Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems

Ong, Yew Soon; Nair, Prasanth B.; Keane, Andy J.; Wong, Ka-Chun

doi:10.1007/978-3-540-44511-1_15

Cited by 96 publications

(72 citation statements)

References 25 publications

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“…Several books and literature reviews have described the advances of surrogate-based optimization in recent years (e.g., Jones, 2001;Ong et al, 2005;Jin, 2011;Koziel and Leifsson, 2013;Wang et al, 2014). Surrogate-based optimization has been applied to economics, robotics, chemistry, physics, civil and environmental engineering, computational fluid dynamics, aerospace designs, etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective parameter optimization of common land model using adaptive surrogate modeling

Gong

Duan

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. Parameter specification usually has significant influence on the performance of land surface models (LSMs). However, estimating the parameters properly is a challenging task due to the following reasons: (1) LSMs usually have too many adjustable parameters (20 to 100 or even more), leading to the curse of dimensionality in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) Regional LSMs are expensive to run, while conventional multi-objective optimization methods need a large number of model runs (typically ∼ 10 5 -10 6 ). It makes parameter optimization computationally prohibitive. An uncertainty quantification framework was developed to meet the aforementioned challenges, which include the following steps: (1) using parameter screening to reduce the number of adjustable parameters, (2) using surrogate models to emulate the responses of dynamic models to the variation of adjustable parameters, (3) using an adaptive strategy to improve the efficiency of surrogate modeling-based optimization; (4) using a weighting function to transfer multi-objective optimization to single-objective optimization. In this study, we demonstrate the uncertainty quantification framework on a single column application of a LSM -the Common Land Model (CoLM), and evaluate the effectiveness and efficiency of the proposed framework. The result indicate that this framework can efficiently achieve optimal parameters in a more effective way. Moreover, this result implies the possibility of calibrating other large complex dynamic models, such as regionalscale LSMs, atmospheric models and climate models.

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Section: Introductionmentioning

confidence: 99%

Multi-objective parameter optimization of common land model using adaptive surrogate modeling

Gong

Duan

et al. 2015

Hydrol. Earth Syst. Sci.

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“…Therefore, it can provide a new idea and approach for the optimization design with great progress and successful application having been achieved in the field of engineering application. However, in practical engineering applications, it always takes several minutes, hours or a couple of days [1][2][3][4] each time to acquire the value of an objective function, such as the fluid mechanics [5][6][7] applied in wing design, the parameter settings in engine controller and the finite element analysis [18] adopted in mechanical structure design. Hence, for the swarm intelligence algorithm with the operating time decided by the computing times of a fitness function, it has gradually aroused the attention of many people about how to reduce the computing time of the objective function to reduce the time spent on optimization [8].…”

Section: Introductionmentioning

confidence: 99%

Efficient Query for Historical Data in Evolutionary Algorithm

Tian¹,

Pan²,

Huang³

2017

Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

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Abstract-For the time-consuming problem in calculating the fitness value, this paper proposes a hash bucket with precision mechanism for a quick query of the data in the neighborhood of a particle. In order to establish a balance between the calculation accuracy and utility, it uses the hash tables with precision mechanism to solve the problem in the storage and query of historical calculation data so that the neighborhood of a to-be-evaluated individual can be determined more accurately to reduce the error in estimating the fitness value. Moreover, it uses the typical reference functions to separately test the effectiveness and accuracy of the algorithms based on the values obtained in different dimensions. The test result proves that compared with the other algorithms described in this paper, our algorithm can provide a better solution in the context of the same number of times for fitness calculation.

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“…However, since constructing accurate surrogate models is less likely due to the curse of dimensionality, building local surrogate models has only been more intensively explored recently. Ong et al [14,15] combined an evolutionary algorithm with a sequential quadratic programming solver in the spirit of Lamarckian learning, in which the trustregion method for interleaving exact models for the objective and constraint functions with computationally cheap surrogate models during local search was employed. Fitness inheritance, which was first proposed by Smith et al [16], can be seen as a special local surrogate technique, where the fitness of the individual is inherited from its parents or other individuals.…”

Section: Introductionmentioning

confidence: 99%

A two-layer surrogate-assisted particle swarm optimization algorithm

et al. 2014

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Like most Evolutionary Algorithms (EAs), Particle Swarm Optimization (PSO) usually requires a large number of fitness evaluations to obtain a sufficiently good solution. This poses an obstacle for applying PSO to computationally expensive problems. This paper proposes a two-layer surrogateassisted PSO (TLSAPSO) algorithm, in which a global and a number of local surrogate models are employed for fitness approximation. The global surrogate model aims to smooth out the local optima of the original multimodal fitness function and guide the swarm to fly quickly to an optimum. In the meantime, a local surrogate model constructed using the data samples near the particle is built to achieve a fitness estimation as accurate as possible. The contribution of each surrogate in the search is empirically verified by experiments on uni-and multi-modal problems. The performance of the proposed TLSAPSO algorithm is examined on ten widely used benchmark problems, and the experimental results show that the proposed algorithm is effective and highly competitive with the state-of-the-art, especially for multimodal optimization problems.

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Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems

Cited by 96 publications

References 25 publications

Multi-objective parameter optimization of common land model using adaptive surrogate modeling

Multi-objective parameter optimization of common land model using adaptive surrogate modeling

Efficient Query for Historical Data in Evolutionary Algorithm

A two-layer surrogate-assisted particle swarm optimization algorithm

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