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Bibliografie tematiche / Combinatorial optimization layers

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Letteratura scientifica selezionata sul tema "Combinatorial optimization layers"

Autore: Grafiati

Pubblicato: 1 marzo 2025

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Articoli di riviste sul tema "Combinatorial optimization layers"

1

Kim, Bomi, Taehyeon Kim, and Yoonsik Choe. "Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning." Applied Sciences 11, no. 5 (2021): 2171. http://dx.doi.org/10.3390/app11052171.

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Incremental learning is a methodology that continuously uses the sequential input data to extend the existing network’s knowledge. The layer sharing algorithm is one of the representative methods which leverages general knowledge by sharing some initial layers of the existing network. To determine the performance of the incremental network, it is critical to estimate how much the initial convolutional layers in the existing network can be shared as the fixed feature extractors. However, the existing algorithm selects the sharing configuration through improper optimization strategy but a brute

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Deng, Beiming, Lijia Xia, and Hui Cheng. "Bilayer Real Time Multi-Robot Communication Maintenance Deployment Framework for Robot Swarms." Journal of Physics: Conference Series 2850, no. 1 (2024): 012011. http://dx.doi.org/10.1088/1742-6596/2850/1/012011.

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Abstract The communication maintenance problem of robot swarms is important to multi-robot control in applications like rescue and area exploration. In this paper, we propose a robot-relay-based framework to keep the robot swarm connected from the view of Line-of-Sight communication. This framework mainly consists of a graphic calculation layer and a numerical optimization layer. The combination of the two layers is intent on blending the combinatorial identity of the problem and the advantage of the differentiable nature of this numerical optimization problem and forming a pipeline for genera

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Lábadi, Zoltán, Noor Taha Ismaeel, Péter Petrik, and Miklós Fried. "Compositional Optimization of Sputtered SnO2/ZnO Films for High Coloration Efficiency." International Journal of Molecular Sciences 25, no. 19 (2024): 10801. http://dx.doi.org/10.3390/ijms251910801.

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We performed an electrochromic investigation to optimize the composition of reactive magnetron-sputtered mixed layers of zinc oxide and tin oxide (ZnO-SnO2). Deposition experiments were conducted as a combinatorial material synthesis approach. The binary system for the samples of SnO2-ZnO represented the full composition range. The coloration efficiency (CE) was determined for the mixed oxide films with the simultaneous measurement of layer transmittance, in a conventional three-electrode configuration, and an electric current was applied by using organic propylene carbonate electrolyte cells.

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Chebakov, Sergey V., and Liya V. Serebryanaya. "Finding algorithm of optimal subset structure based on the Pareto layers in the knapsack problem." Journal of the Belarusian State University. Mathematics and Informatics, no. 2 (July 30, 2020): 97–104. http://dx.doi.org/10.33581/2520-6508-2020-2-97-104.

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An algorithm is developed for finding the structure of the optimal subset in the knapsack problem based on the proposed multicriteria optimization model. A two-criteria relation of preference between elements of the set of initial data is introduced. This set has been split into separate Pareto layers. The depth concept of the elements dominance of an individual Pareto layer is formulated. Based on it, conditions are determined under which the solution to the knapsack problem includes the first Pareto layers. They are defined on a given set of initial data. The structure of the optimal subset

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Wu, Kee Rong, and Chung Wei Yeh. "Solution to the 0-1 Multidimensional Knapsack Problem Based on DNA Computation." Applied Mechanics and Materials 58-60 (June 2011): 1767–72. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1767.

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We proposed a two-layer scheme of Deoxyribonucleic acid (DNA) based computation, DNA-01MKP, to solve the typical NP-hard combinatorial optimization problem, 0-1 multidimensional knapsack problem (0-1 MKP). DNA-01MKP consists of two layers of procedures: (1) translation of the problem equations to strands and (2) solution of problems. For layer 1, we designed flexible well-formatted strands to represent the problem equations; for layer 2, we constructed the DNA algorithms to solve the 0-1 MKP. Our results revealed that this molecular computation scheme is able to solve the complicated operation

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Cao, Zhanmao, Qisong Huang, and Chase Wu. "Maximize concurrent data flows in multi-radio multi-channel wireless mesh networks." Computer Science and Information Systems 17, no. 3 (2020): 759–77. http://dx.doi.org/10.2298/csis200216019c.

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Multi-radio multi-channel (MRMC) wireless mesh networks (WMNs) have emerged as the broadband networks to provide access to the Internet for ubiquitous computing with the support for a large number of data flows. Many applications in WMNs can be abstracted as a multi-flow coexistence problem to carry out multiple concurrent data transfers. More specifically, links in different channel layers must be concatenated to compose multiple data transfer paths based on nodes? free interfaces and available channels. This is typically formulated as a combinatorial optimization problem with various stages

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Rahman, B. S., and D. K. Lieu. "Optimization of Magnetic Pole Geometry for Field Harmonic Control in Electric Motors." Journal of Vibration and Acoustics 116, no. 2 (1994): 173–78. http://dx.doi.org/10.1115/1.2930409.

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A principal source of vibration in permanent magnet motors and generators is the induced stress from the rotating permanent magnets. The harmonic content of this forcing function may excite resonant modes of vibration in the motor or surrounding structure. Thus attenuation of specific harmonics is of considerable interest. This paper describes a method for optimal shaping of the permanent magnets to eliminate one or more of these harmonics. The analytical model for an optimized 4-pole motor consisted of segmented PMs and a solid ring stator. The permanent magnets were modeled as a number of th

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Inga, Esteban, Juan Inga, and Andres Ortega. "Novel Approach Sizing and Routing of Wireless Sensor Networks for Applications in Smart Cities." Sensors 21, no. 14 (2021): 4692. http://dx.doi.org/10.3390/s21144692.

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Citizens are expected to require the growth of multiple Internet of Things (IoT) -based applications to improve public and private services. According to their concept, smart cities seek to improve the efficiency, reliability, and resilience of these services. Consequently, this paper searches for a new vision for resolving problems related to the quick deployment of a wireless sensor network (WSN) by using a sizing model and considering the capacity and coverage of the concentrators. Additionally, three different routing models of these technology resources are presented as alternatives for e

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Farhi, Edward, Jeffrey Goldstone, Sam Gutmann, and Leo Zhou. "The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size." Quantum 6 (July 7, 2022): 759. http://dx.doi.org/10.22331/q-2022-07-07-759.

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The Quantum Approximate Optimization Algorithm (QAOA) is a general-purpose algorithm for combinatorial optimization problems whose performance can only improve with the number of layers p. While QAOA holds promise as an algorithm that can be run on near-term quantum computers, its computational power has not been fully explored. In this work, we study the QAOA applied to the Sherrington-Kirkpatrick (SK) model, which can be understood as energy minimization of n spins with all-to-all random signed couplings. There is a recent classical algorithm by Montanari that, assuming a widely believed con

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Zhang, Xu, Pan Guo, Hua Zhang, and Jin Yao. "Hybrid Particle Swarm Optimization Algorithm for Process Planning." Mathematics 8, no. 10 (2020): 1745. http://dx.doi.org/10.3390/math8101745.

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Process planning is a typical combinatorial optimization problem. When the scale of the problem increases, combinatorial explosion occurs, which makes it difficult for traditional precise algorithms to solve the problem. A hybrid particle swarm optimization (HPSO) algorithm is proposed in this paper to solve problems of process planning. A hierarchical coding method including operation layer, machine layer and logic layer is designed in this algorithm. Each layer of coding corresponds to the decision of a sub-problem of process planning. Several genetic operators of the genetic algorithm are d

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Tesi sul tema "Combinatorial optimization layers"

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Bouvier, Louis. "Apprentissage structuré et optimisation combinatoire : contributions méthodologiques et routage d'inventaire chez Renault." Electronic Thesis or Diss., Marne-la-vallée, ENPC, 2024. http://www.theses.fr/2024ENPC0046.

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Cette thèse découle des défis de recherche opérationnelle de la chaîne logistique Renault. Pour y répondre, nous apportons des contributions à l’architecture et à l’entraînement des réseaux neuronaux avec des couches d’optimisation combinatoire (CO). Nous les combinons avec de nouvelles matheuristiques pour aborder les problèmes de routage d’inventaire de Renault. La Partie I est dédiée aux applications des réseaux neuronaux avec des couches CO en recherche opérationnelle. Nous introduisons une méthode pour approximer les contraintes. Nous utilisons de telles couches pour encoder des politique

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Kelareva, Galina Vladislavovna. "Development and applications of multi-layered genetic algorithms to multi-dimensional optimisation problems." Thesis, 2003. https://eprints.utas.edu.au/20554/7/whole_KelarevaGalinaVladislavovna2003.pdf.

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Genetic algorithms represent a global optimisation method, imitating the principles of natural evolution: selection and survival of the fittest. Genetic algorithms operate on a randomly initialised population of potential solutions to a problem. The solutions develop by passing valuable genetic information to succeeding generations. Genetic algorithms are known as a robust technique suitable for a variety of optimisation problems. However, when applied to complex combinatorial problems with multiple parameters, conventional genetic algorithms are usually slow and ineffective due to the

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Capitoli di libri sul tema "Combinatorial optimization layers"

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Cai, Xuhong, Li Jiang, Songhu Guo, Hejiao Huang, and Hongwei Du. "A Two-Layers Heuristic Search Algorithm for Milk Run with a New PDPTW Model." In Combinatorial Optimization and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64843-5_26.

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Ruthmair, Mario, and Günther R. Raidl. "A Layered Graph Model and an Adaptive Layers Framework to Solve Delay-Constrained Minimum Tree Problems." In Integer Programming and Combinatoral Optimization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20807-2_30.

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Awasthi, Abhishek, Jörg Lässig, Thomas Weise, and Oliver Kramer. "Tackling Common Due Window Problem with a Two-Layered Approach." In Combinatorial Optimization and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48749-6_59.

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Snapper, Marc L., and Amir H. Hoveyda. "Combinatorial approaches to chiral catalyst discovery." In Combinatorial Chemistry. Oxford University PressOxford, 2000. http://dx.doi.org/10.1093/oso/9780199637546.003.0016.

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Abstract In most combinatorial approaches to drug discovery, lead compounds are generally identified from large, structurally diverse libraries (1). Preliminary findings are then often optimized through the subsequent design and examination of more limited libraries that focus and expand on the initial results. In a similar manner, this layered approach to drug discovery and optimization can be adapted to catalyst development (Scheme 1, see also Chapters 14 and 15). In the first phase, a wide range of catalyst candidates can be screened to select specific complexes that effect a reaction of interest. Once a catalyst has been identified that demonstrates the desired reactivity, efforts can then tum toward optimizing the conditions and system to yield the desired selectivity. This two-tiered development strategy can offer distinct advantages in the discovery and identification of catalysts for asymmetric reactions. If the catalyst discovery and optimization protocols are sufficiently rapid and reliable, there is no prerequisite for finding general solutions to catalytic reactions; each reaction can enjoy a catalyst that is designed specifically for the substrate and transformation of interest.

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SUZUKI, KYOTARO, HIDEHARU AMANO, and YOSHIYASU TAKEFUJI. "MULTI-LAYER CHANNEL ROUTING PROBLEMS." In Neural Computing for Optimization and Combinatorics. WORLD SCIENTIFIC, 1996. http://dx.doi.org/10.1142/9789812832122_0005.

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James, Tabitha, and Cesar Rego. "Path Relinking with Multi-Start Tabu Search for the Quadratic Assignment Problem." In Recent Algorithms and Applications in Swarm Intelligence Research. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2479-5.ch004.

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This paper introduces a new path relinking algorithm for the well-known quadratic assignment problem (QAP) in combinatorial optimization. The QAP has attracted considerable attention in research because of its complexity and its applicability to many domains. The algorithm presented in this study employs path relinking as a solution combination method incorporating a multistart tabu search algorithm as an improvement method. The resulting algorithm has interesting similarities and contrasts with particle swarm optimization methods. Computational testing indicates that this algorithm produces results that rival the best QAP algorithms. The authors additionally conduct an analysis disclosing how different strategies prove more or less effective depending on the landscapes of the problems to which they are applied. This analysis lays a foundation for developing more effective future QAP algorithms, both for methods based on path relinking and tabu search, and for hybrids of such methods with related processes found in particle swarm optimization.

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Atti di convegni sul tema "Combinatorial optimization layers"

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Kul'ment'ev, Аlexander. "Artificial intelligence optimization method for nuclear fuel triso-elements in high-temperature reactor." In IXth INTERNATIONAL SAMSONOV CONFERENCE “MATERIALS SCIENCE OF REFRACTORY COMPOUNDS”. Frantsevich Ukrainian Materials Research Society, 2024. http://dx.doi.org/10.62564/m4-ak2225.

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In the present era nuclear energy, has unique advantages compared to other energy sources. Now significant research and development related to TRISO-coated fuels is underway worldwide as part of the activities of the Generation IV International Forum on Very-High-Temperature Reactors. The focus is largely on extending the capabilities of the TRISO-coated fuel system for higher operating temperatures (1250°C) and higher burnups (10 – 20 %). Of greatest concern is the influence of higher fuel temperatures and burnups on fission product interactions with the SiC layer leading to the release of fi

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Cao, Tianxiao, Lu Sun, Canh Hao Nguyen та Hiroshi Mamitsuka. "Learning Low-Rank Tensor Cores with Probabilistic ℓ0-Regularized Rank Selection for Model Compression". У Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/418.

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Compressing deep neural networks is of great importance for real-world applications on resource-constrained devices. Tensor decomposition is one promising answer that retains the functionality and most of the expressive power of the original deep models by replacing the weights with their decomposed cores. Decomposition with optimal ranks can achieve a good compression-accuracy trade-off, but it is expensive to optimize due to its discrete and combinatorial nature. A common practice is to set all ranks equal and tune one hyperparameter, but it may significantly harm the flexibility and general

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Bin Sazali, Muhammad Arif, Nahrul Khair Alang Md Rashid, and Khaidzir Hamzah. "Ant Colony Optimization of Multilayer Shielding for Mixed Neutron and Gamma Radiations: A Preliminary Study." In 2017 25th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/icone25-67765.

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Mixed neutron and gamma radiations require different shielding materials as their interaction with materials is different. Composites were developed in order to combine the shielding capabilities of different materials. However, their homogeneity is difficult to be assured which can lead to pinholes where radiation can penetrate. To avoid this problem, several materials arranged in layers can be used to shield against mixed radiations. Since the multilayer shielding can be made from any material in many configurations, the ant colony optimization (ACO) is a promising method because it deals wi

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Jurewicz, Mateusz, and Leon Derczynski. "Set Interdependence Transformer: Set-to-Sequence Neural Networks for Permutation Learning and Structure Prediction." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/434.

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The task of learning to map an input set onto a permuted sequence of its elements is challenging for neural networks. Set-to-sequence problems occur in natural language processing, computer vision and structure prediction, where interactions between elements of large sets define the optimal output. Models must exhibit relational reasoning, handle varying cardinalities and manage combinatorial complexity. Previous attention-based methods require n layers of their set transformations to explicitly represent n-th order relations. Our aim is to enhance their ability to efficiently model higher-ord

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Monteiro, Daniel Pereira, Lucas Nardelli de Freitas Botelho Saar, Larissa Ferreira Rodrigues Moreira, and Rodrigo Moreira. "On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds." In Workshop de Pesquisa Experimental da Internet do Futuro. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/wpeif.2024.2094.

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Novel applications demand high throughput, low latency, and high reliability connectivity and still pose significant challenges to slicing orchestration architectures. The literature explores network slicing techniques that employ canonical methods, artificial intelligence, and combinatorial optimization to address errors and ensure throughput for network slice data plane. This paper introduces the Enhanced Mobile Broadband (eMBB)-Agent as a new approach that uses Reinforcement Learning (RL) in a vertical application to enhance network slicing throughput to fit Service-Level Agreements (SLAs).

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Potebnia, Artem. "Construction of the comprehensive multi-layer graph model of the search spaces associated with the combinatorial optimization problems." In 2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T). IEEE, 2017. http://dx.doi.org/10.1109/infocommst.2017.8246398.

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Lin, Chen-Chou, Yi-Chih Chow, and Yu-Yu Huang. "Geometry Optimization of Cylindrical Flaps of Oscillating Wave Surge Converters Using Artificial Neural Network Models." In ASME 2019 13th International Conference on Energy Sustainability collocated with the ASME 2019 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/es2019-3878.

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Abstract This paper presents an optimization algorithm based on the Artificial Neural Network (ANN) to determine the optimal shape, size, and density for the cylindrical flap of the Bottom-Hinged Oscillating Wave Surge Converter (BH-OWSC) that can extract maximal wave power under a given wave condition. Eight parameters are selected, and their upper and lower bounds are set at the initial stage, and then 64 cases with different combinatorial parametric settings are generated by the Design of Experiment process. The 64 cases are then fed into FLOW-3D to simulate the operations of the BH-OWSC un

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