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1
Kim, Bomi, Taehyeon Kim, and Yoonsik Choe. "Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning." Applied Sciences 11, no. 5 (March 1, 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 force manner such as searching for all possible sharing layers case. This is a non-convex and non-differential problem. Accordingly, this can not be solved using powerful optimization techniques such as the gradient descent algorithm or other convex optimization problem, and it leads to high computational complexity. To solve this problem, we firstly define this as a discrete combinatorial optimization problem, and propose a novel efficient incremental learning algorithm-based Bayesian optimization, which guarantees the global convergence in a non-convex and non-differential optimization. Additionally, our proposed algorithm can adaptively find the optimal number of sharing layers via adjusting the threshold accuracy parameter in the proposed loss function. With the proposed method, the global optimal sharing layer can be found in only six or eight iterations without searching for all possible layer cases. Hence, the proposed method can find the global optimal sharing layers by utilizing Bayesian optimization, which achieves both high combined accuracy and low computational complexity.
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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 (September 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 generating a swarm deployment solution with low communication loss, low energy consumption, and high efficiency.
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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 (October 8, 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. The optical parameters and composition were measured and mapped by using spectroscopic ellipsometry (SE). Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDS) measurements were carried out to check the SE results, for (TiO2-SnO2). Pure metal targets were placed separately from each other, and the indium–tin-oxide (ITO)-covered glass samples and Si-probes on a glass holder were moved under the two separated targets (Zn and Sn) in a reactive argon–oxygen (Ar-O2) gas mixture. This combinatorial process ensured that all the compositions (from 0 to 100%) were achieved in the same sputtering chamber after one sputtering preparation cycle. The CE data evaluated from the electro-optical measurements plotted against the composition displayed a characteristic maximum at around 29% ZnO. The accuracy of our combinatorial approach was 5%.
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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 is presented, which includes individual Pareto layers. Pareto layers are built in the introduced preference space. This does not require algorithms for enumerating the elements of the initial set. Such algorithms are used when finding only some part of the optimal subset. This reduces the number of operations required to solve the considered combinatorial problem. The method for determining the found Pareto layers shows that the number of operations depends on the volume of the knapsack and the structure of the Pareto layers, into which the set of initial data in the entered two-criteria space is divided.
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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 operational problem with a reasonable time complexity of O(n×k), though it needs further experimental verification in the future. By adjusting the DNA-based procedures, the scheme may be used to resolve different NP-hard problems.
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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 including channel assignment, path computing, and link scheduling. This paper analyzes traffic behaviors and designs a coexisting algorithm to maximize the number of concurrent data flows. Simulations are conducted in combinatorial cases of channel and radio with various traffic requests of multiple pairs. The experimental results show the efficacy of the coexisting algorithm over a randomly generated topology. This scheme can be used to develop routing and scheduling solutions for various multi-flow network applications through prior computing.
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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 (April 1, 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 thin radially cut annular layers with specific sector angles. Changing the shape of the PMs resulted in a different flux density field and thus a different frequency spectrum of the forcing function. Attenuation of specified higher harmonics could be achieved at the expense of increasing other harmonics. For a 4-pole motor, the optimization algorithm was fairly successful at eliminating any one of the 8th, 12th or 16th harmonics. The algorithm used was developed to solve combinatorial optimization problems, and drew heavily upon principles from statistical mechanics. The final pole geometry is dependent upon the choice of the cost function used in the optimization algorithm.
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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 (July 9, 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 each WSN deployment to ensure connectivity between smart meters and hubs required for smart metering. On the other hand, these solutions must reduce costs when this type of wireless communication network is deployed. The present work proposes various optimization models that consider the physical and network layers in order to integrate different wireless communication technologies, thus reducing costs in terms of the minimum number of data aggregation points. Using a heterogeneous wireless network can reduce resource costs and energy consumption in comparison to a single cellular technology, as proposed in previous works. This work proposes a sizing model and three different models for routing wireless networks. In each case, constraints are evaluated and can be associated with different real-world scenarios. This document provides an optimization model that encompasses all of the proposed constraints; due to the combinatorial nature of the problem, this would require a heuristic technique.
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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 conjecture, can efficiently find an approximate solution for a typical instance of the SK model to within (1−ϵ) times the ground state energy. We hope to match its performance with the QAOA.Our main result is a novel technique that allows us to evaluate the typical-instance energy of the QAOA applied to the SK model. We produce a formula for the expected value of the energy, as a function of the 2p QAOA parameters, in the infinite size limit that can be evaluated on a computer with O(16p) complexity. We evaluate the formula up to p=12, and find that the QAOA at p=11 outperforms the standard semidefinite programming algorithm. Moreover, we show concentration: With probability tending to one as n→∞, measurements of the QAOA will produce strings whose energies concentrate at our calculated value. As an algorithm running on a quantum computer, there is no need to search for optimal parameters on an instance-by-instance basis since we can determine them in advance. What we have here is a new framework for analyzing the QAOA, and our techniques can be of broad interest for evaluating its performance on more general problems where classical algorithms may fail.
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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 (October 11, 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 designed to replace the update formula of particle position and velocity in the particle swarm optimization algorithm. The results of the benchmark example in case study show that the algorithm proposed in this paper has better performance.
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Levin, Mark Sh. "Four-layer framework for combinatorial optimization problems domain." Advances in Engineering Software 42, no. 12 (December 2011): 1089–98. http://dx.doi.org/10.1016/j.advengsoft.2011.07.013.
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Frohner, Nikolaus, Jan Gmys, Nouredine Melab, Günther R. Raidl, and El-ghazali Talbi. "Parallel Beam Search for Combinatorial Optimization (Extended Abstract)." Proceedings of the International Symposium on Combinatorial Search 15, no. 1 (July 17, 2022): 273–75. http://dx.doi.org/10.1609/socs.v15i1.21783.
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Inspired by the recent success of parallelized exact methods to solve difficult scheduling problems, we present preliminary results of a general parallel beam search framework for combinatorial optimization problems. Beam search is a constructive metaheuristic traversing a search tree layer by layer while keeping in each layer a bounded number of promising nodes to consider many partial solutions in parallel. We propose a variant which is suitable for intra-node parallelization by multithreading with data parallelism. For sufficiently large problem instances and beam widths our work-in-progress implementation in the JIT-compiled Julia language admits promising speed-ups over 30x on 32 cores with uniform memory access for the Permutation Flow Shop Scheduling (PFSP) problem with flowtime objective.
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Alesiani, Francesco. "Implicit Bilevel Optimization: Differentiating through Bilevel Optimization Programming." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 14683–91. http://dx.doi.org/10.1609/aaai.v37i12.26716.
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Bilevel Optimization Programming is used to model complex and conflicting interactions between agents, for example in Robust AI or Privacy preserving AI. Integrating bilevel mathematical programming within deep learning is thus an essential objective for the Machine Learning community. Previously proposed approaches only consider single-level programming. In this paper, we extend existing single-level optimization programming approaches and thus propose Differentiating through Bilevel Optimization Programming (BiGrad) for end-to-end learning of models that use Bilevel Programming as a layer. BiGrad has wide applicability and can be used in modern machine learning frameworks. BiGrad is applicable to both continuous and combinatorial Bilevel optimization problems. We describe a class of gradient estimators for the combinatorial case which reduces the requirements in terms of computation complexity; for the case of the continuous variable, the gradient computation takes advantage of the push-back approach (i.e. vector-jacobian product) for an efficient implementation. Experiments show that the BiGrad successfully extends existing single-level approaches to Bilevel Programming.
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Lábadi, Zoltán, Dániel Takács, Zsolt Zolnai, Péter Petrik, and Miklós Fried. "Compositional Optimization of Sputtered WO3/MoO3 Films for High Coloration Efficiency." Materials 17, no. 5 (February 22, 2024): 1000. http://dx.doi.org/10.3390/ma17051000.
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Thin films of mixed MoO3 and WO3 were obtained using reactive magnetron sputtering onto ITO-covered glass, and the optimal composition was determined for the best electrochromic (EC) properties. A combinatorial material synthesis approach was applied throughout the deposition experiments, and the samples represented the full composition range of the binary MoO3/WO3 system. The electrochromic characteristics of the mixed oxide films were determined with simultaneous measurement of layer transmittance and applied electric current through the using organic propylene carbonate electrolyte cells in a conventional three-electrode configuration. Coloration efficiency data evaluated from the primary data plotted against the composition displayed a characteristic maximum at around 60% MoO3. Our combinatorial approach allows the localization of the maximum at 5% accuracy.
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Liu, Xiaoyuan, Anthony Angone, Ruslan Shaydulin, Ilya Safro, Yuri Alexeev, and Lukasz Cincio. "Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers." IEEE Transactions on Quantum Engineering 3 (2022): 1–20. http://dx.doi.org/10.1109/tqe.2021.3140190.
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Park, Sojung, and Seon K. Park. "A micro-genetic algorithm (GA v1.7.1a) for combinatorial optimization of physics parameterizations in the Weather Research and Forecasting model (v4.0.3) for quantitative precipitation forecast in Korea." Geoscientific Model Development 14, no. 10 (October 18, 2021): 6241–55. http://dx.doi.org/10.5194/gmd-14-6241-2021.
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Abstract. One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating the subgrid-scale physical processes. For more accurate regional weather and climate prediction by improving physics parameterizations, it is important to optimize a combination of physics schemes and unknown parameters in NWP models. We have developed an interface system between a micro-genetic algorithm (µ-GA) and the WRF model for the combinatorial optimization of cumulus (CU), microphysics (MP), and planetary boundary layer (PBL) schemes in terms of quantitative precipitation forecast for heavy rainfall events in Korea. The µ-GA successfully improved simulated precipitation despite the nonlinear relationship among the physics schemes. During the evolution process, MP schemes control grid-resolving-scale precipitation, while CU and PBL schemes determine subgrid-scale precipitation. This study demonstrates that the combinatorial optimization of physics schemes in the WRF model is one possible solution to enhance the forecast skill of precipitation.
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Mei, H., and Yong Wang. "Ant Colony Optimization for Neural Network." Key Engineering Materials 392-394 (October 2008): 677–81. http://dx.doi.org/10.4028/www.scientific.net/kem.392-394.677.
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A new learning way for neural network (NN) in which its weights can be optimized by using the ant colony algorithm is presented in this paper. The learning of neural network belongs to continuous optimization. The ant colony algorithm is initially developed for hard combinatorial optimization. A kind of ant colony optimization (ACO) for continuous optimization, which includes global searching, local searching and definite searching, is developed based on the basic ant colony algorithm. A three-layer neural network, as an example, is trained to express nonlinear function. The efficiency of the new algorithm is examinated. It is found that the new developed method has the merits of both ant colony algorithm and neural network.
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Kadhim, Ali, Paul Harrison, Jake Meeth, Alaa Al-Mebir, Guanggen Zeng, and Judy Wu. "Development of Combinatorial Pulsed Laser Deposition for Expedited Device Optimization in CdTe/CdS Thin-Film Solar Cells." International Journal of Optics 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/1696848.
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A combinatorial pulsed laser deposition system was developed by integrating a computer controlled scanning sample stage in order to rapidly screen processing conditions relevant to CdTe/CdS thin-film solar cells. Using this system, the thickness of the CdTe absorber layer is varied across a single sample from 1.5 μm to 0.75 μm. The effects of thickness on CdTe grain morphology, crystal orientation, and cell efficiency were investigated with respect to different postprocessing conditions. It is shown that the thinner CdTe layer of 0.75 μm obtained the best power conversion efficiency up to 5.3%. The results of this work shows the importance that CdTe grain size/morphology relative to CdTe thickness has on device performance and quantitatively exhibits what those values should be to obtain efficient thin-film CdTe/CdS solar cells fabricated with pulsed laser deposition. Further development of this combinatorial approach could enable high-throughput exploration and optimization of CdTe/CdS solar cells.
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Jia, Dongbao, Ming Cao, Wenbin Hu, Jing Sun, Hui Li, Yichen Wang, Weijie Zhou, Tiancheng Yin, and Ran Qian. "Multi-Objective Combinatorial Optimization Algorithm Based on Asynchronous Advantage Actor–Critic and Graph Transformer Networks." Electronics 13, no. 19 (September 28, 2024): 3842. http://dx.doi.org/10.3390/electronics13193842.
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Multi-objective combinatorial optimization problems (MOCOPs) are designed to identify solution sets that optimally balance multiple competing objectives. Addressing the challenges inherent in applying deep reinforcement learning (DRL) to solve MOCOPs, such as model non-convergence, lengthy training periods, and insufficient diversity of solutions, this study introduces a novel multi-objective combinatorial optimization algorithm based on DRL. The proposed algorithm employs a uniform weight decomposition method to simplify complex multi-objective scenarios into single-objective problems and uses asynchronous advantage actor–critic (A3C) instead of conventional REINFORCE methods for model training. This approach effectively reduces variance and prevents the entrapment in local optima. Furthermore, the algorithm incorporates an architecture based on graph transformer networks (GTNs), which extends to edge feature representations, thus accurately capturing the topological features of graph structures and the latent inter-node relationships. By integrating a weight vector layer at the encoding stage, the algorithm can flexibly manage issues involving arbitrary weights. Experimental evaluations on the bi-objective traveling salesman problem demonstrate that this algorithm significantly outperforms recent similar efforts in terms of training efficiency and solution diversity.
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Borissova, Daniela, Ivan Mustakerov, and Dilian Korsemov. "Business Intelligence System via Group Decision Making." Cybernetics and Information Technologies 16, no. 3 (September 1, 2016): 219–29. http://dx.doi.org/10.1515/cait-2016-0045.
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Abstract In the paper a business intelligence tool based on group decision making is proposed. The group decision making uses a combinatorial optimization modeling technique. It takes into account weighted coefficients for evaluation criteria assigned by decision makers together with their scores for the alternatives in respect of these criteria. The proposed optimization model for group decision making considers also the knowledge level of the group members involved as decision makers. This optimization model is implemented in three-layer architecture of Web application for business intelligence by group decision making. Developed Web application is numerically tested for a representative problem for software choice considering six decision makers, three alternatives and 19 evaluation criteria. The obtained results show the practical applicability and effectiveness of the proposed approach.
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Ferber, Aaron, Bryan Wilder, Bistra Dilkina, and Milind Tambe. "MIPaaL: Mixed Integer Program as a Layer." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1504–11. http://dx.doi.org/10.1609/aaai.v34i02.5509.
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Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures average accuracy between predicted values and ground truth values. Decision-focused learning explicitly integrates the downstream decision problem when training the predictive model, in order to optimize the quality of decisions induced by the predictions. It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization. However, these previous applications have uniformly focused on problems with simple constraints. Here, we enable decision-focused learning for the broad class of problems that can be encoded as a mixed integer linear program (MIP), hence supporting arbitrary linear constraints over discrete and continuous variables. We show how to differentiate through a MIP by employing a cutting planes solution approach, an algorithm that iteratively tightens the continuous relaxation by adding constraints removing fractional solutions. We evaluate our new end-to-end approach on several real world domains and show that it outperforms the standard two phase approaches that treat prediction and optimization separately, as well as a baseline approach of simply applying decision-focused learning to the LP relaxation of the MIP. Lastly, we demonstrate generalization performance in several transfer learning tasks.
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Zhao, Kaixuan, Shikui Tu, and Lei Xu. "IA-GM: A Deep Bidirectional Learning Method for Graph Matching." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (May 18, 2021): 3474–82. http://dx.doi.org/10.1609/aaai.v35i4.16461.
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Existing deep learning methods for graph matching(GM) problems usually considered affinity learningto assist combinatorial optimization in a feedforward pipeline, and parameter learning is executed by back-propagating the gradients of the matching loss. Such a pipeline pays little attention to the possible complementary benefit from the optimization layer to the learning component. In this paper, we overcome the above limitation under a deep bidirectional learning framework.Our method circulates the output of the GM optimization layer to fuse with the input for affinity learning. Such direct feedback enhances the input by a feature enrichment and fusion technique, which exploits andintegrates the global matching patterns from the deviation of the similarity permuted by the current matching estimate. As a result, the circulation enables the learning component to benefit from the optimization process, taking advantage of both global feature and the embedding result which is calculated by local propagationthrough node-neighbors. Moreover, circulation consistency induces an unsupervised loss that can be implemented individually or jointly to regularize the supervised loss. Experiments on challenging datasets demonstrate the effectiveness of our methods for both supervised learning and unsupervised learning.
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ANZELLOTTI, G., R. BATTITI, I. LAZZIZZERA, G. SONCINI, A. ZORAT, A. SARTORI, G. TECCHIOLLI, and P. LEE. "TOTEM: A HIGHLY PARALLEL CHIP FOR TRIGGERING APPLICATIONS WITH INDUCTIVE LEARNING BASED ON THE REACTIVE TABU SEARCH." International Journal of Modern Physics C 06, no. 04 (August 1995): 555–60. http://dx.doi.org/10.1142/s0129183195000423.
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The training of a Multi-Layer Perceptron (MLP) classifier is considered as a Combinatorial Optimization task and solved using the Reactive Tabu Search (RTS) method. RTS needs only forward passes (no derivatives) and does not require high precision network parameters. TOTEM, a special-purpose VLSI chip, was developed to take advantage of the limited memory and processing requirements of RTS: the final system effects a very close match between hardware and training algorithm. The RTS algorithm and the design of TOTEM are discussed, together with the operational characteristics of the VLSI chip and some preliminary training and generalization tests on triggering tasks.
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Sun, Yang, Yuwei Bian, Huixin Li, Fangqing Tan, and Lihan Liu. "Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing Environments." Symmetry 15, no. 12 (December 14, 2023): 2196. http://dx.doi.org/10.3390/sym15122196.
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Nowadays, multi-access edge computing (MEC) has been widely recognized as a promising technology that can support a wide range of new applications for the Internet of Things (IoT). In dynamic MEC networks, the heterogeneous computation capacities of the edge servers and the diversified requirements of the IoT applications are both asymmetric, where and when to offload and schedule the time-dependent tasks of IoT applications remains a challenge. In this paper, we propose a flexible offloading and task scheduling scheme (FLOATS) to adaptively optimize the computation of offloading decisions and scheduling priority sequences for time-dependent tasks in dynamic networks. We model the dynamic optimization problem as a multi-objective combinatorial optimization problem in an infinite time horizon, which is intractable to solve. To address this, a rolling-horizon-based optimization mechanism is designed to decompose the dynamic optimization problem into a series of static sub-problems. A genetic algorithm (GA)-based computation offloading and task scheduling algorithm is proposed for each static sub-problem. This algorithm encodes feasible solutions into two-layer chromosomes, and the optimal solution can be obtained through chromosome selection, crossover and mutation operations. The simulation results demonstrate that the proposed scheme can effectively reduce network costs in comparison to other reference schemes.
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Liu, Dian Ting, and Fei Fei Jia. "A Method of the Green Product Configuration Design Based on Multi-Layer Generalized Operator and Genetic Algorithm." Applied Mechanics and Materials 483 (December 2013): 542–49. http://dx.doi.org/10.4028/www.scientific.net/amm.483.542.
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Taking the configuration design of the lifting device in a bridge crane as an example, this paper discusses the green optimal design method of enterprise's modular products with uncertain factors. The multi-layer generalized operators and comprehensive mathematical model under uncertainty are established based on the mapping relationship between product function and its structure. The model is transformed to an ascertaining combinatorial optimization model by de-fuzzy operator, and then it is solved by GA (Genetic Algorithm, GA). The method of chromosome encoding in binary that the chromosome is segmented by components and the genes in each segment are ranged as corresponding structures of each component, and the methods of selection and crossover and mutation operator are presented in this paper. The result of the green configuration design on the lifting device verifies the effectiveness and practical value of the method proposed in this paper.
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Di, Zhenhua, Wei Gong, Yanjun Gan, Chenwei Shen, and Qingyun Duan. "Combinatorial Optimization for WRF Physical Parameterization Schemes: A Case Study of Three-Day Typhoon Simulations over the Northwest Pacific Ocean." Atmosphere 10, no. 5 (May 1, 2019): 233. http://dx.doi.org/10.3390/atmos10050233.
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Quantifying a set of suitable physics parameterization schemes for the Weather Research and Forecasting (WRF) model is essential for obtaining highly accurate typhoon forecasts. In this study, a systematic Tukey-based combinatorial optimization method was proposed to determine the optimal physics schemes of the WRF model for 15 typhoon simulations over the Northwest Pacific Ocean, covering all available schemes of microphysics (MP), cumulus (CU), and planetary boundary layer (PBL) physical processes. Results showed that 284 scheme combination searches were sufficient to find the optimal scheme combinations for simulations of track (km), central sea level pressure (CSLP, hPa), and 10 m maximum surface wind (10-m wind, m s−1), compared with the 700 sets of full combinations (i.e., 10 MP × 7 CU × 10 PBL). The decrease in the typhoon simulation error (i.e., root mean square error between simulation and observations) with this optimal scheme combination was 34%, 33.92%, and 25.67% for the track, CSLP, and 10-m wind, respectively. Overall, the results demonstrated that the optimal scheme combination yields reasonable results, and the Tukey-based optimization method is very effective and efficient in terms of computational resources.
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WANG Yunjiang, XI Huiming, XIAO Zhuoyan, WANG Zengbin, and and SHI Sha. "Designing Quantum Approximate Optimization Algorithm for the Maximum Cut Problem." Acta Physica Sinica 74, no. 8 (2025): 0. https://doi.org/10.7498/aps.74.20241223.
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The Max-Cut Problem (MCP) is a classic problem in the field of combinatorial optimization and has important applications in various domains, including statistical physics and image processing. However, except for some special cases, the Max-Cut problem remains an NP-complete problem, and there is currently no known efficient classical algorithm that can solve it in polynomial time. The Quantum Approximate Optimization Algorithm (QAOA), as a pivotal algorithm in the Noisy Intermediate-Scale Quantum (NISQ) computing era, has shown significant potential for solving the Max-Cut problem. However, due to the lack of quantum error correction, the reliability of computations in NISQ systems sharply declines as the circuit depth of the algorithm increases. Therefore, designing an efficient, shallow-depth, and low-complexity QAOA for the Max-Cut problem is a critical challenge in demonstrating the advantages of quantum computing in the NISQ era.<br>In this paper, based on the standard QAOA algorithm, we introduce Pauli Y rotation gates into the target Hamiltonian circuit for the Max-Cut problem. By enhancing the flexibility of quantum trial functions and improving the efficiency of Hilbert space exploration within a single iteration, we significantly improve the performance of QAOA on the Max-Cut problem.<br>We conduct extensive numerical simulations using the MindSpore Quantum platform, comparing the proposed RY-layer-assisted QAOA with standard QAOA and its existing variants, including MA-QAOA and QAOA+. The experiments are performed on various graph types, including complete graphs, 3-regular graphs, 4-regular graphs, and random graphs with edge probabilities between 0.3 and 0.5. Our results demonstrate that the RY-layer-assisted QAOA achieves a higher approximation ratio across all graph types, particularly in regular and random graphs, where traditional QAOA variants struggle. Moreover, the proposed method exhibits strong robustness as the graph size increases, maintaining high performance even for larger graphs. Importantly, the RY-layer-assisted QAOA requires fewer CNOT gates and has a lower circuit depth compared to standard QAOA and its variants, making it more suitable for NISQ devices with limited coherence times and high error rates.<br>In conclusion, the RY-layer-assisted QAOA offers a promising approach to solving Max-Cut problems in the NISQ era. By improving the approximation ratio while reducing circuit complexity, this method demonstrates significant potential for practical quantum computing applications, paving the way for more efficient and reliable quantum optimization algorithms.
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Xu, Huihui, and Xiaobing Yuan. "Cross-Layer Design for Energy-Efficient Reliable Multi-Path Transmission in Event-Driven Wireless Sensor Networks." Sensors 23, no. 14 (July 19, 2023): 6520. http://dx.doi.org/10.3390/s23146520.
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In event-driven wireless sensor networks (WSNs), a reliable, efficient, and scalable routing solution is required for the reliable delivery of sensory data to the base station (BS). However, existing routing algorithms rarely address the issue of energy efficiency under multi-path conflicts for multi-event-driven scenarios. In order to maximize energy efficiency while maintaining a manageable conflict probability, this paper investigates a cross-layer design of routing and power control for multi-event-driven WSNs. We first develop a mathematical characterization of the conflict probability in multi-path routing, and we then formulate the energy efficiency maximization problem as a non-convex combinatorial fractional optimization problem subject to a maximum conflict probability constraint. By utilizing non-linear fractional programming and dual decomposition, an iterative search algorithm was used to obtain near-optimal power allocation and routing solutions. Extensive results demonstrate that our proposed algorithm achieved a gain of 9.09% to 35.05% in energy efficiency compared to other routing algorithms, thus indicating that our proposed algorithm can avoid unnecessary control overhead from multi-path conflicts with a lower conflict probability and can ensure maximum energy efficiency through routing and power control design.
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Xu, Haiyang, and Hengyou Lan. "An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems." Electronics 12, no. 7 (April 2, 2023): 1681. http://dx.doi.org/10.3390/electronics12071681.
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Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when their scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC_IGA). The primary idea behind ALC_IGA is to break down a large-scale problem into a series of small-scale problems. First, the k-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified 2-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC_IGA is between O(nlogn) and O(n2). The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC_IGA surpasses the compared two-layered and three-layered algorithms in convergence speed, stability, and solution quality. Specifically, with parallelization, the ALC_IGA can solve instances with 2×105 nodes within 0.15 h, 1.4×106 nodes within 1 h, and 2×106 nodes in three dimensions within 1.5 h.
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Zhou, Mingjun, Jiahao Ge, Hao Xu, and Chi-Wing Fu. "Computational Design of LEGO ® Sketch Art." ACM Transactions on Graphics 42, no. 6 (December 5, 2023): 1–15. http://dx.doi.org/10.1145/3618306.
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This paper presents computational methods to aid the creation of LEGO ®1 sketch models from simple input images. Beyond conventional LEGO ® mosaics, we aim to improve the expressiveness of LEGO ® models by utilizing LEGO ® tiles with sloping and rounding edges, together with rectangular bricks, to reproduce smooth curves and sharp features in the input. This is a challenging task, as we have limited brick shapes to use and limited space to place bricks. Also, the search space is immense and combinatorial in nature. We approach the task by decoupling the LEGO ® construction into two steps: first approximate the shape with a LEGO ® -buildable contour then filling the contour polygon with LEGO ® bricks. Further, we formulate this contour approximation into a graph optimization with our objective and constraints and effectively solve for the contour polygon that best approximates the input shape. Further, we extend our optimization model to handle multi-color and multi-layer regions, and formulate a grid alignment process and various perceptual constraints to refine the results. We employ our method to create a large variety of LEGO ® models and compare it with humans and baseline methods to manifest its compelling quality and speed.
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Hou, Yue, Da Li, Di Zhang, and Zhiyuan Deng. "An Improved Phase Space Reconstruction Method-Based Hybrid Model for Chaotic Traffic Flow Prediction." Discrete Dynamics in Nature and Society 2022 (September 23, 2022): 1–22. http://dx.doi.org/10.1155/2022/5604674.
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Traffic flow is chaotic due to nonstationary realistic factors, and revealing the internal nonlinear dynamics of chaotic data and making high-accuracy predictions is the key to traffic control and inducement. Given that high-quality phase space reconstruction is the foundation of predictive modeling. Firstly, an improved C-C method based on the fused norm search domain is proposed to address the issue that the C-C method in the phase space reconstruction algorithm does not meet the Euclidean metric accuracy and reduces the reconstruction quality when the infinite norm metric is used. Secondly, to address the problem of insufficient learning ability of traditional convolutional combinatorial modeling for complex phase space laws of chaotic traffic flow, the high-dimensional phase space features are extracted using the layer-by-layer pretraining mechanism of convolutional deep belief networks (CDBNs), and the temporal features are extracted by combining with long short-term memory (LSTM). Finally, an improved probabilistic dynamic reproduction-based genetic algorithm (PDRGA) is proposed to address the problem of the hybrid model falling into a local optimum when learning the phase space law. Experiments are conducted in three aspects: phase space reconstruction quality analysis, comparison of optimization algorithm convergence, and prediction model performance comparison. The experimentation with two data sets demonstrates that the improved C-C method combines the advantages of the high accuracy metric of the L2 norm with the low operational complexity of the infinite norm, achieving a balance between reconstruction quality and algorithm efficiency. The proposed PDRGA optimization algorithm is a lightweight improvement of the traditional genetic algorithm (GA) and solves the problem that the model tends to fall into a local optimum by optimizing the initial weights of CDBN. Meanwhile, the five error evaluation indexes of the proposed PDRGA-CDBN-LSTM hybrid model are lower than those of the baseline model, providing a new modeling idea for chaotic traffic flow prediction.
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Haddar, Houssem, and Alexander Konschin. "Factorization method for imaging a local perturbation in inhomogeneous periodic layers from far field measurements." Inverse Problems & Imaging 14, no. 1 (2020): 133–52. http://dx.doi.org/10.3934/ipi.2019067.
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Banane, Mouad, and Abdessamad Belangour. "Towards a New Scalable Big Data System Semantic Web Applied on Mobile Learning." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 01 (January 20, 2020): 126. http://dx.doi.org/10.3991/ijim.v14i01.10922.
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In Web 3.0, semantic data gives machines the ability to understand and process data. Resource Description Framework (RDF) is the liagna franca of Semantic Web. While Big Data handles the problematic of storing and processing massive data, it still does not provide a support for RDF data. In this paper, we present a new Big Data semantic web comprised of a classical Big Data system with a semantic layer. As a proof of concept of our approach, we use Mobile-learning as a case study. The architecture we propose is composed of two main parts: a knowledge server and an adaptation model. The knowledge server allows trainers and business experts to represent their expertise using business rules and ontology to ensure heterogeneous knowledge. Then, in a mobility environment, the knowledge server makes it possible to take into account the constraints of the environment and the user constraints thanks to the RDF exchange format. The adaptation model based on RDF graphs corresponds to combinatorial optimization algorithms, whose objective is to propose to the learner a relevant combination of Learning Object based on its contextual constraints. Our solution guarantees scalability, and high data availability through the use of the principle of replication. The results obtained in the system evaluation experiments, on a large number of servers show the efficiency, scalability, and robustness of our system if the amount of data processed is very large.
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Jain, Adarsh, Christian Marcks, Mohit Chatwani, Anna K. Mechler, Vineetha Vinayakumar, and Mohit Chatwani. "Understanding the Mechanism of Evaporation-Induced Islands during the Formation of Catalyst Layers and Their Influence on the Alkaline Oxidation Evolution Reaction." ECS Meeting Abstracts MA2024-02, no. 28 (November 22, 2024): 2134. https://doi.org/10.1149/ma2024-02282134mtgabs.
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Advancements in anode development substantially reduce the cost and enhance the performance of water electrolysis. The strategic configuration of nano-sized catalyst materials on anode supports is a pivotal factor in the expansion of the production of catalyst layers and has a significant influence on their electrochemical performance [1–3]. Therefore, it is essential to understand the structure formation mechanisms during electrode fabrication. This contribution aims to explore the inherent drying mechanisms in the coating process and their subsequent influence on the morphological evolution of anodes, ultimately establishing a linkage between these morphological changes and electrochemical performance and stability metrics. The initial phase of the study involved a detailed characterization of commercial nickel cobalt oxide nanoparticles via advanced analytical techniques, including transmission electron microscopy and energy-dispersive X-ray spectroscopy [4]. Subsequently, a stable ink was formulated by dispersing the catalyst alongside a binder in a solvent, which was then uniformly applied to a conductive nickel plate utilizing an ultrasonic spray coater. Prior to application, the catalyst inks underwent optimization based on the Hansen solubility parameters of catalyst materials [5, 6] and subsequent visualization of their stability and dispersibility characteristics through transmittogram analysis [7]. The drying process of the coating was conducted at two distinct temperatures, 50 °C and 150 °C, to investigate the effect of temperature on solvent evaporation. Morphological and structural analyses of the anodes were subsequently performed using scanning electron microscopy (SEM), laser scanning microscopy (LSM), and atomic force microscopy (AFM), providing insight into the alterations induced by the drying process. The analyses conducted in this study elucidate significant modifications in the morphology of the layer structures attributable to variations in drying temperatures as illustrated in Figure 1. Specifically, lower temperature resulted in reduced evaporation rates that facilitated the formation of larger, isolated regions/islands on the anode support. These are indicative of a non-uniform coating distribution. In contrast, a higher temperature promoted rapid evaporation, resulting in a quite homogeneous distribution of smaller islands accompanied by the emergence of voids within the anode layer. Electrochemical analysis revealed that these morphological changes are of vital importance: they govern the wetting behaviour and the dynamics of bubble formation which are important for the electrochemical performance and durability of the catalyst layers. Moreover, the insights gleaned from this study are instrumental in identifying optimal configurations of catalyst layer structures, i.e., features that should be maintained during scale-up. In conclusion, this study significantly advances the field by revealing novel principles governing the design and optimization of electrode structures for the oxygen evolution reaction. In particular, the complicated relationship between drying temperatures and morphological evolution, which significantly influences electrochemical performance is explained. References [1] V. Vinayakumar, A. Jain, and D. Segets, "Enhancing Material Analysis with Statistical and Computer Vision Techniques: A Comprehensive Approach for Evaluating Electrode Quality," Meet. Abstr., MA2023-02, no. 54, p. 2584, 2023, doi: 10.1149/MA2023-02542584mtgabs. [2] A. Jain et al., "From Small‐Area Observations to Insight: Surface‐Feature‐Extrapolation of Anodes for Alkaline Oxygen Evolution Reaction," ChemCatChem, 2024, doi: 10.1002/cctc.202301461. [3] D. Siegmund et al., "Crossing the Valley of Death: From Fundamental to Applied Research in Electrolysis," JACS Au, vol. 1, no. 5, pp. 527–535, 2021, doi: 10.1021/jacsau.1c00092. [4] V. Vinayakumar et al., "Scalable Production of Nickel-Cobalt Based Anode Materials for Alkaline Electrolysis: A Multi-Technique Approach from Micro Powder Analysis to Electrochemical Performance," Meet. Abstr., MA2023-02, no. 58, p. 2846, 2023, doi: 10.1149/MA2023-02582846mtgabs. [5] A. S. Amin et al., "A Procedure for Rational Probe Liquids Selection to Determine Hansen Solubility Parameters," ChemCatChem, 2024, doi: 10.1002/cctc.202301393. [6] O. Anwar, S. Bapat, J. Ahmed, X. Xie, J. Sun, and D. Segets, "Hansen parameter evaluation for the characterization of titania photocatalysts using particle size distributions and combinatorics," Nanoscale, vol. 14, no. 37, pp. 13593–13607, 2022, doi: 10.1039/D2NR02711A. [7] S. Bapat and D. Segets, "Sedimentation Dynamics of Colloidal Formulations through Direct Visualization: Implications for Fuel Cell Catalyst Inks," ACS Appl. Nano Mater., vol. 3, no. 8, pp. 7384–7391, 2020, doi: 10.1021/acsanm.0c01467. Figure 1
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Kandaswamy, Djano, Thierry Blu, and Dimitri Van De Ville. "Analytic sensing for multi-layer spherical models with application to EEG source imaging." Inverse Problems & Imaging 7, no. 4 (2013): 1251–70. http://dx.doi.org/10.3934/ipi.2013.7.1251.
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Chen, Zhiming, Chao Liang, and Xueshuang Xiang. "An anisotropic perfectly matched layer method for Helmholtz scattering problems with discontinuous wave number." Inverse Problems & Imaging 7, no. 3 (2013): 663–78. http://dx.doi.org/10.3934/ipi.2013.7.663.
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Kontogiannis, Dimitrios, Dimitrios Bargiotas, Aspassia Daskalopulu, and Lefteri H. Tsoukalas. "A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality." Energies 14, no. 19 (September 24, 2021): 6088. http://dx.doi.org/10.3390/en14196088.
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Power forecasting models offer valuable insights on the electricity consumption patterns of clients, enabling the development of advanced strategies and applications aimed at energy saving, increased energy efficiency, and smart energy pricing. The data collection process for client consumption models is not always ideal and the resulting datasets often lead to compromises in the implementation of forecasting models, as well as suboptimal performance, due to several challenges. Therefore, combinations of elements that highlight relationships between clients need to be investigated in order to achieve more accurate consumption predictions. In this study, we exploited the combined effects of client similarity and causality, and developed a power consumption forecasting model that utilizes ensembles of long short-term memory (LSTM) networks. Our novel approach enables the derivation of different representations of the predicted consumption based on feature sets influenced by similarity and causality metrics. The resulting representations were used to train a meta-model, based on a multi-layer perceptron (MLP), in order to combine the results of the LSTM ensembles optimally. This combinatorial approach achieved better overall performance and yielded lower mean absolute percentage error when compared to the standalone LSTM ensembles that do not include similarity and causality. Additional experiments indicated that the combination of similarity and causality resulted in more performant models when compared to implementations utilizing only one element on the same model structure.
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Saini, Meenaxi, Anu, Alexander Rapoport, Santosh Kumar Tiwari, Davender Singh, Vinay Malik, Sandeep Kumar, and Bijender Singh. "Bioacetoin Production by Bacillus subtilis subsp. subtilis Using Enzymatic Hydrolysate of Lignocellulosic Biomass." Fermentation 9, no. 8 (July 25, 2023): 698. http://dx.doi.org/10.3390/fermentation9080698.
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Acetoin is an important bio-product useful in the chemical, food and pharmaceutical industries. Microbial fermentation is the major process for the production of bioacetoin, as the petroleum resources used in chemical methods are depleting day by day. Bioacetoin production using wild microorganisms is an easy, eco-friendly and economical method for the production of bioacetoin. In the present study, culture conditions and nutritional requirements were optimized for bioacetoin production by a wild and non-pathogenic strain of B. subtilis subsp. subtilis JJBS250. The bacterial culture produced maximum bioacetoin (259 mg L−1) using peptone (3%) and sucrose (2%) at 30 °C, 150 rpm and pH 7.0 after 24 h. Further supplementation of combinatorial nitrogen sources, i.e., peptone (1%) and urea (0.5%), resulted in enhanced titre of bioacetoin (1017 mg L−1) by the bacterial culture. An approximately 46.22–fold improvement in bioacetoin production was achieved after the optimization process. The analysis of samples using thin layer chromatography confirmed the presence of bioacetoin in the culture filtrate. The enzymatic hydrolysate was obtained by saccharification of pretreated rice straw and sugarcane bagasse using cellulase from Myceliophthora thermophila. Fermentation of the enzymatic hydrolysate (3%) of pretreated rice straw and sugarcane bagasse by the bacterial culture resulted in 210 and 473.17 mgL−1 bioacetoin, respectively. Enzymatic hydrolysates supplemented with peptone as a nitrogen source showed a two to four-fold improvement in the production of bioacetoin. Results have demonstrated the utility of wild type B. subtilis subsp. subtilis JJBS250 as a potential source for economical bioacetoin production by making use of renewable and cost-effective lignocellulosic substrate. Therefore, this study will help in the sustainable management of agricultural waste for the industrial production of bioacetoin, and in combating environmental pollution.
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Zhang, Zhenwei, Xue Li, Yuping Duan, Ke Yin, and Xue-Cheng Tai. "An efficient multi-grid method for TV minimization problems." Inverse Problems & Imaging 15, no. 5 (2021): 1199. http://dx.doi.org/10.3934/ipi.2021034.
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<p style='text-indent:20px;'>We propose an efficient multi-grid domain decomposition method for solving the total variation (TV) minimization problems. Our multi-grid scheme is developed based on the piecewise constant function spanned subspace correction rather than the piecewise linear one in [<xref ref-type="bibr" rid="b17">17</xref>], which ensures the calculation of the TV term only occurs on the boundaries of the support sets. Besides, the domain decomposition method is implemented on each layer to enable parallel computation. Comprehensive comparison results are presented to demonstrate the improvement in CPU time and image quality of the proposed method on medium and large-scale image denoising and reconstruction problems.</p>
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Boudrahem, Nassim, and Ahmed Berboucha. "Theoretical justification of Ventcel's boundary conditions for a thin layer three-dimensional thermoelasticity problem." Miskolc Mathematical Notes 22, no. 2 (2021): 581. http://dx.doi.org/10.18514/mmn.2021.3679.
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Amiraliyev, Gabil M., and Omer Yapman. "On the Volterra Delay-Integro-Differential equation with layer behavior and its numerical solution." Miskolc Mathematical Notes 20, no. 1 (2019): 75. http://dx.doi.org/10.18514/mmn.2019.2424.
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Wang, Qi, Leandro R. Tessler, Helio Moutinho, Bobby To, John Perkins, Daxing Han, Dave Ginley, and Howard M. Branz. "Combinatorial Approach to Thin-Film Silicon Materials and Devices." MRS Proceedings 762 (2003). http://dx.doi.org/10.1557/proc-762-a9.1.
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AbstractWe apply combinatorial approaches to thin-film Si materials and device research. Our hot-wire chemical vapor deposition chamber is fitted with substrate xyz translation, a motorized shutter, and interchangable shadow masks to implement various combinatorial methods. For example, we have explored, in detail, the transition region through which thin Si changes from amorphous to microcrystalline silicon. This transition is very sensitive to deposition parameters such as hydrogen-to-silane dilution of the source gas, chamber pressure, and substrate temperature. A material library, on just a few substrates, led to a three-dimensional map of the transition as it occurs in our deposition system. This map guides our scientific studies and enables us to use several distinct transition materials in our solar-cell optimization research. We also grew thickness-graded wedge samples spanning the amorphous-to-microcrystalline Si transition. These single stripes map the temporal change of the thin silicon phase onto a single spatial dimension. Therefore, the structural, optical, and electrical properties can easily be studied through the phase transition. We have examined the nature of the phase change on the wedges with Raman spectroscopy, atomic force microscopy, extended x-ray absorption fine structure (EXAFS), x-ray absorption near-edge spectroscopy (XANES), ultraviolet reflectivity, and other techniques. Combinatorial techniques also accelerate our device research. In solar cells, for example, the combinatorial approach has significantly accelerated the optimization process of p-, i-, n-, and buffer layers through wide exploration of the complex space of growth parameters and layer thicknesses. Again, only a few deposition runs are needed. It has also been useful to correlate the materials properties of single layers in a device to their performance in the device. We achieve this by depositing layers that extend beyond the device dimensions to permit independent characterization of the layers. Not only has the combinatorial approach greatly increased the rate of materials and device experimentation in our laboratory, it has also been a powerful tool leading to a better understanding of structure-property relationships in thin-film Si.
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Lee, Soo Hyung, Sunwoo Cheong, Jea Min Cho, Néstor Ghenzi, Dong Hoon Shin, Yoon Ho Jang, Janguk Han, et al. "In‐Materia Annealing and Combinatorial Optimization Based on Vertical Memristive Array." Advanced Materials, August 28, 2024. http://dx.doi.org/10.1002/adma.202410191.
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AbstractDue to its area and energy efficiency, a memristive crossbar array (CBA) has been extensively studied for various combinatorial optimization applications, from network problems to circuit design. However, conventional approaches include heavily burdening software fine‐tuning for the annealing process. Instead, this study introduces the “in‐materia annealing” method, where the inter‐layer interference of vertically stacked memristive CBA is utilized as an annealing method. When mapping combinatorial optimization problems into the configuration layer of the CBA, exponentially decaying annealing profiles are generated in nearby noise layers. Moreover, in‐materia annealing profiles can be controlled by changing compliance current, read voltage, and read pulse width. Therefore, the annealing profiles can be arbitrarily controlled and generated individually for each cell, providing rich noise sources to solve the problem efficiently. Consequently, the experimental and simulation of Max‐Cut and weighted Max‐Cut problems achieve notable results with the minimum software burden.
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Stradins, Paul, Howard M. Branz, Jian Hu, Scott Ward, and Qi Wang. "Combinatorial Studies of Switching and Solid-Phase Crystallization in Amorphous Silicon." MRS Proceedings 894 (2005). http://dx.doi.org/10.1557/proc-0894-ll02-01.
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AbstractCombinatorial approaches are successfully applied for the optimization of electric write-once, thin-film Si antifuse memory devices, as well as for studying the solid-phase epitaxy kinetics of amorphous silicon on c-Si. High forward, low reverse current thin film Si diode deposition recipes are selected using cross-strips of different combinations of amorphous and microcrystalline doped layers, as well as a thickness-wedged intrinsic a-Si:H buffer layer. By studying switching in thickness-wedged a-Si:H layers, it is found that switching requires both a critical field and a critical bias voltage across the metallic contacts. Solid-phase epitaxy speed has a non-linear dependence on the film thickness, which is easily observed by optical image monitoring and analysis in wedged a-Si:H films on c-Si wafers.
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Renata de Araujo Braga and Elias Saraiva Barroso. "A permutation algorithm for stacking sequence optimization of composite laminates." Ibero-Latin American Congress on Computational Methods in Engineering (CILAMCE), December 2, 2024. https://doi.org/10.55592/cilamce.v6i06.10326.
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Structures made of fiber-reinforced composite material are used in many industries, such as automotive, naval, aeronautical, and construction civil. Laminated structures are produced by thin layers of composite material, such as carbon fiber-reinforced laminates, which have high strength and stiffness. These composites are stacked in different orientations in order to provide superior mechanical properties compared to other conventional structures. Due to the number of variables, the conventional design methodology based on trial and error is not attractive, and it is advantageous to use optimization techniques. When designing laminated structures, strength, stiffness, and performance constraints must be satisfied. Using optimization techniques, it is possible to find an optimal lamination scheme that meets the established prerequisites. In this type of structure, the arrangement of the layers and the orientation of the fibers can have a significant impact on the final performance of the structure. The permutation problem in optimization refers to determining the most effective sequence of layers and fiber orientations that meets the design requirements. This work proposes the implementation of a heuristic optimization technique based on design variable permutation. In this specific combinatorial optimization problem, the quantity of layers of each type (i.e. orientation) is known a prior, and the optimal arrangement of these layers is to be evaluated. The algorithm is implemented in Octave language and is demonstrated in optimization of laminated plates.
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Herrman, Rebekah, Phillip C. Lotshaw, James Ostrowski, Travis S. Humble, and George Siopsis. "Multi-angle quantum approximate optimization algorithm." Scientific Reports 12, no. 1 (April 26, 2022). http://dx.doi.org/10.1038/s41598-022-10555-8.
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AbstractThe quantum approximate optimization algorithm (QAOA) generates an approximate solution to combinatorial optimization problems using a variational ansatz circuit defined by parameterized layers of quantum evolution. In theory, the approximation improves with increasing ansatz depth but gate noise and circuit complexity undermine performance in practice. Here, we investigate a multi-angle ansatz for QAOA that reduces circuit depth and improves the approximation ratio by increasing the number of classical parameters. Even though the number of parameters increases, our results indicate that good parameters can be found in polynomial time for a test dataset we consider. This new ansatz gives a 33% increase in the approximation ratio for an infinite family of MaxCut instances over QAOA. The optimal performance is lower bounded by the conventional ansatz, and we present empirical results for graphs on eight vertices that one layer of the multi-angle anstaz is comparable to three layers of the traditional ansatz on MaxCut problems. Similarly, multi-angle QAOA yields a higher approximation ratio than QAOA at the same depth on a collection of MaxCut instances on fifty and one-hundred vertex graphs. Many of the optimized parameters are found to be zero, so their associated gates can be removed from the circuit, further decreasing the circuit depth. These results indicate that multi-angle QAOA requires shallower circuits to solve problems than QAOA, making it more viable for near-term intermediate-scale quantum devices.
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Herrman, Rebekah, Phillip C. Lotshaw, James Ostrowski, Travis S. Humble, and George Siopsis. "Multi-angle quantum approximate optimization algorithm." Scientific Reports 12, no. 1 (April 26, 2022). http://dx.doi.org/10.1038/s41598-022-10555-8.
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AbstractThe quantum approximate optimization algorithm (QAOA) generates an approximate solution to combinatorial optimization problems using a variational ansatz circuit defined by parameterized layers of quantum evolution. In theory, the approximation improves with increasing ansatz depth but gate noise and circuit complexity undermine performance in practice. Here, we investigate a multi-angle ansatz for QAOA that reduces circuit depth and improves the approximation ratio by increasing the number of classical parameters. Even though the number of parameters increases, our results indicate that good parameters can be found in polynomial time for a test dataset we consider. This new ansatz gives a 33% increase in the approximation ratio for an infinite family of MaxCut instances over QAOA. The optimal performance is lower bounded by the conventional ansatz, and we present empirical results for graphs on eight vertices that one layer of the multi-angle anstaz is comparable to three layers of the traditional ansatz on MaxCut problems. Similarly, multi-angle QAOA yields a higher approximation ratio than QAOA at the same depth on a collection of MaxCut instances on fifty and one-hundred vertex graphs. Many of the optimized parameters are found to be zero, so their associated gates can be removed from the circuit, further decreasing the circuit depth. These results indicate that multi-angle QAOA requires shallower circuits to solve problems than QAOA, making it more viable for near-term intermediate-scale quantum devices.
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Pellow-Jarman, Aidan, Shane McFarthing, Ilya Sinayskiy, Daniel K. Park, Anban Pillay, and Francesco Petruccione. "The effect of classical optimizers and Ansatz depth on QAOA performance in noisy devices." Scientific Reports 14, no. 1 (July 11, 2024). http://dx.doi.org/10.1038/s41598-024-66625-6.
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AbstractThe Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm for Near-term Intermediate-Scale Quantum computers (NISQ) providing approximate solutions for combinatorial optimization problems. The QAOA utilizes a quantum-classical loop, consisting of a quantum ansatz and a classical optimizer, to minimize some cost function, computed on the quantum device. This paper presents an investigation into the impact of realistic noise on the classical optimizer and the determination of optimal circuit depth for the Quantum Approximate Optimization Algorithm (QAOA) in the presence of noise. We find that, while there is no significant difference in the performance of classical optimizers in a state vector simulation, the Adam and AMSGrad optimizers perform best in the presence of shot noise. Under the conditions of real noise, the SPSA optimizer, along with ADAM and AMSGrad, emerge as the top performers. The study also reveals that the quality of solutions to some 5 qubit minimum vertex cover problems increases for up to around six layers in the QAOA circuit, after which it begins to decline. This analysis shows that increasing the number of layers in the QAOA in an attempt to increase accuracy may not work well in a noisy device.
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Takahashi, R., Y. Matsumoto, H. Koinuma, M. Lippmaa, and M. Kawasaki. "Combinatorial optimization of atomically controlled growth for oxide films by the carrousel type laser molecular beam epitaxy." MRS Proceedings 700 (2001). http://dx.doi.org/10.1557/proc-700-s1.4.
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AbstractA new combinatorial pulsed laser deposition system has been developed for rapid optimization of epitaxial growth process by using a carrousel type masking plate. Under in-situ monitoring of growing surface with reflection high energy electron diffraction, eight films with different compositions or preparation parameters can be fabricated on a single substrate. By using this system, we have succeeded in the one lot optimization of YBa2Cu3O7-d(YBCO), PrGaO3, SrO and BaO film growths on the B-site (TiO2) terminated SrTiO3(001) substrates. Key results from these experiments include the high sensitivity of YBCO film crystallinity to the laser focusing as well as of growth behavior of epitaxial SrO and BaO films to the crystal habit with the underlying atomic layers.
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Lee, Mankoo, Dipankar Pramanik, Haifan Liang, Ed Korczynski, and Jeroen van Duren. "Optimization of Graded CIGS Solar Cells Using TCAD Simulations." MRS Proceedings 1447 (2012). http://dx.doi.org/10.1557/opl.2012.1167.
Full textAbstract:
ABSTRACTTo understand paths towards higher efficiency (η) for copper-indium-gallium-(sulfur)-selenide [CIG(S)Se] solar cells, we investigated a variety of absorber composition grading schemes for various back-side gallium (Ga), front-side sulfur (S), and double-graded Ga composition depth profiles in TCAD 1D/2D simulations. We fitted experimental results of a Back-Side Graded (BSG) solar cell with our TCAD models, prior to investigating other grading and interface schemes. The BSG solar cell was fabricated on a High Productivity Combinatorial (HPC™) platform based on sputtering Cu(In,Ga) followed by selenization. Our TCAD simulation methodology for optimizing CIG(S)Se solar cells started with a sensitivity analysis using 1D Solar-cell CAPacitance Simulator (SCAPS) [1] by selecting a typical range of key model parameters and analyzing the impact on η. We then used a 2D commercially-available Sentaurus simulation tool [2] to incorporate wavelength-dependent optical characteristics. As a result, we provide insight in the impact of grading schemes on efficiency for a fixed ‘material quality’ equal to an in-house BSG solar cell. We also quantify the effects of interface layers like MoSe2 at the Mo/CIG(S)Se interface, and an inverted surface layer at the CIG(S)Se/CdS interface.