Neural Networks for Surrogate-assisted Evolutionary optimization of Chemical Processes

Janus, Tim; Luubbers, Anne; Engell, Sebastian

doi:10.1109/cec48606.2020.9185781

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Generally speaking, the fitness approximation mainly includes three procedures: data processing, surrogate model building, and model update and management. In order to approximate the fitness function and build surrogates accurately, various model building methods can be used, including traditional interpolation methods, such as polynomial regression model [34,35] , and machine learning techniques, such as the Kriging model [36−38] , artificial neural networks [39,40] , radial basis function neural networks [41−44] , and random forest [45−47] . To better show the application scope of different surrogates, the comparison of some commonly seen surrogates is presented in Table 1.…”

Section: Fitness Approximationmentioning

confidence: 99%

“…KTA2 [37] Expensive, many-objective Influential point-insensitive model with an adaptive infill criterion 2021 IEEE Transactions on Cybernetics GCS-MOE [38] Expensive, many-objective Multi-task surrogate to approximate subproblems 2019 IEEE Transactions on Cybernetics MTCNP [39] Expensive, multi-task Surrogate-assisted multi-task learning 2020…”

Section: Ieee Transactions On Evolutionary Computationmentioning

confidence: 99%

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Evolutionary Computation for Expensive Optimization: A Survey

Zhan

Zhang

2022

Mach. Intell. Res.

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Expensive optimization problem (EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solution. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization approach for EOP. Among various optimization approaches, evolutionary computation (EC) is a promising global optimization tool widely used for solving EOP efficiently in the past decades. Given the fruitful advancements of EC for EOP, it is essential to review these advancements in order to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey to show why and how EC can solve EOP efficiently. For this aim, this paper firstly analyzes the total optimization cost of EC in solving EOP. Then, based on the analysis, three promising research directions are pointed out for solving EOP, which are problem approximation and substitution, algorithm design and enhancement, and parallel and distributed computation. Note that, to the best of our knowledge, this paper is the first that outlines the possible directions for efficiently solving EOP by analyzing the total expensive cost. Based on this, existing works are reviewed comprehensively via a taxonomy with four parts, including the above three research directions and the real-world application part. Moreover, some future research directions are also discussed in this paper. It is believed that such a survey can attract attention, encourage discussions, and stimulate new EC research ideas for solving EOP and related real-world applications more efficiently.

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Section: Fitness Approximationmentioning

confidence: 99%

Section: Ieee Transactions On Evolutionary Computationmentioning

confidence: 99%

Evolutionary Computation for Expensive Optimization: A Survey

Zhan

Zhang

2022

Mach. Intell. Res.

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“…Then in Sect. 4 we explain the machine learning (ML) methods that we used to speed up the optimization process 13. Afterwards, we investigate how these changes improve the performance of the optimization and show that the turnaround time between an engineer and the framework is significantly reduced.…”

Section: Introductionmentioning

confidence: 99%

Iterative Process Design with Surrogate‐Assisted Global Flowsheet Optimization

Janus

Engell

2021

Chemie Ingenieur Technik

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Flowsheet optimization is an important part of process design where commercial process simulators are widely used, due to their extensive library of models and ease of use. However, the application of a framework for global flowsheet optimization upon them is computationally expensive. Based on machine learning methods, we added mechanisms for rejection and generation of candidates to a framework for global flowsheet optimization. These extensions halve the amount of time needed for optimization such that the integration of the framework in a workflow for iterative process design becomes applicable.

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