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Добірка наукової літератури з теми "Optimization variable"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Optimization variable"

ARAKAWA, Masao, Takaharu Shirai, Hitomi Kono, Hirotaka NAKAYAMA, and Hiroshi ISHIKAWA. "Approximate Optimization Using RBF : Mixed variable Optimization with Discrete Variables." Proceedings of Design & Systems Conference 2003.13 (2003): 108–11. http://dx.doi.org/10.1299/jsmedsd.2003.13.108.

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Salgueiro, Yamisleydi, Jorge L. Toro, Rafael Bello, and Rafael Falcon. "Multiobjective variable mesh optimization." Annals of Operations Research 258, no. 2 (May 18, 2016): 869–93. http://dx.doi.org/10.1007/s10479-016-2221-5.

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Puris, Amilkar, Rafael Bello, Daniel Molina, and Francisco Herrera. "Variable mesh optimization for continuous optimization problems." Soft Computing 16, no. 3 (August 10, 2011): 511–25. http://dx.doi.org/10.1007/s00500-011-0753-9.

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Liao, Tianjun, Krzysztof Socha, Marco A. Montes de Oca, Thomas Stutzle, and Marco Dorigo. "Ant Colony Optimization for Mixed-Variable Optimization Problems." IEEE Transactions on Evolutionary Computation 18, no. 4 (August 2014): 503–18. http://dx.doi.org/10.1109/tevc.2013.2281531.

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Singh, Prem, and Himanshu Chaudhary. "A Modified Jaya Algorithm for Mixed-Variable Optimization Problems." Journal of Intelligent Systems 29, no. 1 (October 23, 2018): 1007–27. http://dx.doi.org/10.1515/jisys-2018-0273.

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Abstract Mixed-variable optimization problems consist of the continuous, integer, and discrete variables generally used in various engineering optimization problems. These variables increase the computational cost and complexity of optimization problems due to the handling of variables. Moreover, there are few optimization algorithms that give a globally optimal solution for non-differential and non-convex objective functions. Initially, the Jaya algorithm has been developed for continuous variable optimization problems. In this paper, the Jaya algorithm is further extended for solving mixed-variable optimization problems. In the proposed algorithm, continuous variables remain in the continuous domain while continuous domains of discrete and integer variables are converted into discrete and integer domains applying bound constraint of the middle point of corresponding two consecutive values of discrete and integer variables. The effectiveness of the proposed algorithm is evaluated through examples of mixed-variable optimization problems taken from previous research works, and optimum solutions are validated with other mixed-variable optimization algorithms. The proposed algorithm is also applied to two-plane balancing of the unbalanced rigid threshing rotor, using the number of balance masses on plane 1 and plane 2. It is found that the proposed algorithm is computationally more efficient and easier to use than other mixed optimization techniques.

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Naik, Kamlesh Kumar. "Optimization of Complex Function Variable." International Journal for Research in Applied Science and Engineering Technology V, no. X (October 22, 2017): 554–57. http://dx.doi.org/10.22214/ijraset.2017.10081.

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Segretier, Wilfried, Martine Collard, Laurent Brisson, and Jean-Emile Symphor. "Variable optimization for flood prediction." Ingénierie des systèmes d'information 16, no. 3 (June 30, 2011): 113–39. http://dx.doi.org/10.3166/isi.16.3.113-139.

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Deng, Geng, and Michael C. Ferris. "Variable-Number Sample-Path Optimization." Mathematical Programming 117, no. 1-2 (July 18, 2007): 81–109. http://dx.doi.org/10.1007/s10107-007-0164-y.

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Tian, Hao, Xiang Fan Piao, and Cheng Zhe Xu. "Parameter Optimization of Gas Purge-Microsyringe Extraction." Advanced Materials Research 1033-1034 (October 2014): 607–10. http://dx.doi.org/10.4028/www.scientific.net/amr.1033-1034.607.

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This paper proposes parameter optimization method of GP-MSE based on extraction parameters model. First, we identify the function relationship between dependent and independent variables by using neural networks. Second, independent variable set is made by interpolating the original independent variables, and then input these independent variables to fitted function to generate the dependent variable set. Last, we find the maximum value on dependent variable set to confirm the optimal parameters. Experimental result shows that the proposed method is not only simple and fast, but also ideal for recovery of single analyte.

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Gao, Li, and Rong Rong Wang. "Study on Mix-Variable Collaborative Design Optimization." Applied Mechanics and Materials 215-216 (November 2012): 592–96. http://dx.doi.org/10.4028/www.scientific.net/amm.215-216.592.

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In order to deal with complex product design optimization problems with both discrete and continuous variables, mix-variable collaborative design optimization algorithm is put forward based on collaborative optimization, which is an efficient way to solve mix-variable design optimization problems. On the rule of “divide and rule”, the algorithm decouples the problem into some relatively simple subsystems. Then by using collaborative mechanism, the optimal solution is obtained. Finally, the result of a case shows the feasibility and effectiveness of the new algorithm.

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Більше джерел

Дисертації з теми "Optimization variable"

Pelamatti, Julien. "Mixed-variable Bayesian optimization : application to aerospace system design." Thesis, Lille 1, 2020. http://www.theses.fr/2020LIL1I003.

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Анотація:

Dans le cadre de la conception de systèmes complexes, tels que les aéronefs et les lanceurs, la présence de fonctions d'objectifs et/ou de contraintes à forte intensité de calcul (e.g., modèles d'éléments finis) couplée à la dépendance de choix de conception technologique discrets et non ordonnés entraîne des problèmes d'optimisation difficiles. De plus, une partie de ces choix technologiques est associée à un certain nombre de variables de conception continues et discrètes spécifiques qui ne doivent être prises en considération que si des choix technologiques spécifiques sont faits. Par conséquent, le problème d'optimisation qui doit être résolu afin de déterminer la conception optimale du système présente un espace de recherche et un domaine de faisabilité variant de façon dynamique. Les algorithmes existants qui permettent de résoudre ce type particulier de problèmes ont tendance à exiger une grande quantité d'évaluations de fonctions afin de converger vers l'optimum réalisable, et sont donc inadéquats lorsqu'il s'agit de résoudre les problèmes à forte intensité de calcul. Pour cette raison, cette thèse explore la possibilité d'effectuer une optimisation de l'espace de conception contraint à variables mixtes et de taille variable en s'appuyant sur des méthodes d’optimisation à base de modèles de substitution créés à l'aide de processus Gaussiens, également connue sous le nom d'optimisation Bayésienne. Plus spécifiquement, 3 axes principaux sont discutés. Premièrement, la modélisation de substitution par processus gaussien de fonctions mixtes continues/discrètes et les défis qui y sont associés sont discutés en détail. Un formalisme unificateur est proposé afin de faciliter la description et la comparaison entre les noyaux existants permettant d'adapter les processus gaussiens à la présence de variables discrètes non ordonnées. De plus, les performances réelles de modélisation de ces différents noyaux sont testées et comparées sur un ensemble de benchmarks analytiques et de conception ayant des caractéristiques et des paramétrages différents. Dans la deuxième partie de la thèse, la possibilité d'étendre la modélisation de substitution mixte continue/discrète à un contexte d'optimisation Bayésienne est discutée. La faisabilité théorique de cette extension en termes de modélisation de la fonction objectif/contrainte ainsi que de définition et d'optimisation de la fonction d'acquisition est démontrée. Différentes alternatives possibles sont considérées et décrites. Enfin, la performance de l'algorithme d'optimisation proposé, avec diverses paramétrisations des noyaux et différentes initialisations, est testée sur un certain nombre de cas-test analytiques et de conception et est comparée aux algorithmes de référence.Dans la dernière partie de ce manuscrit, deux approches permettant d'adapter les algorithmes d'optimisation bayésienne mixte continue/discrète discutés précédemment afin de résoudre des problèmes caractérisés par un espace de conception variant dynamiquement au cours de l’optimisation sont proposées. La première adaptation est basée sur l'optimisation parallèle de plusieurs sous-problèmes couplée à une allocation de budget de calcul basée sur l'information fournie par les modèles de substitution. La seconde adaptation, au contraire, est basée sur la définition d'un noyau permettant de calculer la covariance entre des échantillons appartenant à des espaces de recherche partiellement différents en fonction du regroupement hiérarchique des variables dimensionnelles. Enfin, les deux alternatives sont testées et comparées sur un ensemble de cas-test analytiques et de conception.Globalement, il est démontré que les méthodes d'optimisation proposées permettent de converger vers les optimums des différents types de problèmes considérablement plus rapidement par rapport aux méthodes existantes. Elles représentent donc un outil prometteur pour la conception de systèmes complexes
Within the framework of complex system design, such as aircraft and launch vehicles, the presence of computationallyintensive objective and/or constraint functions (e.g., finite element models and multidisciplinary analyses)coupled with the dependence on discrete and unordered technological design choices results in challenging optimizationproblems. Furthermore, part of these technological choices is associated to a number of specific continuous anddiscrete design variables which must be taken into consideration only if specific technological and/or architecturalchoices are made. As a result, the optimization problem which must be solved in order to determine the optimalsystem design presents a dynamically varying search space and feasibility domain.The few existing algorithms which allow solving this particular type of problems tend to require a large amountof function evaluations in order to converge to the feasible optimum, and result therefore inadequate when dealingwith the computationally intensive problems which can often be encountered within the design of complex systems.For this reason, this thesis explores the possibility of performing constrained mixed-variable and variable-size designspace optimization by relying on surrogate model-based design optimization performed with the help of Gaussianprocesses, also known as Bayesian optimization. More specifically, 3 main axes are discussed. First, the Gaussianprocess surrogate modeling of mixed continuous/discrete functions and the associated challenges are extensivelydiscussed. A unifying formalism is proposed in order to facilitate the description and comparison between theexisting kernels allowing to adapt Gaussian processes to the presence of discrete unordered variables. Furthermore,the actual modeling performances of these various kernels are tested and compared on a set of analytical and designrelated benchmarks with different characteristics and parameterizations.In the second part of the thesis, the possibility of extending the mixed continuous/discrete surrogate modeling toa context of Bayesian optimization is discussed. The theoretical feasibility of said extension in terms of objective/-constraint function modeling as well as acquisition function definition and optimization is shown. Different possiblealternatives are considered and described. Finally, the performance of the proposed optimization algorithm, withvarious kernels parameterizations and different initializations, is tested on a number of analytical and design relatedtest-cases and compared to reference algorithms.In the last part of this manuscript, two alternative ways of adapting the previously discussed mixed continuous/discrete Bayesian optimization algorithms in order to solve variable-size design space problems (i.e., problemscharacterized by a dynamically varying design space) are proposed. The first adaptation is based on the paralleloptimization of several sub-problems coupled with a computational budget allocation based on the informationprovided by the surrogate models. The second adaptation, instead, is based on the definition of a kernel allowingto compute the covariance between samples belonging to partially different search spaces based on the hierarchicalgrouping of design variables. Finally, the two alternatives are tested and compared on a set of analytical and designrelated benchmarks.Overall, it is shown that the proposed optimization methods allow to converge to the various constrained problemoptimum neighborhoods considerably faster when compared to the reference methods, thus representing apromising tool for the design of complex systems

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Zebian, Hussam. "Multi-variable optimization of pressurized oxy-coal combustion." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/67808.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 81-82).
Simultaneous multi-variable gradient-based optimization with multi-start is performed on a 300 MWe wet-recycling pressurized oxy-coal combustion process with carbon capture and sequestration. The model accounts for realistic component behavior such as heat losses, steam leaks, pressure drops, cycle irreversibilities, and other technological and economical considerations. The optimization study involves 16 variables, three of which are integer valued, and 10 constraints with the objective of maximizing thermal efficiency. The solution procedure follows active inequality constraints which are identified by thermodynamic-based analysis to facilitate convergence. Results of the multi-variable optimization are compared to a pressure sensitivity analysis similar to those performed in literature; the basecase of both assessments performed here is a favorable solution found in literature. Significant cycle performance improvements are obtained compared to this literature design at a much lower operating pressure and with moderate changes in the other operating variables. The effect of the variables on the cycle performance and on the constraints are analyzed and explained to obtain increased understanding of the actual behavior of the system. This study reflects the importance of simultaneous multi-variable optimization in revealing the system characteristics and uncovering the favorable solutions with higher efficiency than the atmospheric operation or those obtained by single variable sensitivity analysis.
by Hussam Zebian.
S.M.

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Ndiaye, Eugene. "Safe optimization algorithms for variable selection and hyperparameter tuning." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT004/document.

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Le traitement massif et automatique des données requiert le développement de techniques de filtration des informations les plus importantes. Parmi ces méthodes, celles présentant des structures parcimonieuses se sont révélées idoines pour améliorer l’efficacité statistique et computationnelle des estimateurs, dans un contexte de grandes dimensions. Elles s’expriment souvent comme solution de la minimisation du risque empirique régularisé s’écrivant comme une somme d’un terme lisse qui mesure la qualité de l’ajustement aux données, et d’un terme non lisse qui pénalise les solutions complexes. Cependant, une telle manière d’inclure des informations a priori, introduit de nombreuses difficultés numériques pour résoudre le problème d’optimisation sous-jacent et pour calibrer le niveau de régularisation. Ces problématiques ont été au coeur des questions que nous avons abordées dans cette thèse.Une technique récente, appelée «Screening Rules», propose d’ignorer certaines variables pendant le processus d’optimisation en tirant bénéfice de la parcimonie attendue des solutions. Ces règles d’élimination sont dites sûres lorsqu’elles garantissent de ne pas rejeter les variables à tort. Nous proposons un cadre unifié pour identifier les structures importantes dans ces problèmes d’optimisation convexes et nous introduisons les règles «Gap Safe Screening Rules». Elles permettent d’obtenir des gains considérables en temps de calcul grâce à la réduction de la dimension induite par cette méthode. De plus, elles s’incorporent facilement aux algorithmes itératifs et s’appliquent à un plus grand nombre de problèmes que les méthodes précédentes.Pour trouver un bon compromis entre minimisation du risque et introduction d’un biais d’apprentissage, les algorithmes d’homotopie offrent la possibilité de tracer la courbe des solutions en fonction du paramètre de régularisation. Toutefois, ils présentent des instabilités numériques dues à plusieurs inversions de matrice, et sont souvent coûteux en grande dimension. Aussi, ils ont des complexités exponentielles en la dimension du modèle dans des cas défavorables. En autorisant des solutions approchées, une approximation de la courbe des solutions permet de contourner les inconvénients susmentionnés. Nous revisitons les techniques d’approximation des chemins de régularisation pour une tolérance prédéfinie, et nous analysons leur complexité en fonction de la régularité des fonctions de perte en jeu. Il s’ensuit une proposition d’algorithmes optimaux ainsi que diverses stratégies d’exploration de l’espace des paramètres. Ceci permet de proposer une méthode de calibration de la régularisation avec une garantie de convergence globale pour la minimisation du risque empirique sur les données de validation.Le Lasso, un des estimateurs parcimonieux les plus célèbres et les plus étudiés, repose sur une théorie statistique qui suggère de choisir la régularisation en fonction de la variance des observations. Ceci est difficilement utilisable en pratique car, la variance du modèle est une quantité souvent inconnue. Dans de tels cas, il est possible d’optimiser conjointement les coefficients de régression et le niveau de bruit. Ces estimations concomitantes, apparues dans la littérature sous les noms de Scaled Lasso, Square-Root Lasso, fournissent des résultats théoriques aussi satisfaisants que celui du Lasso tout en étant indépendant de la variance réelle. Bien que présentant des avancées théoriques et pratiques importantes, ces méthodes sont aussi numériquement instables et les algorithmes actuellement disponibles sont coûteux en temps de calcul. Nous illustrons ces difficultés et nous proposons à la fois des modifications basées sur des techniques de lissage pour accroitre la stabilité numérique de ces estimateurs, ainsi qu’un algorithme plus efficace pour les obtenir
Massive and automatic data processing requires the development of techniques able to filter the most important information. Among these methods, those with sparse structures have been shown to improve the statistical and computational efficiency of estimators in a context of large dimension. They can often be expressed as a solution of regularized empirical risk minimization and generally lead to non differentiable optimization problems in the form of a sum of a smooth term, measuring the quality of the fit, and a non-smooth term, penalizing complex solutions. Although it has considerable advantages, such a way of including prior information, unfortunately introduces many numerical difficulties both for solving the underlying optimization problem and to calibrate the level of regularization. Solving these issues has been at the heart of this thesis. A recently introduced technique, called "Screening Rules", proposes to ignore some variables during the optimization process by benefiting from the expected sparsity of the solutions. These elimination rules are said to be safe when the procedure guarantees to not reject any variable wrongly. In this work, we propose a unified framework for identifying important structures in these convex optimization problems and we introduce the "Gap Safe Screening Rules". They allows to obtain significant gains in computational time thanks to the dimensionality reduction induced by this method. In addition, they can be easily inserted into iterative algorithms and apply to a large number of problems.To find a good compromise between minimizing risk and introducing a learning bias, (exact) homotopy continuation algorithms offer the possibility of tracking the curve of the solutions as a function of the regularization parameters. However, they exhibit numerical instabilities due to several matrix inversions and are often expensive in large dimension. Another weakness is that a worst-case analysis shows that they have exact complexities that are exponential in the dimension of the model parameter. Allowing approximated solutions makes possible to circumvent the aforementioned drawbacks by approximating the curve of the solutions. In this thesis, we revisit the approximation techniques of the regularization paths given a predefined tolerance and we propose an in-depth analysis of their complexity w.r.t. the regularity of the loss functions involved. Hence, we propose optimal algorithms as well as various strategies for exploring the parameters space. We also provide calibration method (for the regularization parameter) that enjoys globalconvergence guarantees for the minimization of the empirical risk on the validation data.Among sparse regularization methods, the Lasso is one of the most celebrated and studied. Its statistical theory suggests choosing the level of regularization according to the amount of variance in the observations, which is difficult to use in practice because the variance of the model is oftenan unknown quantity. In such case, it is possible to jointly optimize the regression parameter as well as the level of noise. These concomitant estimates, appeared in the literature under the names of Scaled Lasso or Square-Root Lasso, and provide theoretical results as sharp as that of theLasso while being independent of the actual noise level of the observations. Although presenting important advances, these methods are numerically unstable and the currently available algorithms are expensive in computation time. We illustrate these difficulties and we propose modifications based on smoothing techniques to increase stability of these estimators as well as to introduce a faster algorithm

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Venezia, Joseph. "VARIABLE RESOLUTION & DIMENSIONAL MAPPING FOR 3D MODEL OPTIMIZATION." Master's thesis, University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2273.

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Three-dimensional computer models, especially geospatial architectural data sets, can be visualized in the same way humans experience the world, providing a realistic, interactive experience. Scene familiarization, architectural analysis, scientific visualization, and many other applications would benefit from finely detailed, high resolution, 3D models. Automated methods to construct these 3D models traditionally has produced data sets that are often low fidelity or inaccurate; otherwise, they are initially highly detailed, but are very labor and time intensive to construct. Such data sets are often not practical for common real-time usage and are not easily updated. This thesis proposes Variable Resolution & Dimensional Mapping (VRDM), a methodology that has been developed to address some of the limitations of existing approaches to model construction from images. Key components of VRDM are texture palettes, which enable variable and ultra-high resolution images to be easily composited; texture features, which allow image features to integrated as image or geometry, and have the ability to modify the geometric model structure to add detail. These components support a primary VRDM objective of facilitating model refinement with additional data. This can be done until the desired fidelity is achieved as practical limits of infinite detail are approached. Texture Levels, the third component, enable real-time interaction with a very detailed model, along with the flexibility of having alternate pixel data for a given area of the model and this is achieved through extra dimensions. Together these techniques have been used to construct models that can contain GBs of imagery data.
M.S.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering MSCpE

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Robinson, Theresa Dawn 1978. "Surrogate-based optimization using multifidelity models with variable parameterization." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39666.

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Анотація:

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 131-138).
Engineers are increasingly using high-fidelity models for numerical optimization. However, the computational cost of these models, combined with the large number of objective function and constraint evaluations required by optimization methods, can render such optimization computationally intractable. Surrogate-based optimization (SBO) - optimization using a lower-fidelity model most of the time, with occasional recourse to the high-fidelity model - is a proven method for reducing the cost of optimization. One branch of SBO uses lower-fidelity physics models of the same system as the surrogate. Until now however, surrogates using a different set of design variables from that of the high-fidelity model have not been available to use in a provably convergent numerical optimization. New methods are herein developed and demonstrated to reduce the computational cost of numerical optimization of variableparameterization problems, that is, problems for which the low-fidelity model uses a different set of design variables from the high-fidelity model.
(cont.) Four methods are presented to perform mapping between variable-parameterization spaces, the last three of which are new: space mapping, corrected space mapping, a mapping based on proper orthogonal decomposition (POD), and a hybrid between POD mapping and space mapping. These mapping methods provide links between different models of the same system and have further applications beyond formal optimization strategies. On an unconstrained airfoil design problem, it achieved up to 40% savings in highfidelity function evaluations. On a constrained wing design problem it achieved 76% time savings, and on a bat flight design problem, it achieved 45% time savings. On a large-scale practical aerospace application, such time savings could represent weeks.
by Theresa D. Robinson.
Ph.D.

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Golovidov, Oleg. "Variable-Complexity Approximations for Aerodynamic Parameters in Hsct Optimization." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36789.

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A procedure for generating and using polynomial approximations to the range or to the cruise drag components in terms of 29 design variables for the High Speed Civil Transport (HSCT) configuration and performance design is presented. Response surface model methodology is used to fit quadratic polynomials to data gathered from a series of numerical analyses of different HSCT designs. Several techniques are employed to minimize the number of required analyses and to maintain accuracy. Approximate analysis techniques are used to find regions of the design space where reasonable HSCT designs could occur and response surface models are built using higher fidelity analysis results of the designs in this "reasonable" region. Regression analysis and analysis of variance are then used to reduce the number of polynomial terms in the response surface model functions. Optimizations of the HSCT are then carried out both with and without the response surface models, and the effect of the use of the response surface models is discussed. Results of the work showed that considerable reduction of the amount of numerical noise in optimization is achieved with response surface models and the convergence rate was slightly improved. Careful attention was required to keep the accuracy of the models at an acceptable level. NOTE: (07/2012) An updated copy of this ETD was added after there were patron reports of problems with the file.
Master of Science

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Thomas, George L. "Biogeography-Based Optimization of a Variable Camshaft Timing System." Cleveland State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=csu1419775790.

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Lott, Eric M. "A Design and Optimization Methodology for Multi-Variable Systems." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440274138.

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Fouquet, Yoann. "Optimization methods for network design under variable link capacities." Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2233/document.

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Cette thèse porte sur l’optimisation des stratégies de reroutage dans les réseaux de télécommunications. Plus précisément, l’objectif est de proposer ou d’adapter des mécanismes permettant de router le trafic du réseau après une panne partielle, c’est-à-dire, après une baisse de la bande passante d’un ou plusieurs liens du réseau, tout en minimisant le coût de dimensionnement du réseau. Nos contributions principales sont la proposition de deux stratégies de protection/routage nommée Flow Thinning et Elastic Flow Rerouting. La thèse est organisée en trois parties. Dans la première partie, nous présentons la problématique de la thèse avant de passer en revue les stratégies de protection et reroutage de la littérature, leur modélisation et méthode de résolution. La deuxième partie présente en détails la première stratégie de protection appelée Flow-Thinning. Cette stratégie gère les pannes partielles en diminuant la bande passante de certain flots qui passent par le ou les arc(s) perturbés. Cela implique un surdimensionnement du routage nominal permettant d’assurer le trafic en cas de perturbations. La troisième et dernière partie concerne la deuxième stratégie de routage dénommée Elastic Flow Rerouting. Cette stratégie est un peu plus complexe que la première dans le sens où, en cas de panne, une distinction est faite entre les demandes perturbées ou non. Si une demande est perturbée, elle peu augmenter le trafic sur ces chemins. Si elle ne l’est pas, elle peut libérer de la bande passante sous la condition qu’elle ne devienne pas perturbée à son tour. Notons que ces deux stratégies sont assez difficiles du point de vue de leur complexité. Cette thèse a fait l’objet de divers travaux écrits : trois articles (acceptés ou en révision) dans des journaux (Fouquet et al. (2015b), Pióro et al. (2015), Shinko et al. (2015)), deux articles invités (Fouquet and Nace (2015), Fouquet et al. (2014c)) et huit articles dans des conférences internationales (Fouquet et al. (2015a; 2014d;a;b;e), Pióro et al. (2013b;a), Shinko et al. (2013)). Notons que Pióro et al. (2013b) a reçu le "Best Paper Award" de la conférence RNDM 2013. Pour finir, notons que cette thèse a été réalisée au laboratoire Heudiasyc de l’Université de Technologie de Compiègne (UTC). Elle a été financée par le Ministère de l’enseignement et de la recherche français3 avec le soutien du labex MS2T4 de l’UTC
This thesis summaries the work we have done in optimization of resilient communication networks. More specifically, the main goal is to propose appropriated recovery mechanisms for managing the demand traffic in a network under partial failures, i.e. when some part of the network (one or some links and/or nodes) is operational with reduced capacity. The main criterion in deciding the efficiency of the proposed recovery scheme is the dimensioning cost of the network while keeping the management cost at reasonable levels. Our main contribution is the design of two restoration strategies named Flow Thinning and Elastic Flow Rerouting. This document is organized in three main parts. In the first part, we present the problematic of the thesis. It includes an introduction on the protection and rerouting state-of-art strategies, together with their mathematical models and resolution methods. The second part presents in depth the first protection strategy named Flow Thinning. This strategy manages partial failures by decreasing appropriately the bandwidth on some flows routed through one of perturbed links. This implies overdimensionning of the network in the nominal state to ensure demand traffic in all failure states. The third and last part deals with the second rerouting strategy called Elastic Flow Rerouting. This strategy is a bit more complex than the first one because, in a failure state, we need to distinguish demands which are disturbed and the one which are not. If a demand is disturbed, it can increase the traffic on some of its paths. If it is not disturbed, it can release bandwidth on paths at the condition it remains non-disturbed. All this allows for further reducing the dimensioning cost but at a higher cost in terms of recovery process management. Note that the dimensioning problems for each strategy are shown to be NP-hard in their general form. The work of the thesis has been published in: three journal articles (Fouquet et al. (2015b), Pióro et al. (2015), Shinko et al. (2015)), two invited articles (Fouquet and Nace (2015), Fouquet et al. (2014c)) and height articles in international conferences (Fouquet et al. (2015a; 2014d;a;b;e), Pióro et al. (2013b;a), Shinko et al. (2013)). Note that Pióro et al. (2013b) has been rewarded by a "Best Paper Award" from the RNDM conference. To conclude, note that this thesis was realized in the Heudiasyc laboratory, from the Université de Technologie de Compiègne (UTC). It was financed by the French Ministry of Higher Education and Research1 with the support of the Labex MS2T2 of the UTC

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Socha, Krzysztof. "Ant colony optimization for continuous and mixed-variable domains." Doctoral thesis, Universite Libre de Bruxelles, 2008. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210533.

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In this work, we present a way to extend Ant Colony Optimization (ACO), so that it can be applied to both continuous and mixed-variable optimization problems. We demonstrate, first, how ACO may be extended to continuous domains. We describe the algorithm proposed, discuss the different design decisions made, and we position it among other metaheuristics.

Following this, we present the results of numerous simulations and testing. We compare the results obtained by the proposed algorithm on typical benchmark problems with those obtained by other methods used for tackling continuous optimization problems in the literature. Finally, we investigate how our algorithm performs on a real-world problem coming from the medical field—we use our algorithm for training neural network used for pattern classification in disease recognition.

Following an extensive analysis of the performance of ACO extended to continuous domains, we present how it may be further adapted to handle both continuous and discrete variables simultaneously. We thus introduce the first native mixed-variable version of an ACO algorithm. Then, we analyze and compare the performance of both continuous and mixed-variable

ACO algorithms on different benchmark problems from the literature. Through the research performed, we gain some insight into the relationship between the formulation of mixed-variable problems, and the best methods to tackle them. Furthermore, we demonstrate that the performance of ACO on various real-world mixed-variable optimization problems coming from the mechanical engineering field is comparable to the state of the art.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished

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Книги з теми "Optimization variable"

Eichfelder, Gabriele. Variable Ordering Structures in Vector Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54283-1.

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Freeman, T. L. Parallel projected variable metric algorithms for unconstrained optimization. Hampton, Va: Institute for Computer Applications in Science and Engineering, 1989.

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Freeman, T. L. Parallel projected variable metric algorithms for unconstrained optimization. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1990.

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1943-, Sano Akira, and Atherton Derek P, eds. State variable methods in automatic control. Chichester [England]: Wiley, 1988.

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Zaslavski, Alexander J., Simeon Reich, and B. Sh Mordukhovich. Nonlinear analysis and optimization: Workshop on Nonlinear Analysis and Optimization, June 12, 2014, Technion--Israel Institute of Technology, Haifa, Israel : IMU/AMS Special Session on Nonlinear Analysis and Optimization, June 16-19, 2014, Bar-Ilan University and Tel-Aviv Universities, Ramat-Gan and Tel-Aviv, Israel. Providence, Rhode Island: American Mathematical Society, 2016.

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Burgdorf, Sabine, Igor Klep, and Janez Povh. Optimization of Polynomials in Non-Commuting Variables. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33338-0.

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Joydeep, Dutta, ed. Optimality conditions in convex optimization: A finite-dimensional view. Boca Raton: CRC Press, 2012.

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Agranovskiĭ, M. L. (Mark Lʹvovich), ed. Complex analysis and dynamical systems IV: May 18-22, 2009, Nahariya, Israel. Providence, R.I: American Mathematical Society, 2011.

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Shou-Yang, Wang, and Lai Kin Keung, eds. Generalized convexity and vector optimization. Berlin: Springer, 2009.

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Eldar, Yonina C., and Daniel P. Palomar. Convex optimization in signal processing and communications. Cambridge, UK: Cambridge University Press, 2010.

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Частини книг з теми "Optimization variable"

Eichfelder, Gabriele. "Variable Ordering Structures." In Vector Optimization, 1–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54283-1_1.

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Bartholomew–Biggs, Michael. "One-variable Optimization." In Nonlinear Optimization with Engineering Applications, 1–22. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-78723-7_2.

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Kalyagin, V. A., A. P. Koldanov, P. A. Koldanov, and P. M. Pardalos. "Random Variable Networks." In SpringerBriefs in Optimization, 7–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60293-2_2.

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Wang, Shuming, and Junzo Watada. "Fuzzy Random Variable." In Fuzzy Stochastic Optimization, 9–54. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-9560-5_2.

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Eichfelder, Gabriele. "Variable Ordering Structures in Vector Optimization." In Vector Optimization, 95–126. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21114-0_4.

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Bartholomew–Biggs, Michael. "n-Variable Unconstrained Optimization." In Nonlinear Optimization with Engineering Applications, 1–12. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-78723-7_4.

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Bhunia, Asoke Kumar, Laxminarayan Sahoo, and Ali Akbar Shaikh. "Bounded Variable Technique." In Springer Optimization and Its Applications, 127–36. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9967-2_6.

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Du, Ding-Zhu, Panos M. Pardalos, and Weili Wu. "Variable Metric Methods." In Nonconvex Optimization and Its Applications, 133–50. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-5795-8_9.

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Öchsner, Andreas, and Resam Makvandi. "Unconstrained Functions of One Variable." In Numerical Engineering Optimization, 15–45. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43388-8_2.

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Öchsner, Andreas, and Resam Makvandi. "Constrained Functions of One Variable." In Numerical Engineering Optimization, 47–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43388-8_3.

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Тези доповідей конференцій з теми "Optimization variable"

Daxberger, Erik, Anastasia Makarova, Matteo Turchetta, and Andreas Krause. "Mixed-Variable Bayesian Optimization." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/365.

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The optimization of expensive to evaluate, black-box, mixed-variable functions, i.e. functions that have continuous and discrete inputs, is a difficult and yet pervasive problem in science and engineering. In Bayesian optimization (BO), special cases of this problem that consider fully continuous or fully discrete domains have been widely studied. However, few methods exist for mixed-variable domains and none of them can handle discrete constraints that arise in many real-world applications. In this paper, we introduce MiVaBo, a novel BO algorithm for the efficient optimization of mixed-variable functions combining a linear surrogate model based on expressive feature representations with Thompson sampling. We propose an effective method to optimize its acquisition function, a challenging problem for mixed-variable domains, making MiVaBo the first BO method that can handle complex constraints over the discrete variables. Moreover, we provide the first convergence analysis of a mixed-variable BO algorithm. Finally, we show that MiVaBo is significantly more sample efficient than state-of-the-art mixed-variable BO algorithms on several hyperparameter tuning tasks, including the tuning of deep generative models.

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Robinson, Theresa, Karen Willcox, Michael Eldred, and Robert Haimes. "Multifidelity Optimization for Variable-Complexity Design." In 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-7114.

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Teng, Chin-Pun, and Jorge Angeles. "Structural Optimization Under Variable Loading Conditions." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/dac-14299.

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Abstract The problem of structural optimization under variable loading conditions is discussed here. We assume a linearly-elastic structure subject to one single load of constant magnitude but of arbitrary orientation. Moreover, we assume that the structure is discretized by finite elements. The result of this study is an optimality criterion: the eigenvalues of the stiffness matrix of the optimum structure observe a minimum variance. In other words, the optimum structure under variable load must have a stiffness matrix that is as close as possible to isotropy. Furthermore, in order to implement the foregoing criterion, we introduce a novel method of automatic mesh generation, that is based on the concept of penalty functions of nonlinear programming. Finally, we illustrate these concepts by means of the optimization of a triangular lamina of given side lengths, with an elliptical hole centered at its centroid, of a prescribed area, the design parameters being the semiaxes of the ellipse and the orientation of these axes with respect to the edges of the lamina.

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Pedrycz, Adam, Fangyan Dong, and Kaoru Hirota. "Variable-geometry clustering and its optimization." In 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2009. http://dx.doi.org/10.1109/icsmc.2009.5346949.

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Rehbach, Frederik, Lorenzo Gentile, and Thomas Bartz-Beielstein. "Variable reduction for surrogate-based optimization." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377930.3390195.

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Arora, Jasbir S. "Methods for Discrete Variable Structural Optimization." In Structures Congress 2000. Reston, VA: American Society of Civil Engineers, 2000. http://dx.doi.org/10.1061/40492(2000)23.

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Pilloni, Alessandro, Alessandro Pisano, Mauro Franceschelli, and Elio Usai. "A discontinuous algorithm for distributed convex optimization." In 2016 14th International Workshop on Variable Structure Systems (VSS). IEEE, 2016. http://dx.doi.org/10.1109/vss.2016.7506884.

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TISCHLER, V., and V. VENKAYYA. "Ply-orientation as a variable in multidisciplinary optimization." In 4th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-4793.

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Ghate, Devendra, Amitay Isaacs, K. Sudhakar, Prasanna Mujumdar, and Anil Marathe. "3D-Duct Design Using Variable Fidelity Method." In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4427.

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Liu, Ping, and Guodong Li. "Slack variable-based control variable parameterization method for constrained engineering optimization." In 2017 Chinese Automation Congress (CAC). IEEE, 2017. http://dx.doi.org/10.1109/cac.2017.8244002.

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Temple, Brian Allen. Introduction to Mixed Variable Optimization (MVO). Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1507314.

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Smith, Jonathan C. Variable Expansion Techniques for Decomposable Optimization Problems. Fort Belvoir, VA: Defense Technical Information Center, March 2011. http://dx.doi.org/10.21236/ada563794.

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Chechetka, Anton, and Katia Sycara. A Decentralized Variable Ordering Method for Distributed Constraint Optimization. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada598539.

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Abramson, Mark A., Charles Audet, Jr Dennis, and J. E. Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada445031.

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Romero, Vicente JosÔe, and Chun-Hung Chen. Development of a new adaptive ordinal approach to continuous-variable probabilistic optimization. Office of Scientific and Technical Information (OSTI), November 2006. http://dx.doi.org/10.2172/896553.

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Abramson, Mark A. Mixed Variable Optimization of a Load-Bearing Thermal Insulation System Using a Filter Pattern Search Algorithm. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada451457.

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Stepanović, Milica, Dragoljub Bajić, and Dušan Polomši. Multicriteria Analysis and Optimization of Groundwater Control Systems with Variable Values of Criterion over Predefined Time Points. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, August 2021. http://dx.doi.org/10.7546/crabs.2021.08.09.

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Walker, H. A., Jal D. Desai, and Ammar Qusaibaty. Life-Cycle Cost and Optimization of PV Systems Based on Power Duration Curve with Variable Performance Ratio and Availability. Office of Scientific and Technical Information (OSTI), February 2020. http://dx.doi.org/10.2172/1601963.

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Ryan, Lisa B. Advancing Forward-Looking Metrics: A Linear Program Optimization and Robust Variable Selection for Change in Stock Levels as a Result of Recurring MICAP Parts. Fort Belvoir, VA: Defense Technical Information Center, June 2013. http://dx.doi.org/10.21236/ada581072.

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Rahman, Shahedur, Rodrigo Salgado, Monica Prezzi, and Peter J. Becker. Improvement of Stiffness and Strength of Backfill Soils Through Optimization of Compaction Procedures and Specifications. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317134.

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Vibration compaction is the most effective way of compacting coarse-grained materials. The effects of vibration frequency and amplitude on the compaction density of different backfill materials commonly used by INDOT (No. 4 natural sand, No. 24 stone sand, and No. 5, No. 8, No. 43 aggregates) were studied in this research. The test materials were characterized based on the particle sizes and morphology parameters using digital image analysis technique. Small-scale laboratory compaction tests were carried out with variable frequency and amplitude of vibrations using vibratory hammer and vibratory table. The results show an increase in density with the increase in amplitude and frequency of vibration. However, the increase in density with the increase in amplitude of vibration is more pronounced for the coarse aggregates than for the sands. A comparison of the maximum dry densities of different test materials shows that the dry densities obtained after compaction using the vibratory hammer are greater than those obtained after compaction using the vibratory table when both tools were used at the highest amplitude and frequency of vibration available. Large-scale vibratory roller compaction tests were performed in the field for No. 30 backfill soil to observe the effect of vibration frequency and number of passes on the compaction density. Accelerometer sensors were attached to the roller drum (Caterpillar, model CS56B) to measure the frequency of vibration for the two different vibration settings available to the roller. For this roller and soil tested, the results show that the higher vibration setting is more effective. Direct shear tests and direct interface shear tests were performed to study the impact of particle characteristics of the coarse-grained backfill materials on interface shear resistance. The more angular the particles, the greater the shear resistance measured in the direct shear tests. A unique relationship was found between the normalized surface roughness and the ratio of critical-state interface friction angle between sand-gravel mixture with steel to the internal critical-state friction angle of the sand-gravel mixture.

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