A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing

Moiz, Ahmed Abdul; Pal, Pinaki; Probst, Daniel; Pei, Yuanjiang; Zhang, Yu; Som, Sibendu; Kodavasal, Janardhan

doi:10.4271/2018-01-0190

Cited by 82 publications

(58 citation statements)

References 38 publications

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“…The ML-GA is an optimization technique that can be used to efficiently optimize internal combustion engines. More details about this approach can be found in Moiz et al [25] and only a brief description of the main features is provided here. In an engine optimization task, the optimization problem is first posed and the different engine design parameters are defined.…”

Section: Definition and Detailsmentioning

confidence: 99%

“…The ML optimization algorithm was then used without any further CFD modeling to search for optimum engine design parameters. Their approach yielded comparable optimum operating conditions in significantly less computing time compared to the CFD-GA approach [25]. Other studies that implemented ML-GA algorithms for engine optimization may be found in the literature [7,[10][11][12].…”

Section: Introductionmentioning

confidence: 98%

“…Other ML algorithms, such as random forest and support vector machines, have also been used in engine and vehicle-related problems [22][23][24]. Recently, Moiz et al [25] proposed an improved Machine Learning-Genetic Algorithm (ML-GA) approach based on an ensemble machine learning technique known as SuperLearner [26], to optimize the operating conditions of a heavy-duty (HD) gasoline compression ignition engine (GCI). DoE was first performed to generate a database to train an ML algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, an ML-GGA (Machine Learning Grid Gradient Ascent) approach, with similar functionality to that of Moiz et al [25] is proposed. The developed ML-GGA code builds upon the work of Moiz et al [25]. In addition, the repeatability of the optimization method is investigated.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Badra

Khaled

Tang

et al. 2020

Journal of Energy Resources Technology

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Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Algorithm (ML-GGA) approach was developed to optimize the performance of internal combustion engines. Machine learning (ML) offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine learning Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variables limits extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a Research Octane Number (RON) of 80. The ML-GGA approach yielded > 2% improvements in the merit function, compared to the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared to traditional approaches.

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Section: Definition and Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Badra

Khaled

Tang

et al. 2020

Journal of Energy Resources Technology

Self Cite

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“…Therefore, the author of Reference [11] combined the EMI technology and ANN (artificial neural network, ANN) to recognize changes in the structural surface, in which a large number of training samples are required. For damage parameters identification, there are many feature selection methods like firefly algorithm [12], PSO (particle swarm optimization, PSO) [13], differential evolution [14], and genetic algorithm [15], but in this paper, the EMI method sensitive to a structural state change, combined with the AI optimization algorithm based on the concept of PSO is first proposed to recognize minor structural damages, which is mainly because that the PSO algorithm has the characteristics of fewer parameters, simple implementation, fast computing speed, etc. [16] Although many advantages are mentioned above, however, in the standard PSO algorithm, the particles converge in the form of orbits, and the search space of the particles is limited and cannot cover the entire space.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Recognition Based on the Finite Element Method and Quantum Particle Swarm Optimization Algorithm

Zhang

et al. 2020

IEEE Access

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Structural damage recognition is always the concerned focus in many fields like aerospace, petroleum and petrochemical industry, industrial production and civil life. For damage recognition in complex structure or structural interior, especially somewhere sensors can't go, minor damage is often hard identified by not only traditional nondestructive testing methods like ultrasonic testing, radiographic testing, magnetic particle testing, penetrant testing, eddy current testing, but also the current popular ultrasonic guided wave based on the piezoelectric wafer, electromagnetic acoustic transducer or magnetostrictive sensor, which is mainly because the response signals are always affected by many structural features. In this article, the advanced global search algorithm, quantum particle swarm optimization algorithm is first combined with the finite element method to accurately recognize the structural damage based on the conductance-frequency spectrum resulted from electromechanical impedance method. Meanwhile, the objective function is designed to compare the difference of peak frequency variations in the experiment and finite element calculation respectively. By adopting the stiffness reduction method of the elements near the structural damage, the identification efficiency is largely improved for no need to repeatedly partition the model grid. And after multiple iteration optimization of the artificial intelligence algorithm-quantum particle swarm optimization algorithm QPSO, the identification error of damage parameters including location and degree can be reduced to below 4 percent. Therefore, the combination of finite element method and quantum particle swarm optimization algorithm is quite effective for guaranteeing high accuracy and efficiency for damage parameters' recognition in complex structures.

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Using CNNs to Optimize Numerical Simulations in Geotechnical Engineering

Wolf

Donzallaz

Jost

et al. 2020

Artificial Neural Networks in Pattern Recognition

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A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing

Cited by 82 publications

References 38 publications

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Structural Damage Recognition Based on the Finite Element Method and Quantum Particle Swarm Optimization Algorithm

Using CNNs to Optimize Numerical Simulations in Geotechnical Engineering

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