Surrogate-based optimization for mixed-integer nonlinear problems

Kim, Sun Hye; Boukouvala, Fani

doi:10.1016/j.compchemeng.2020.106847

Cited by 56 publications

(30 citation statements)

References 58 publications

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“…For example, while analyzing laminated composite plates or shells, the number of plies is a discrete variable while other parameters like ply angle and material properties are continuous variables. Kim et al [389] compared the performance of ANN and gaussian process in the presence of mixed variable input space and found that GP works better in such cases due to its interpolating nature. Parallel optimization algorithms are proven to be the most efficient ones in handling the nonconvex mixed variable problems [390].…”

Section: Mixed Variable Problemsmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

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The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites.Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.

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Section: Mixed Variable Problemsmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…Simulation-based optimization approaches can be the most practical and efficient way to optimize problems with a large system of partial differential or ordinary differential equations or large and complex equations with discontinuities. In these problems, optimization relies on simulation input-output data and the derivatives of the original model are not directly used by an optimization solver [71]. Chen et al proposed a simulationbased simultaneous optimization and heat integration approach in which the process was designed in a process simulator (e.g., Aspen Plus), and the heat integration module was developed using optimization software (e.g., GAMS) for the minimization of the utility cost and heat exchanger area [72].…”

Section: Process Optimizationmentioning

confidence: 99%

Advancements in Optimization and Control Techniques for Intensifying Processes

et al. 2021

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Process Intensification (PI) is a vast and growing area in Chemical Engineering, which deals with the enhancement of current technology to enable improved efficiency; energy, cost, and environmental impact reduction; small size; and better integration with the other equipment. Since process intensification results in novel, but complex, systems, it is necessary to rely on optimization and control techniques that can cope with such new processes. Therefore, this review presents some advancements in the field of process intensification that are worthy of exploring in detail in the coming years. At the end, several important open questions that can be taken into consideration in the coming years are listed.

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“…Similar ideas are utilised in [22,28]. Another interesting new research direction is to combine the advantages of Gaussian processes and artificial neural networks [17], although more research is required to make this computationally feasible for larger problems. Other research groups have focused their attention on mixed-variable problems with multiple objectives [14,30].…”

Section: Related Workmentioning

confidence: 99%

Black-box mixed-variable optimisation using a surrogate model that satisfies integer constraints

Bliek

Guijt

Verwer

et al. 2021

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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A challenging problem in both engineering and computer science is that of minimising a function for which we have no mathematical formulation available, that is expensive to evaluate, and that contains continuous and integer variables, for example in automatic algorithm configuration. Surrogate-based algorithms are very suitable for this type of problem, but most existing techniques are designed with only continuous or only discrete variables in mind. Mixed-Variable ReLU-based Surrogate Modelling (MVRSM) is a surrogate-based algorithm that uses a linear combination of rectified linear units, defined in such a way that (local) optima satisfy the integer constraints. Unlike other methods, it also has a constant run-time per iteration. This method outperforms the state of the art on several synthetic benchmarks with up to 238 continuous and integer variables, and achieves competitive performance on two real-life benchmarks: XG-Boost hyperparameter tuning and Electrostatic Precipitator optimisation. CCS CONCEPTS• Mathematics of computing → Mathematical optimization; • Computing methodologies → Active learning settings; Search methodologies; • Theory of computation → Mixed discretecontinuous optimization.

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Surrogate-based optimization for mixed-integer nonlinear problems

Cited by 56 publications

References 58 publications

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Advancements in Optimization and Control Techniques for Intensifying Processes

Black-box mixed-variable optimisation using a surrogate model that satisfies integer constraints

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