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Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "HYBRID OPTIMIZATION MODEL"

1

Xue, Li-hua, and Yong-hua Li. "Hybrid optimization model of product concepts." Journal of Central South University of Technology 13, no. 1 (February 2006): 105–9. http://dx.doi.org/10.1007/s11771-006-0115-4.

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2

Gao, Xiaoli, Yangfei Yuan, Jie Li, and Weifeng Gao. "A Hybrid Search Model for Constrained Optimization." Discrete Dynamics in Nature and Society 2022 (September 28, 2022): 1–15. http://dx.doi.org/10.1155/2022/1190174.

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This paper proposes a hybrid model based on decomposition for constrained optimization problems. Firstly, a constrained optimization problem is transformed into a biobjective optimization problem. Then, the biobjective optimization problem is divided into a set of subproblems, and different subproblems are assigned to different Fitness functions by the direction vectors. Different from decomposition-based multiobjective optimization algorithms in which each subproblem is optimized by using the information of its neighboring subproblems, the neighbors of each subproblem are deFined based on corresponding direction vector only in the method. By combining three main components, namely, the local search model, the global search model, and the direction vector adjusting strategy, the population can gradually move toward the global optimal solution. Experiments on two sets of test problems and Five real-world engineering design problems have shown that the proposed method performs better than or is competitive with other compared methods.
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3

Miracle, D. Blandina, R. K. Viral, P. M. Tiwari, and Mohit Bansal. "Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System." International Transactions on Electrical Energy Systems 2023 (April 6, 2023): 1–25. http://dx.doi.org/10.1155/2023/5395658.

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Hybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power generation facilities to meet the rising power demand. Economic load dispatch (ELD) is a generic procedure in the electrical power system, and the ELD in power system problems involves scheduling the power generating units to reduce cost and satisfy system constraints. Metaheuristic algorithms are gaining popularity for solving constrained ELD issues because of their larger global solution capacity, flexibility, and derivative-free construction. In this research, the ELD problem of integrated renewable resources is solved using a unique solution model based on hybrid optimization. Furthermore, this work considers multiobjectives such as total wind generation cost, total cost function of thermal units, and penalty cost function. The hybrid optimization model optimizes the power generation of thermal power plants within the maximum and minimum limitations. Additionally, the turbines are selected optimally by the hybrid optimization model to ensure the power generation of wind turbines based on the demands. The proposed hybrid optimization is a combination of particle swarm optimization (PSO) and cat swarm optimization (CSO), and the new algorithm is referred to as the particle oriented cat swarm optimization model (POCSO). Finally, the performance of the proposed work is compared to other conventional models. In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. Also, for IEEE-30 bus system, the best value of the proposed work is 7.46%, 5.41%, 16.30%, 14.88%, 17.60%, 13.86%, 15.21%, 17.49%, and 4.27% better than that of the PSO, CSO, SLnO, DA, MFO, EHO, WOA, multiagent glowworm swarm optimization (MAGSO), and Harris hawks optimization-based feed-forward neural network (HHO-FNN) methods, respectively.
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Stevanović, Dejan, Mirjana Banković, Milica Pešić-Georgiadis, and Lazar Stojanović. "Hybrid model for uncertainty assessment in open pit optimization." Tehnika 75, no. 2 (2020): 161–71. http://dx.doi.org/10.5937/tehnika2002161s.

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5

Sukheja, Deepak, and Umesh Kumar Singh. "Novel Distributed Query Optimization Model and Hybrid Query Optimization Algorithm." International Journal of Computer Applications 75, no. 17 (August 23, 2013): 22–32. http://dx.doi.org/10.5120/13203-0461.

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6

Regitha, M. R., Dr Paul Varghese, Shailesh Sivan, and Antony Nijo. "Handoff Delay Optimization Using Hybrid Prediction Model." International Journal of Networked and Distributed Computing 6, no. 2 (2018): 99. http://dx.doi.org/10.2991/ijndc.2018.6.2.5.

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Cahyandari, R., R. L. Ariany, and Sukono. "Optimization of hybrid model on hajj travel." IOP Conference Series: Materials Science and Engineering 332 (March 2018): 012042. http://dx.doi.org/10.1088/1757-899x/332/1/012042.

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Franco-Lara, E., N. Volk, T. Hertel, V. Galvanauskas, and A. Lübbert. "Model-Supported Optimization of Recombinant Protein Production Using Hybrid Models." Chemie Ingenieur Technik 73, no. 6 (June 2001): 654–55. http://dx.doi.org/10.1002/1522-2640(200106)73:6<654::aid-cite6543333>3.0.co;2-8.

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9

He, Jian Feng, and Xiao Xiong Jin. "Multiobjective Optimization of Hybrid Electrical Vehicle Powertrain Mounting System Using Hybrid Genetic Algorithm." Applied Mechanics and Materials 87 (August 2011): 30–37. http://dx.doi.org/10.4028/www.scientific.net/amm.87.30.

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Powertrain mounting system of a Hybrid Electrical Vehicle (HEV) is analyzed and researched, the expression of energy distribution matrix and that of mounting reaction force are derived, and mathematical model of the system is established in Matlab. Correctness of the model established is tested and verified through model establishing for simulation and calculation in ADAMS. Features of Hybrid Genetic Algorithm (HGA) for multiobjective optimization are analyzed and researched, model for calculation of multiobjective optimization using Hybrid Genetic Algorithm is established, targets for optimization of the system are determined, and optimization is executed based on the mounting stiffness parameters. The result that the system is optimized apparently by Hybrid Genetic Algorithm is revealed through contrast of the energy distribution matrix and mounting reaction force of pre and post-optimization.
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10

Li, Wenwei, and Long Zhu. "Multi-objective Optimization Method for Hybrid Energy Storage Capacity of Wind Farm Based on Source-load Interaction." Journal of Physics: Conference Series 2418, no. 1 (February 1, 2023): 012054. http://dx.doi.org/10.1088/1742-6596/2418/1/012054.

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Abstract The traditional method for multi-objective optimization of a wind farm’s hybrid energy storage capacity does not fully consider the impact of source-load interaction on wind power consumption capacity, resulting in a high total system operation cost. Therefore, a multi-objective optimization method for the hybrid energy storage capacity of the wind farm based on source-load interaction is proposed. According to the topological structure of the system, a multi-objective optimization model is constructed. The constraint conditions of the optimization model are studied. By synthesizing the constraint conditions, multiple objective functions are aggregated into a single objective function. The model is solved by using the ordinal optimization theory to get the optimal solution of the objective function. The experimental demonstration adopted the proposed method to improve the wind power system’s energy storage capacity. The results show that the operation cost of the system is lower after the proposed method is adopted to improve the wind farm’s hybrid energy storage capacity. Therefore, it can be proved that the total operation cost of the system can be decreased by integrating the interactive load into the hybrid energy storage optimization’s wind farm capacity.
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Дисертації з теми "HYBRID OPTIMIZATION MODEL"

1

Sachs, Julia [Verfasser]. "Model-Based Optimization of Hybrid Energy Systems / Julia Sachs." Aachen : Shaker, 2016. http://d-nb.info/1101185112/34.

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Werner, Quentin. "Model-based optimization of electrical system in the early development stage of hybrid drivetrains." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0109.

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Cette thèse analyse les challenges auxquels sont confrontés les composants électriques pour les systèmes de traction hybrides. L’analyse de ces composants et de leurs interactions en tant qu’entité indépendante est un sujet de recherche important afin de dimensionner de manière optimale le système au lieu de combiner des composants optimaux. Les véhicules hybrides sont un domaine de recherche qui suscite un grand intérêt parce qu’il s’agit d’une solution efficace à court terme afin de préparer la transition énergétique vers les véhicules à zéro émission. Malgré les avantages de cette solution, c’est un sujet de recherche complexe car les composants électriques doivent être intégrés dans un système de propulsion conventionnel. Ainsi le but de ce travail de recherche est axé sur la détermination de méthodes appropriées pour étudier les composants électriques et les contributions apportées par cette thèse visent à répondre à la problématique suivante : déterminer le niveau suffisant de détails pour modéliser les systèmes électriques pour les systèmes de traction pour véhicules hybrides afin d’identifier le dimensionnement idéal des composants pour différents systèmes pendant la phase de développement. Afin de résoudre cette problématique, ce rapport est divisé en quatre parties au sein de six chapitres. D’abord l’état de l’art des véhicules hybrides, des composants électriques ainsi que des méthodes d’optimisation associées sont présentés (chapitre 1). Ensuite, pour chaque composant (chapitre 2 à 4), des méthodes de modélisation appropriées sont déterminées afin de les modéliser mais aussi afin d’évaluer leur intégration dans le système de propulsion. Puis, une solution pour l’étude du système globale est déterminée à partir de l’analyse de travaux précédents (chapitre 5). Finalement, une approche d’optimisation est développée et permet d’analyser différents systèmes ainsi que l’influence de différents paramètres sur le dimensionnement (chapitre 6). Grâce à l’analyse du développement actuel et des travaux précédents sur le sujet ainsi qu’au développement d’outils de simulation, cette thèse étudie et analyse les relations entre le niveau de tension et de courant, et les performances du système dans différents cas. Les résultats permettent de déterminer l’influence de ces paramètres sur les composants ainsi que l’impact de l’environnement industriel sur les résultats. En tenant compte du cadre législatif actuel, les résultats convergent globalement tous dans la même direction : une réduction du niveau de tension, respectivement une augmentation du courant, entraine une amélioration du système global par rapport aux méthodes de dimensionnent actuelles. Ces observations sont liées à l’architecture, au cycle d’évaluation et à l’environnement considérés mais les méthodes et l’approche développée ont posé les bases pour étendre les connaissances dans le domaine de l’optimisation des véhicules hybrides. En plus de l’optimisation générale, des cas particuliers sont analysés afin de montrer la modularité des méthodes et l’influence de paramètres supplémentaires (système 48V ou convertisseur Boost). Afin de conclure, cette thèse a mis en place les bases pour l’étude des composants électriques pour les véhicules hybrides. De part un environnement fluctuant et les nombreuses technologies possibles, ce sujet suscite encore un grand intérêt et les points suivants peuvent être encore étudiés de manière plus détaillée : * Application des méthodes pour d’autres systèmes de propulsion (autre architectures hybrides, véhicule à pile à combustible ou tout électrique), * Étude de nouvelles technologies comme le carbure de silicium pour l’électronique de puissance, la machine à reluctance variable ou le sulfure de lithium pour les batteries, * Analyse d’autre cycle d’évaluation ainsi que leur cadre législatif, * Mise en place de structures additionnelles pour l’électronique de puissance, * Validations supplémentaires avec d’autres composants
This work analyses the challenges faced by the electric components for traction purpose in hybrid drivetrains. It investigates the components and their interactions as an independent entity in order to refine the scope of investigation and to find the best combinations of components instead of the best components combinations. Hybrid vehicle is currently a topic of high interest because it stands for a suitable short-term solution towards zero emission vehicle. Despite its advantages, it is a challenging topic because the components need to be integrated in a conventional drivetrain architecture. Therefore, the focus of this work is set on the determination of the right methods to investigate only the electric components for traction purpose. The aim and the contributions of this work lies thereby in the resolution of the following statement: Determine the sufficient level of details in modeling electric components at the system level and develop models and tools to perform dynamic simulations of these components and their interactions in a global system analysis to identify ideal designs of various drivetrain electric components during the design process. To address these challenges, this work is divided in four main parts within six chapters. First the current status of the hybrid vehicle, the electric components and the associated optimization methods and simulation are presented (first chapter). Then for each component, the right modeling approach is defined in order to investigate the electrical, mechanical and thermal behavior of the components as well as methods to evaluate their integration in the drivetrain (second to fourth chapter). After this, a suitable method is defined to evaluate the global system and to investigate the interactions between the components based on the review of relevant previous works (chapter five). Finally, the last chapter presents the optimization approach considered in this work and the results by analyzing different system and cases (chapter six). Thanks to the analysis of the current status, previous works and the development of the simulations tools, this work investigates the relationships between the voltage, the current and the power in different cases. The results enable, under the considered assumptions of the work, to determine the influence of these parameters on the components and of the industrial environment on the optimization results. Considering the current legislative frame, all the results converge toward the same observation referred to the reference systems: a reduction of the voltage and an increase of the current leads to an improvement of the integration and the performance of the system. These observations are linked with the considered architecture, driving cycle and development environment but the developed methods and approaches have set the basis to extend the knowledge for the optimization of the electric system for traction purpose. Beside the main optimization, special cases are investigated to show the influence of additional parameters (increase of the power, 48V-system, machine technology, boost-converter…) In order to conclude, this work have set the basis for further investigations about the electric components for traction purpose in more electrified vehicle. Due to the constantly changing environment, the new technologies and the various legislative frame, this topic remains of high interest and the following challenges still need to be deeper investigated: * Application of the methods for other drivetrain architecture (series hybrid, power-split hybrid, fuel-cell vehicle, full electric vehicle), * Investigation of new technologies such as silicon-carbide for the power electronics, lithium–sulfur battery or switch reluctance machine, * Investigation of other driving cycle, legislative frame, * Integration of additional power electronics structure, * Further validation of the modeling approaches with additional components
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3

Bertini, Lorenzo. "Modeling and Optimization of a Fuel Cell Hybrid System." Thesis, KTH, Skolan för kemivetenskap (CHE), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-51143.

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The purpose of this project was the modeling, optimization and prediction of a hybrid system composed of a fuel cell, a dc-dc converter and a supercapacitor in series. Lab tests were performed for each device to understand their behavior, and then each one was modeled using software (Simulink). The validation of the model was done by comparing its results with measured data; finally the model was used for the optimization and the prediction of the hybrid system
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4

Šandera, Čeněk. "Hybridní model metaheuristických algoritmů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-234259.

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The main topic of this PhD thesis is metaheuristic algorithm in wider scope. The first chapters are dedicated to a description of broader context of metaheuristics, i.e. various optimization classes, determination of their omplexity and different approaches to their solutions. The consequent discussion about metaheuristics and their typical characteristics is followed by several selected examples of metaheuristics concepts. The observed characteristics serve as a base for building general metaheuristics model which is suitable for developing brand new or hybrid algorithms. The thesis is concluded by illustration of author’s publications with discussion about their adaptation to the proposed model. On the attached CD, there is also available a program implementation of the created model.
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5

Thalheimer, William Cooper. "Structural analysis and optimization with a locally-Cartesian Hybrid Shell Model." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107054.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 131-133).
The Hybrid Shell Model (HSM) is presented as an intermediate-fidelity structural model well suited for conceptual design of aerospace vehicles. Although significantly simpler and more economical than full 3D elasticity models, it can still capture full 3D geometries, large deformations, and anisotropic materials. HSM is formulated from the full 3D equilibrium and compatibility equations all projected onto local bases defined on the 2D shell manifold. General anisotropic constitutive equations are also formulated in the local 2D shell manifold bases. The resulting continuous HSM formulation is discretized in weak form with a Galerkin finite element method (FEM), with spherical interpolation used for the local basis vectors. Displacements, basis rotations, and stress resultants are the primary unknowns. A fully adjoint-consistent plane-stress HSM version (HSM2D) is developed for the purpose of model verification and demonstration of order-of-accuracy convergence. The Method of Exact Solutions (MES) is applied to the case of a uniform plate hanging under its own weight. The effectiveness of the adjoint model for structural optimization is also demonstrated for a simplified rotor blade in a centrifugal force field, featuring non-uniform forcing, non-zero Poisson ratio, large deflection, and optimization of multiple parameters. The suitability of HSM as an intermediate fidelity conceptual aircraft design tool is thus demonstrated.
by William Cooper Thalheimer.
S.M.
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6

Wu, Zheng. "Hybrid Multi-Objective Optimization Models for Managing Pavement Assets." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/26092.

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Increasingly tighter budgets, changes in government role/function, declines in staff resources, and demands for increased accountability in the transportation field have brought unprecedented challenges for state transportation officials at all management levels. Systematic methodologies for effective management of a specific type of infrastructure (e.g., pavement and bridges) as well as for holistically managing all types of infrastructure assets are being developed to approach these challenges. In particular, the intrinsic characteristics of highway system make the use of multi-objective optimization techniques particularly attractive for managing highway assets. Recognizing the need for effective tradeoff tools and the limitations of state-of-practice analytical models and tools in highway asset management, the main objective of this dissertation was to develop a performance-based asset management framework that uses multi-objective optimization techniques and consists of stand-alone but logically interconnected optimization models for different management levels. Based on a critical review of popular multi-objective optimization techniques and their applications in highway asset management, a synergistic integration of complementary multi-criteria optimization techniques is recommended for the development of practical and efficient decision-supporting tools. Accordingly, the dissertation first proposes and implements a probabilistic multi-objective model for performance-based pavement preservation programming that uses the weighting sum method and chance constraints. This model can handle multiple incommensurable and conflicting objectives while considering probabilistic constraints related to the available budget over the planning horizon, but is found more suitable to problems with small number of objective functions due to its computational intensity. To enhance the above model, a hybrid model that requires less computing time and systematically captures the decision maker's preferences on multiple objectives is developed by combining the analytic hierarchy process and goal programming. This model is further extended to also capture the relative importance existent within optimization constraints to be suitable for allocations of funding across multiple districts for a decentralized state department of transportation. Finally, as a continuation of the above proposed models for the succeeding management level, a project selection model capable of incorporating qualitative factors (e.g. equity, user satisfaction) into the decision making is developed. This model combines k-means clustering, analytic hierarchy process and integer linear programming. All the models are logically interconnected in a comprehensive resource allocation framework. Their feasibility, practicality and potential benefits are illustrated through various case studies and recommendations for further developments are provided.
Ph. D.
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7

Meyer, Danielle L. "Energy Optimization of a Hybrid Unmanned Aerial Vehicle (UAV)." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523493111005807.

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Yeung, King-ho, and 楊景豪. "An optimization model for a solar hybrid water heating and adsorption ice-making system." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29632432.

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Shah, Kshitij P. "Calibration and Validation of a Hybrid Vehicle Model for its Implementation inOptimization Routines for Model-Based Fuel Economy Optimization." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1501183963696296.

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Jiang, Siyu. "A Comparison of PSO, GA and PSO-GA Hybrid Algorithms for Model-based Fuel Economy Optimization of a Hybrid-Electric Vehicle." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu156612591067731.

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Книги з теми "HYBRID OPTIMIZATION MODEL"

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Melin, Patricia, Ivette Miramontes, and German Prado Arechiga. Nature-inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-82219-4.

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Hybrid Simulation Models of Production Networks. Springer, 2010.

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Hybrid Simulation Models of Production Networks. Springer, 2001.

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Melin, Patricia, Ivette Miramontes, and German Prado Arechiga. Nature-Inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis. Springer International Publishing AG, 2021.

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5

Bäck, Thomas. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099713.001.0001.

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This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.
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Частини книг з теми "HYBRID OPTIMIZATION MODEL"

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Fontaine, Daniel, Laurent Michel, and Pascal Van Hentenryck. "Model Combinators for Hybrid Optimization." In Lecture Notes in Computer Science, 299–314. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40627-0_25.

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Margain, Lourdes, Alberto Ochoa, Lissette Martínez Almaguer, and Rigoberto Velázquez. "Model on Oil Platform Using Brain Storm Optimization Algorithm." In Hybrid Intelligent Systems, 311–20. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76351-4_32.

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Ben Younes, Hajer, Ameni Azzouz, and Meriem Ennigrou. "Solving Flexible Job Shop Scheduling Problem Using Hybrid Bilevel Optimization Model." In Hybrid Intelligent Systems, 340–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14347-3_33.

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Sun, Zhan-Li, Nan Wang, Ru-Xia Ban, and Xia Chen. "Facial Age Estimation with a Hybrid Model." In Proceedings in Adaptation, Learning and Optimization, 262–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01520-6_24.

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Khorram, Mahsa, Pedro Faria, Omid Abrishambaf, and Zita Vale. "Economic Impact of an Optimization-Based SCADA Model for an Office Building." In Hybrid Intelligent Systems, 166–75. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14347-3_17.

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Chanda, Anupama, R. N. Mukherjee, and Bijan Sarkar. "Performance Evaluation of Management Faculty Using Hybrid Model of Logic—AHP." In Operations Research and Optimization, 365–75. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7814-9_25.

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Nagaraju, Vidhyashree, and Lance Fiondella. "A Hybrid Model Fitting Framework Considering Accuracy and Performance." In Reliability and Maintenance Modeling with Optimization, 257–78. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003095231-14.

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Majed, Hadeer, Samaher Al-Janabi, and Saif Mahmood. "Hybridized Deep Learning Model with Optimization Algorithm: A Novel Methodology for Prediction of Natural Gas." In Hybrid Intelligent Systems, 79–95. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27409-1_8.

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Chen, Benhui, and Jinglu Hu. "Protein Structure Prediction Based on HP Model Using an Improved Hybrid EDA." In Evolutionary Learning and Optimization, 193–214. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12834-9_9.

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Melin, Patricia, Ivette Miramontes, and German Prado Arechiga. "Conclusions of the Hybrid Medical Model." In Nature-inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis, 111–12. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82219-4_5.

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Тези доповідей конференцій з теми "HYBRID OPTIMIZATION MODEL"

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Horng, Ming-Huwi, Jin-Yi Chen, and Ren-Jean Liou. "Honey Bee Mating Optimization Scheme for Active Contour Model." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.42.

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Zhang, Zhijie. "A new Hybrid Infection model optimization Algorithm." In 3rd International Conference on Material, Mechanical and Manufacturing Engineering (IC3ME 2015). Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/ic3me-15.2015.202.

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De Souza, Bruno, Andre De Carvalho, Rodrigo Calvo, and Renato Ishii. "Multiclass SVM Model Selection Using Particle Swarm Optimization." In 2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06). IEEE, 2006. http://dx.doi.org/10.1109/his.2006.264914.

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Nenashev, Alexey V. "Mathematical Model of Billing for TheOoL DAO." In International Workshop “Hybrid methods of modeling and optimization in complex systems”. European Publisher, 2023. http://dx.doi.org/10.15405/epct.23021.2.

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Pokushko, M. "Slack Based Model for Enterprises’ Efficiency Improvement." In International Workshop “Hybrid methods of modeling and optimization in complex systems”. European Publisher, 2023. http://dx.doi.org/10.15405/epct.23021.43.

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Khurana, Rahul, and Himanshu Gupta. "A hybrid model on cloud security." In 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2016. http://dx.doi.org/10.1109/icrito.2016.7784979.

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Bansal, Mani, and D. K. Lobiyal. "Word-Character Hybrid Machine Translation Model." In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2020. http://dx.doi.org/10.1109/icrito48877.2020.9197865.

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Jianfang, Wang, and Li Weihua. "Optimization Algorithm Based on T-S Fuzzy Model of Self-Adaptive Disturbed Particle Swarm Optimization and Neural Network." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.94.

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Rocha, Lucio A., and Eleri Cardozo. "A Hybrid Optimization Model for Green Cloud Computing." In 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC). IEEE, 2014. http://dx.doi.org/10.1109/ucc.2014.9.

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Chanyuan Liu and Jinpeng Lu. "A hybrid optimization model for hotel yield management." In Proceedings of ICSSSM '05. 2005 International Conference on Services Systems and Services Management, 2005. IEEE, 2005. http://dx.doi.org/10.1109/icsssm.2005.1499473.

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Звіти організацій з теми "HYBRID OPTIMIZATION MODEL"

1

Hough, Patricia Diane, Genetha Anne Gray, Joseph Pete Jr Castro, .), and Anthony Andrew Giunta. Developing a computationally efficient dynamic multilevel hybrid optimization scheme using multifidelity model interactions. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/877137.

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Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0739.

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The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor. Afterwards, based on the Isight and ADVISOR co-simulation platform, with the goal of ensuring certain dynamic performance and optimal fuel economy of the vehicle, the multi-island genetic algorithm is used to optimize the parameters of the membership function of the fuzzy control strategy to overcome it to a certain extent. The disadvantages of selecting parameters based on experience are compensated for, and the efficiency and feasibility of fuzzy control are improved. Finally, the PHEV vehicle model simulation comparison was carried out under the UDDS working condition through ADVISOR software. The optimization results show that while ensuring the required power performance, the vehicle fuzzy controller after parameter optimization using the multi-island genetic algorithm is more efficient, which can significantly reduce vehicle fuel consumption and improve exhaust emissions.
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3

Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0741.

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Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for this one control input. This paper presents a novel model based on an input linearizing transformation. It is demonstrably superior to alternative model forms, in that the core dynamics of the model (battery state of energy) are linear, and the non-linearities of the model are pushed into the inputs and outputs in a Wiener/Hammerstein form. The output non-linearities can be approximated using a quadratic model, which creates a problem in the linear-quadratic framework. This facilitates the direct application of linear control approaches such as LQR control, predictive control, or Model Predictive Control (MPC). The paper demonstrates the approach using the ELectrified Vehicle library for sImulation and Optimization (ELVIO). It is an open-source MATLAB/Simulink library designed for the quick and easy simulation and optimization of different powertrain and drivetrain architectures. It follows a modelling methodology that combines backward-facing and forward-facing signal path, which means that no driver model is required. The results show that the approximated solution provides a performance that is very close to the solution of the original problem except for extreme parts of the operating range (in which case the solution tends to be driven by constraints anyway).
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