Efficient hierarchical surrogate-assisted differential evolution for high-dimensional expensive optimization

Chen, Guodong; Li, Yong; Zhang, Kai; Xue, Xiaoming; Wang, Jian; Luo, Qi; Yao, Chuanjin; Ye, Jun

doi:10.1016/j.ins.2020.06.045

Cited by 66 publications

(12 citation statements)

References 39 publications

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“…Chen et. al [51] presented hierarchical surrogateassisted differential evolution algorithm for high-dimensional expensive optimization problems with radial basis function network and benchmark functions were used and further application to oil reservoir production optimization problem provided promising results. Yi et.…”

Section: Surrogate-assisted Optimizationmentioning

confidence: 99%

“…Chen et. al [51] presented efficient hierarchical surrogate-assisted differential evolution for high-dimensional expensive optimization using global and local surrogate model featuring RBF network with an application to an oil reservoir production optimization problem. The results show that the method was effective for most benchmark functions and gave promising performance for reservoir production optimization problem.…”

Section: Surrogate-assisted Optimizationmentioning

confidence: 99%

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Surrogate-assisted distributed swarm optimisation for computationally expensive models

Chandra¹,

Vardhan²

2022

Preprint

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Advances in parallel and distributed computing have enabled efficient implementation of distributed swarm and evolutionary algorithms for complex and computationally expensive models. Evolutionary algorithms provide gradient-free optimisation which is beneficial for models that do not have such information available, for instance geoscientific landscape evolution models. However, such models are so computationally expensive that even distributed swarm and evolutionary algorithms with the power of parallel computing struggle. We need to incorporate efficient strategies such as surrogate assisted optimisation that further improves their performance; however this becomes a challenge given parallel processing and inter-process communication for implementing surrogate training and prediction. In this paper, we implement surrogate-based estimation of fitness evaluation in distributed swarm optimisation over a parallel computing architecture. Our results demonstrate very promising results for benchmark functions and geoscientific landscape evolution models. We obtain reduction in computationally time while retaining optimisation solution accuracy though the use of surrogates in a parallel computing environment.

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Section: Surrogate-assisted Optimizationmentioning

confidence: 99%

Section: Surrogate-assisted Optimizationmentioning

confidence: 99%

Surrogate-assisted distributed swarm optimisation for computationally expensive models

Chandra¹,

Vardhan²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Surrogate model (also called proxy) has gained increasing attention recently due to the promising ability on reducing the number of simulation runs during the optimization processes [27][28][29]. Various surrogate methods have been developed to save computational time, e.g., reduced order model (ROM) [30], capacitance-resistance model (CRM) [31] and machine learning methods [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning methods, which are computationally cheap mathematical models, can approximate the input/output relationship between the decision variables and the objective function [27,35]. Commonly used machine learning methods as surrogate are:…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [48] used a boosting strategy for model management and a localized data generation method to alleviate small sample problem on solving 10-100 dimensional problems (BDDEA-LDG). Chen et al [27] presented a hierarchical surrogate-assisted evolutionary algorithm (EHSDE) combining global prescreening strategy and local acceleration strategy on solving 20-100 dimensional problems, and the performance is promising in comparison with other state-of-the-art algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization

Chen

Luo

Jiao

et al. 2022

Fuel

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Radial basis function-assisted adaptive differential evolution using cooperative dual-phase sampling for high-dimensional expensive optimization problems

Shi

et al. 2022

Struct Multidisc Optim

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Efficient hierarchical surrogate-assisted differential evolution for high-dimensional expensive optimization

Cited by 66 publications

References 39 publications

Surrogate-assisted distributed swarm optimisation for computationally expensive models

Surrogate-assisted distributed swarm optimisation for computationally expensive models

Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization

Radial basis function-assisted adaptive differential evolution using cooperative dual-phase sampling for high-dimensional expensive optimization problems

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