RNN), are used to build an ensemble metamodel through adaptive boosting. Finally, the results from the metamodel are applied in a discrete imperialist competitive algorithm (ICA) for optimization.

Findings

Analyzing the results shows that the yellow zone section is considered as a potential bottleneck of the ED. After 100 executions of the algorithm, the results show a reduction of 24.82 per cent in the door to doctor time with a success rate of 59 per cent.

Originality/value

This study fulfils an identified need to optimize human resources in an ED with less computational time.

Details

Kybernetes, vol. 49 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

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Article
Publication date: 16 April 2018

Qi Zhou, Xinyu Shao, Ping Jiang, Tingli Xie, Jiexiang Hu, Leshi Shu, Longchao Cao and Zhongmei Gao

Engineering system design and optimization problems are usually multi-objective and constrained and have uncertainties in the inputs. These uncertainties might…

Abstract

Purpose

Engineering system design and optimization problems are usually multi-objective and constrained and have uncertainties in the inputs. These uncertainties might significantly degrade the overall performance of engineering systems and change the feasibility of the obtained solutions. This paper aims to propose a multi-objective robust optimization approach based on Kriging metamodel (K-MORO) to obtain the robust Pareto set under the interval uncertainty.

Design/methodology/approach

In K-MORO, the nested optimization structure is reduced into a single loop optimization structure to ease the computational burden. Considering the interpolation uncertainty from the Kriging metamodel may affect the robustness of the Pareto optima, an objective switching and sequential updating strategy is introduced in K-MORO to determine (1) whether the robust analysis or the Kriging metamodel should be used to evaluate the robustness of design alternatives, and (2) which design alternatives are selected to improve the prediction accuracy of the Kriging metamodel during the robust optimization process.

Findings

Five numerical and engineering cases are used to demonstrate the applicability of the proposed approach. The results illustrate that K-MORO is able to obtain robust Pareto frontier, while significantly reducing computational cost.

Practical implications

The proposed approach exhibits great capability for practical engineering design optimization problems that are multi-objective and constrained and have uncertainties.

Originality/value

A K-MORO approach is proposed, which can obtain the robust Pareto set under the interval uncertainty and ease the computational burden of the robust optimization process.

Details

Engineering Computations, vol. 35 no. 2
Type: Research Article
ISSN: 0264-4401

Keywords

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