The multi-objective optimisation of breakwaters using evolutionary approach

Nikitin, Nikolay O.; Polonskaia, Iana S.; Kalyuzhnaya, Anna V.; Boukhanovsky, Alexander V.

doi:10.1201/9781003216599-82

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…There are various parameterization that can be used to represent the structures, Cartesian coordinates just one of them. In some cases, right choice of parameterization can provide improvement in optimization process, in [3] angular encoding approach demonstrates superiority over relative spatial coordinates.…”

Section: Problem Statementmentioning

confidence: 99%

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Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

Starodubcev¹,

Nikitin²,

Kalyuzhnaya³

2022

Preprint

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In the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convolutional neural network is used as a surrogate model. This model allows optimizing a configuration of breakwaters with a different number of structures and segments. In addition to the surrogate, an assistant model was developed to estimate the confidence of predictions. The proposed approach was tested on the synthetic water area, the SWAN model was used to calculate the wave heights. The experimental results confirm that the proposed approach allows obtaining more effective (less expensive with better protective properties) solutions than non-surrogate approaches for the same time.

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Section: Problem Statementmentioning

confidence: 99%

“…To increase efficiency, reduce the cost of construction and operation, as well as to reduce the time spent by expert designers, researchers formulate and solve a multiobjective optimization problem [2]. Evolutionary algorithms (EA) are widely used for solving such tasks [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

Starodubcev¹,

Nikitin²,

Kalyuzhnaya³

2022

Preprint

Self Cite

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“…The idea of evolutionary generative design allows the algorithm to extend the possible design from the creation and improvement of the digital twins of real-world objects [ 20 ]. Researchers have also successfully applied evolutionary algorithms and other AI methods in different areas, including studies for coastal structures [ 21 ], mathematical models [ 22 ], architecture [ 23 ], and drug designs [ 24 ]. However, the adaptation of this approach to the hydrodynamic cell trappings has specific factors that should be taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Single Red Blood Cell Hydrodynamic Traps via the Generative Design

et al. 2022

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This paper describes a generative design methodology for a micro hydrodynamic single-RBC (red blood cell) trap for applications in microfluidics-based single-cell analysis. One key challenge in single-cell microfluidic traps is to achieve desired through-slit flowrates to trap cells under implicit constraints. In this work, the cell-trapping design with validation from experimental data has been developed by the generative design methodology with an evolutionary algorithm. L-shaped trapping slits have been generated iteratively for the optimal geometries to trap living-cells suspended in flow channels. Without using the generative design, the slits have low flow velocities incapable of trapping single cells. After a search with 30,000 solutions, the optimized geometry was found to increase the through-slit velocities by 49%. Fabricated and experimentally tested prototypes have achieved 4 out of 4 trapping efficiency of RBCs. This evolutionary algorithm and trapping design can be applied to cells of various sizes.

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Interpretable Structural Analysis for Evolutionary Generative Design of Coastal Breakwaters

Sidorenko,

Starodubcev,

Pinchuk

et al. 2024

Communications in Computer and Information Science

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This paper presents an interpretable approach for the generative design of coastal breakwaters that combines evolutionary optimization and structural analysis of solutions. It allows both to improve the convergence of optimization for breakwaters structure and analyze the sensitivity of each sub-part of the solution to various changes. We conduct experiments on synthetic harbour configuration to validate this approach. The results confirm the effectiveness of our method in generating high-quality and explainable breakwater designs.

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The multi-objective optimisation of breakwaters using evolutionary approach

Cited by 4 publications

References 21 publications

Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

Single Red Blood Cell Hydrodynamic Traps via the Generative Design

Interpretable Structural Analysis for Evolutionary Generative Design of Coastal Breakwaters

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