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A genetic engineering algorithm for the generalized quadratic assignment problem

Neural Computing and Applications 2025年 1-27页

作者： Sohrabi, Majid Fathollahi-Fard, Amir M. Gromov, Vasilii A. Dulebenets, Maxim A. School of Data Analysis and Artificial Intelligence National Research University Higher School of Economics 11 Pokrovsky Boulevard Moscow109028 Russia Laboratory for Models and Methods of Computational Pragmatics National Research University Higher School of Economics Moscow Russia Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Taipa China College of Engineering 2035 E Paul Dirac Dr. Sliger Building Suite 275 TallahasseeFL32310 United States

The generalized quadratic assignment problem (GQAP) poses a significant challenge in optimization, known for its NP-hard complexity and wide-ranging applications in supply chain and manufacturing contexts. While various metaheuristic algorithms have addressed the GQAP, this paper introduces a novel modification of the genetic algorithm (GA), namely the genetic engineering algorithm (GEA). A traditional GA initializes the population of solutions randomly and refines these solutions iteratively through crossover and mutation operators. However, its reliance on randomness often leads to premature convergence, susceptibility to local optima, and suboptimal solutions at termination of the algorithmic run. To address these drawbacks, our research presents a modified GA framework inspired by genetic engineering principles. This framework introduces novel search mechanisms mimicking gene mining processes, targeting population enhancement through insertion, purification, and genetic material exchange from elite solutions. The GEA adds three search scenarios to the traditional GA framework to exhibit desired characteristics from chromosomes, enhancing solution quality and convergence speed. This GEA demonstrates a superior performance across various search scenarios against state-of-the-art algorithms and exact solutions for the benchmark instances of the GQAP. Additionally, this study conducts comprehensive sensitivity analyses on the search scenarios of the GEA. By employing gene mining strategies, the proposed GEA offers promising solutions for effectively addressing the NP-hard optimization challenges of the GQAP on large-scale datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Sensitivity analysis

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On Managing Geometric Models for Multi-component, Multi-disciplinary Analysis and Design

On Managing Geometric Models for Multi-component, Multi-disc...

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AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2023

作者： Dannenhoffer, John F. Bhagat, Nitin Aerospace Computational Methods Laboratory Syracuse University Mechanical and Aerospace Engineering SyracuseNY13244 United States University of Dayton Research Institute Applied Mechanics Division DaytonOH45469 United States

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

Geometric models are central to the analysis and design of complex configurations, such as aerospace vehicles. As models expand to include more of the vehicle components, and to include more than one discipline, they become very complex and hard to manage. This makes understanding the linkages between components for a single discipline, or under- standing the linkages between the various discipline analysis for a single component, very difficult. This problem is compounded further when one realizes that the design process involves a series of sub-models (for any component and/or discipline) that evolve over time, in which the design is changed or fidelity is enhanced;these various versions must also be managed. Described herein is a new management scheme that directly attacks this problem. It centers around a set of user-defined component files which define the geometric models of the components that are needed by various analyses required for design. After describing the basic ideas of the new management scheme, it is demonstrated on a transport config- uration. Then tools that a user can employ to understand a model are described. All this is brought together in an exercise that converts a legacy (dusty-deck) model into the new management scheme. © 2023, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Design

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Transport Corrected Diffusion Reduced Order Model for Thermal Radiative Transfer

Transport Corrected Diffusion Reduced Order Model for Therma...

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2023 Transactions of the American Nuclear Society Annual Meeting and Technology Expo, ANS 2023

作者： Coale, Joseph M. Anistratov, Dmitriy Y. Computational Physics and Methods Group Los Alamos National Laboratory Los AlamosNM87545 United States Department of Nuclear Engineering North Carolina State University RaleighNC27695-7909 United States

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Novel MC TRT Method: Vectorizable Variance Reduction for Energy Spectra

Novel MC TRT Method: Vectorizable Variance Reduction for Ene...

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2022 Transactions of the American Nuclear Society Annual Meeting, ANS 2022

作者： Morgan, Jackson P. Long, Alex Long, Kendra Niemeyer, Kyle E. School of Mechanical Industrial and Manufacturing Engineering Oregon State University CorvallisOR97331 United States Computational Physics and Methods Group Los Alamos National Laboratory NM87545 United States United States

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Evolving Safety Protocols: Deep Learning-Enabled Detection of Personal Protective Equipment 1

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4th International Conference on Computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Alahmid, Mustafa Bhimani, Kishankumar Saradva, Khushbu Ghildiyal, Sushil Saeedi, Sayed Aryan Ali, Saraa Yan, Han Filippov, Evgeny Faculty of Computer Science and Technology Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Laboratory on AI for Computational Biology Faculty of Computer Science HSE University Moscow Russia Department of Computer Science and Engineering Indian Institute of Technology Ropar Punjab India Laboratory of Methods for Big Data Analysis Faculty of Computer Science HSE University Moscow Russia Deggendorf Institute of Technology Bavaria Germany

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks (Shrestha and Mahmood in IEEE Access 7:53,040–53,065, 2019 [25]) to construct an innovative AI model. The designed model detects the usage of personal protective equipment (PPE) (Personal protective equipment. Geneva: World Health Organization, 2020 [18]) by workers in high-risk industries such as construction and manufacturing. We have used Google’s TensorFlow object detection API (Sai and Sasikala in Object detection and count of objects in image using tensor flow object detection API, pp 542–546, 2019 [22]) to modify and train a model for dual purposes: PPE detection and face recognition. The state-of-the-art of this research is to substantially enhance safety compliance by addressing the prevalent issue of PPE non-compliance. To emphasis this, we have developed a pioneering software prototype that synergizes PPE detection with a face recognition-based clock-in system. This prototype demonstrates impressive object detection metrics with a mean average precision (mAP) of 0.9 for vests and 0.85 for helmets. Moreover, it exhibited efficient face recognition with a successful threshold range of 17–20%. The implementation of AI in our system promises significant enhancements to worker safety, while concurrently reducing the financial burden associated with big hazards and accidents. Beyond the development and performance of the system, this paper provides a thorough exploration of the encountered challenges, potential real-world applications (particularly in employee monitoring and clock-in systems), and the future implications of this study on research and practical applications in the field of AI-integrated safety compliance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software prototyping

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3d Multi-Species Kinetic-Fluid Coupling for High-Energy Density Simulations

SSRN

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SSRN 2023年

作者： Chuna, Thomas Sagert, Irina Murillo, Michael S. Haack, Jeffrey R. Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Computational Physics and Methods Group Los Alamos National Laboratory Los AlamosNM87545 United States

Many physics problems are subject to a mix of continuum and non-equilibrium flows or conditions where one transition into the other, as a function of time and/or space. Numerically, such flows could be described with a purely kinetic method which, in the limit of small particle mean free paths, reproduces a continuum regime. However, from a computational perspective, such approaches are usually very expensive or simply not feasible, even on modern computational architectures. A solution is the construction of hybrid methods which connect the kinetic and hydrodynamic system of evolution equations. Such coupling usually requires specific boundary conditions between non-equilibrium and continuum regimes which come with their own numerical challenges and can introduce unphysical effects. Here, we present a hybrid model which uses a buffer region to transition from a kinetic into a fluid description following the original work of Degond et al. (2005). In the buffer region, both, the kinetic and hydrodynamic equations are solved simultaneously while being coupled via a so-called transition function. The latter also ensures a smooth conversion from the coupled model to either the kinetic or continuum approach at the interfaces of the buffer region. With that, the method avoids the need to find direct interface boundary conditions and allows one to localize the use of a high dimensional kinetic model only where it is needed. In our work, we extend the original method of Degond to flows with multiple particle species in 3D. We derive the coupled equations for the multispecies Vlasov-Bhatnagar-Gross-Krook (VBGK) model and its limiting Euler or Navier-Stokes hydrodynamic equations. To validate our model numerically, we simulate a Sod shock problem and study the effect of kinetic multi-species mixing in the preheat phase of a high energy-density plasma physics experiment. © 2023, The Authors. All rights reserved.

关键词： Kinetics

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Bibliometric Study on Virtual Reality Applied to Military Training

Bibliometric Study on Virtual Reality Applied to Military Tr...

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International Conference on Electrical, Communication, and Computer engineering (ICECCE)

作者： Diogo Pacheco Salazar Araujo Romullo Girardi Jauvane C. de Oliveira Paulo Fernando Ferreira Rosa Department of Computer Engineering (SE19) Military Institute of Engineering (IME) Rio de Janeiro Brazil Coordination of Mathematical and Computational Methods National Laboratory for Scientific Computing (LNCC) Rio de Janeiro Brazil Laboratory of AI Robotics and Cybernetics (LIARC) Military Institute of Engineering (IME) Rio de Janeiro Brazil

ISBN: (数字)9798331529437

ISBN: (纸本)9798331529444

In the defense domain, the adoption of Virtual Reality (VR)-based training simulators has significantly increased in recent years in response to the limitations of traditional military training methods. This paper presents a quantitative approach study based on bibliometric metrics to assess the use of VR in defense. Using the Scopus and Web of Science databases as references, the study aimed to identify various aspects related to the research field: distribution of publications, most relevant sources, dynamics of interaction among these sources, prominent topics in recent research, as well as prevalent application areas and supporting technologies. Thus, the study provides valuable insights into the current state and research trends of VR applied to the military context.

关键词： Training Measurement Databases Bibliometrics Virtual reality Market research

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Griffin: A Moose-Based Reactor Physics Application for Multiphysics Simulation of Advanced Nuclear Reactors

SSRN

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SSRN 2024年

作者： Wang, Yaqi Prince, Zachary Merritt Calvin, William Olin Park, Hansol Choi, Namjae Jung, Yeon Sang Schunert, Sebastian Kumar, Shikhar Hanophy, Joshua T. Labouré, Vincent M. Lee, Changho Ortensi, Javier Harbour, Logan H. Harter, Jackson R. Reactor Physics Methods and Analysis Idaho National Laboratory Idaho FallsID United States Nuclear Science and Engineering Argonne National Laboratory LemontIL United States Computational Frameworks Idaho National Laboratory Idaho FallsID United States University of Idaho United States

Griffin is a MOOSE based reactor physics application for multiphysics simulations of advanced reactor designs jointly developed by Idaho National laboratory and Argonne National laboratory. This paper summarizes the motivation, significance, architecture, design, and features of Griffin. The most fundamental aspect that sets Griffin apart from other reactor analysis codes is that it is developed based on the MOOSE framework. A modular development approach is strongly enforced, with multiphysics being an essential element considered since the beginning of Griffin's development. Griffin uses various finite element methods for spatial discretization, multigroup approximation for energy discretization and discrete ordinates method, spherical harmonics expansion method, and diffusion approximation for streaming direction discretization to solve the neutron transport equation. Griffin has been adopted into the reactor analysis system for the U.S. NRC and is in use at U.S. companies, universities and national laboratories. © 2024, The Authors. All rights reserved.

关键词： Multiphysics

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Chlorine Worth Study and Nuclear Data

Chlorine Worth Study and Nuclear Data

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2024 Annual Conference on Transactions of the American Nuclear Society, ANS 2024

作者： Thompson, Nicholas W. Cutler, Theresa Amundson, Kelsey Favorite, Jeffrey A. Grove, Travis Hutchinson, Jesson Kleedtke, Noah Advanced Nuclear Technology Group Nuclear Engineering and Nonproliferation Division Los Alamos National Laboratory P.O. Box 1663 Los AlamosNM87545 United States Radiation Transport Applications Group X-Theoretical Design Division Los Alamos National Laboratory P.O. Box 1663 Los AlamosNM87545 United States Monte Carlo Methods Codes and Applications Group X-Computational Physics Division Los Alamos National Laboratory P.O. Box 1663 Los AlamosNM87545 United States

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Physics-constrained coupled neural differential equations for one dimensional blood flow modeling

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Computers in Biology and Medicine 2025年 186卷 109644-109644页

作者： Csala, Hunor Mohan, Arvind Livescu, Daniel Arzani, Amirhossein Department of Mechanical Engineering University of Utah Salt Lake CityUT United States Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT United States Computational Physics and Methods Los Alamos National Laboratory Los AlamosNM United States

Background: computational cardiovascular flow modeling plays a crucial role in understanding blood flow dynamics. While 3D models provide acute details, they are computationally expensive, especially with fluid–structure interaction (FSI) simulations. 1D models offer a computationally efficient alternative, by simplifying the 3D Navier–Stokes equations through axisymmetric flow assumption and cross-sectional averaging. However, traditional 1D models based on finite element methods (FEM) often lack accuracy compared to 3D averaged solutions. methods: This study introduces a novel physics-constrained machine learning technique that enhances the accuracy of 1D cardiovascular flow models while maintaining computational efficiency. Our approach, utilizing a physics-constrained coupled neural differential equation (PCNDE) framework, demonstrates superior performance compared to conventional FEM-based 1D models across a wide range of inlet boundary condition waveforms and stenosis blockage ratios. A key innovation lies in the spatial formulation of the momentum conservation equation, departing from the traditional temporal approach and capitalizing on the inherent temporal periodicity of blood flow. Results: This spatial neural differential equation formulation switches space and time and overcomes issues related to coupling stability and smoothness, while simplifying boundary condition implementation. The model accurately captures flow rate, area, and pressure variations for unseen waveforms and geometries, having 3–5 times smaller error than 1D FEM, and less than 1.2% relative error compared to 3D averaged training data. We evaluate the model's robustness to input noise and explore the loss landscapes associated with the inclusion of different physics terms. Conclusion: This advanced 1D modeling technique offers promising potential for rapid cardiovascular simulations, achieving computational efficiency and accuracy. By combining the strengths of physics-based and data-d

关键词： Digital elevation model

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