SCGOSR: Surrogate-based constrained global optimization using space reduction

Dong, Huachao; Song, Baowei; Dong, Zuomin; Wang, Peng

doi:10.1016/j.asoc.2018.01.041

Cited by 62 publications

(22 citation statements)

References 45 publications

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“…It is noted that the optimized solutions by MAM and extended MAM are the best feasible designs since no violated constraints are observed in Table 5. SCGOSR [52] could find a near-optimal design with the cost of 5885.3653, which is slightly heavier than the result by extended MAM, but the first and second constraints are violated. In average, the extended MAM outperforms the MAM to seek the optimum in terms of the number of iterations used in the case studies with different starting points shown in Table 6.…”

Section: A Reactor Pressure Vessel Examplementioning

confidence: 93%

“…e design variables consist of the shell thickness T s , the Mathematical Problems in Engineering spherical head thickness T h , the radius of cylindrical shell R, and the shell length L. e detailed problem formulation is given in [35]. e comparison of results obtained by the extended MAM and other metamodel-based methods (SCGOSR [52], eDIRECT-C [53], ConstrLMSRBF [53], CORBA [53], and CiMPS [53]) has been presented in Table 4.…”

Section: A Reactor Pressure Vessel Examplementioning

confidence: 99%

“…e cost computed using the extended MAM or MAM has been further reduced to 5885.268 by 0.0009% from 5885.33, which was the best design referred in [52]. To build the metamodels at each iteration of the optimization process, nine sampling points are used in this example.…”

Section: A Reactor Pressure Vessel Examplementioning

confidence: 99%

See 2 more Smart Citations

Computationally Efficient Approximations Using Adaptive Weighting Coefficients for Solving Structural Optimization Problems

Dong

Liu

et al. 2021

Mathematical Problems in Engineering

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With rapid development of advanced manufacturing technologies and high demands for innovative lightweight constructions to mitigate the environmental and economic impacts, design optimization has attracted increasing attention in many engineering subjects, such as civil, structural, aerospace, automotive, and energy engineering. For nonconvex nonlinear constrained optimization problems with continuous variables, evaluations of the fitness and constraint functions by means of finite element simulations can be extremely expensive. To address this problem by algorithms with sufficient accuracy as well as less computational cost, an extended multipoint approximation method (EMAM) and an adaptive weighting-coefficient strategy are proposed to efficiently seek the optimum by the integration of metamodels with sequential quadratic programming (SQP). The developed EMAM stems from the principle of the polynomial approximation and assimilates the advantages of Taylor’s expansion for improving the suboptimal continuous solution. Results demonstrate the superiority of the proposed EMAM over other evolutionary algorithms (e.g., particle swarm optimization technique, firefly algorithm, genetic algorithm, metaheuristic methods, and other metamodeling techniques) in terms of the computational efficiency and accuracy by four well-established engineering problems. The developed EMAM reduces the number of simulations during the design phase and provides wealth of information for designers to effectively tailor the parameters for optimal solutions with computational efficiency in the simulation-based engineering optimization problems.

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Section: A Reactor Pressure Vessel Examplementioning

confidence: 93%

Section: A Reactor Pressure Vessel Examplementioning

confidence: 99%

See 1 more Smart Citation

Computationally Efficient Approximations Using Adaptive Weighting Coefficients for Solving Structural Optimization Problems

Dong

Liu

et al. 2021

Mathematical Problems in Engineering

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“…For kriging-based constraint optimization, the KCGO (kriging-based constrained global optimization) algorithm [11] can deal with the problem that the objective and constraints are black-box functions when all sampling points are infeasible. Combined with a space reduction strategy, the SCGOSR (surrogate-based constrained global optimization using space reduction) algorithm [40] also completes the optimization of the black-box constraint problem. In addition, based on the EI, feasibility probability, and prediction variance of the constraint function, the three-objective kriging-based constrained global optimization (TOKCGO) [41] method is realized.…”

Section: Testmentioning

confidence: 99%

A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Shen

Zi-liang

et al. 2021

Mathematics

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The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while improving the accuracy of convergence and reducing the number of expensive evaluations has been a wide concern. For this reason, a kriging-assisted multi-objective constrained global optimization (KMCGO) method has been proposed. The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle. Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective optimization. Finally, the sample data from the Pareto frontier set is further screened to obtain more promising and valuable sampling points. The test results of five benchmark functions, four design problems, and a fuel economy simulation optimization prove the effectiveness of the proposed algorithm.

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“…The quality of a surrogate model has a great influence on the computational cost or the convergence of the surrogate model-based design optimization problems. Under the limited computational budget, the quality of a surrogate model largely depends on the distribution of sample points [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

An adaptive sampling method for Kriging surrogate model with multiple outputs

Zhai

Wang³

2020

Engineering with Computers

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The sample distribution has a vital influence on the quality of a Kriging surrogate model, which may further influence the required cost or convergence of the surrogate model-based design and optimization problems. Adaptive sampling methods utilize the information from existing samples to reasonably allocate the sequential samples, which can generally build a more accurate Kriging surrogate model under the same computational budget. However, most of the existing adaptive sampling methods for the Kriging surrogate model are only available for single-output problems, and there are few studies on problems with multiple responses. In this paper, an adaptive sampling method based on Delaunay triangulation and technique for order preference by similarity to ideal solution (TOPSIS) is proposed for Kriging surrogate model with multiple outputs (mKMDT). In the proposed mKMDT, Delaunay triangulation is used to partition the design space into multiple triangle regions, whose area denotes the dispersion of the sample points. The prediction error at each triangle's centroid represents the local approximation error. Specifically, three different strategies are developed when allocating weights to the area and the prediction error of each triangle with the entropy method and the TOPSIS method. The performance of the proposed method is illustrated through numerical examples with different numbers of outputs and a collision problem between the missile and the adapter. Results show that the proposed method can construct an accuracy surrogate model with few samples, which is useful for practical engineering design problems with multiple outputs.

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SCGOSR: Surrogate-based constrained global optimization using space reduction

Cited by 62 publications

References 45 publications

Computationally Efficient Approximations Using Adaptive Weighting Coefficients for Solving Structural Optimization Problems

Computationally Efficient Approximations Using Adaptive Weighting Coefficients for Solving Structural Optimization Problems

A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

An adaptive sampling method for Kriging surrogate model with multiple outputs

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