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Literatura académica sobre el tema "Global Optimization, Clustering Methods"

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Publicado: 10 de marzo de 2023

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Artículos de revistas sobre el tema "Global Optimization, Clustering Methods"

Törn, A. A. "Clustering Methods in Global Optimization". IFAC Proceedings Volumes 19, n.º 5 (mayo de 1986): 247–52. http://dx.doi.org/10.1016/s1474-6670(17)59803-1.

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Rinnooy Kan, A. H. G. y G. T. Timmer. "Stochastic global optimization methods part I: Clustering methods". Mathematical Programming 39, n.º 1 (septiembre de 1987): 27–56. http://dx.doi.org/10.1007/bf02592070.

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Bagattini, Francesco, Fabio Schoen y Luca Tigli. "Clustering methods for large scale geometrical global optimization". Optimization Methods and Software 34, n.º 5 (1 de marzo de 2019): 1099–122. http://dx.doi.org/10.1080/10556788.2019.1582651.

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Schoen, Fabio y Luca Tigli. "Efficient large scale global optimization through clustering-based population methods". Computers & Operations Research 127 (marzo de 2021): 105165. http://dx.doi.org/10.1016/j.cor.2020.105165.

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Aldosari, Fahd, Laith Abualigah y Khaled H. Almotairi. "A Normal Distributed Dwarf Mongoose Optimization Algorithm for Global Optimization and Data Clustering Applications". Symmetry 14, n.º 5 (17 de mayo de 2022): 1021. http://dx.doi.org/10.3390/sym14051021.

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As data volumes have increased and difficulty in tackling vast and complicated problems has emerged, the need for innovative and intelligent solutions to handle these difficulties has become essential. Data clustering is a data mining approach that clusters a huge amount of data into a number of clusters; in other words, it finds symmetric and asymmetric objects. In this study, we developed a novel strategy that uses intelligent optimization algorithms to tackle a group of issues requiring sophisticated methods to solve. Three primary components are employed in the suggested technique, named GNDDMOA: Dwarf Mongoose Optimization Algorithm (DMOA), Generalized Normal Distribution (GNF), and Opposition-based Learning Strategy (OBL). These parts are used to organize the executions of the proposed method during the optimization process based on a unique transition mechanism to address the critical limitations of the original methods. Twenty-three test functions and eight data clustering tasks were utilized to evaluate the performance of the suggested method. The suggested method’s findings were compared to other well-known approaches. In all of the benchmark functions examined, the suggested GNDDMOA approach produced the best results. It performed very well in data clustering applications showing promising performance.

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Fong, Simon, Suash Deb, Xin-She Yang y Yan Zhuang. "Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms". Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/564829.

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Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

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Gerasina, O., V. Korniienko, O. Gusev, K. Sosnin y S. Matsiuk. "Detecting fishing URLs using fuzzy clustering algorithms with global optimization". System technologies 2, n.º 139 (30 de marzo de 2022): 53–67. http://dx.doi.org/10.34185/1562-9945-2-139-2022-06.

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An algorithm for detecting phishing URLs (classifier) using fuzzy clustering is proposed, which includes choosing the type of intelligent classifier and justifying its parameters using global optimization methods. The following were studied as intellectual classifiers: subtractive clustering and fuzzy clustering of C-means. To find (adjust) the optimal (for a specific task) parameters of intelligent classifiers, the use of global optimization methods is justified, including genetic algorithm, direct random search, annealing simulation method, multicriteria optimization and threshold acceptance method. As a criterion of global optimization, a combined criterion was used, which includes the definition of the regularity criterion calculated on the test sample and the bias (minimum shift) criterion based on the analysis of solutions. By modeling in the Matlab environment with the help of standard and developed programs, the evaluated efficiency of using the proposed algorithm is evaluated on the example of experimental data – a set of 150 phishing and 150 secure URLs. The set of experimental data included information about the domain name registrar, the lifetime of the domain, the geolocation of the hosting server, the presence of a secure connection with a valid certificate. By simulation it is established that the fuzzy classifier with the subtractive clustering algorithm and using the Sugeno structure and 6 clusters meets the minimum of the combined criterion. All phishing URLs that were mistakenly classified as secure were found to have a secure con-nection with a valid certificate. Thus, further research should be aimed at exploring additional informative attributes (features) that could allow better separation of phishing and secure URLs.

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Duan, Yiqiang, Haoliang Yuan, Chun Sing Lai y Loi Lei Lai. "Fusing Local and Global Information for One-Step Multi-View Subspace Clustering". Applied Sciences 12, n.º 10 (18 de mayo de 2022): 5094. http://dx.doi.org/10.3390/app12105094.

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Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects. (1) The subspace representation yielded by the self-expression reconstruction model ignores the local structure information of the data. (2) The construction of subspace representation and clustering are used as two individual procedures, which ignores their interactions. To address these problems, we propose a novel multi-view subspace clustering method fusing local and global information for one-step multi-view clustering. Our contribution lies in three aspects. First, we merge the graph learning into the self-expression model to explore the local structure information for constructing the specific subspace representations of different views. Second, we consider the multi-view information fusion by integrating these specific subspace representations into one common subspace representation. Third, we combine the subspace representation learning, multi-view information fusion, and clustering into a joint optimization model to realize the one-step clustering. We also develop an effective optimization algorithm to solve the proposed method. Comprehensive experimental results on nine popular multi-view data sets confirm the effectiveness and superiority of the proposed method by comparing it with many state-of-the-art multi-view clustering methods.

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Wen, Guoqiu, Yonghua Zhu, Linjun Chen, Mengmeng Zhan y Yangcai Xie. "Global and Local Structure Preservation for Nonlinear High-dimensional Spectral Clustering". Computer Journal 64, n.º 7 (14 de mayo de 2021): 993–1004. http://dx.doi.org/10.1093/comjnl/bxab020.

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Abstract Spectral clustering is widely applied in real applications, as it utilizes a graph matrix to consider the similarity relationship of subjects. The quality of graph structure is usually important to the robustness of the clustering task. However, existing spectral clustering methods consider either the local structure or the global structure, which can not provide comprehensive information for clustering tasks. Moreover, previous clustering methods only consider the simple similarity relationship, which may not output the optimal clustering performance. To solve these problems, we propose a novel clustering method considering both the local structure and the global structure for conducting nonlinear clustering. Specifically, our proposed method simultaneously considers (i) preserving the local structure and the global structure of subjects to provide comprehensive information for clustering tasks, (ii) exploring the nonlinear similarity relationship to capture the complex and inherent correlation of subjects and (iii) embedding dimensionality reduction techniques and a low-rank constraint in the framework of adaptive graph learning to reduce clustering biases. These constraints are considered in a unified optimization framework to result in one-step clustering. Experimental results on real data sets demonstrate that our method achieved competitive clustering performance in comparison with state-of-the-art clustering methods.

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Wang, Hong Chun, Feng Wen Wen y Feng Song. "Clustering Algorithm Based on Improved Particle Swarm Optimization". Advanced Materials Research 765-767 (septiembre de 2013): 486–88. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.486.

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K-means algorithm has therefore become one of the methods widely used in cluster analysis. But the classification results of K-means algorithm depend on the initial cluster centers choice. We present a new neighborhood for PSO methods called the area of influence (AOI) and consider the combination of K-means has strong capacity of local searching and PSO has power global search ability. The improved PSO, i.e., improves the K-means local searching capacity, accelerates the convergence rate, and prevents the premature convergence effectively.

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Tesis sobre el tema "Global Optimization, Clustering Methods"

SOUZA, Ellen Polliana Ramos. "Swarm optimization clustering methods for opinion mining". Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/25227.

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Opinion Mining (OM), also known as sentiment analysis, is the field of study that analyzes people’s sentiments, evaluations, attitudes, and emotions about different entities expressed in textual input. This is accomplished through the classification of an opinion into categories, such as positive, negative, or neutral. Supervised machine learning (ML) and lexicon-based are the most frequent approaches for OM. However, these approaches require considerable effort for preparing training data and to build the opinion lexicon, respectively. In order to address the drawbacks of these approaches, this Thesis proposes the use of unsupervised clustering approach for the OM task which is able to produce accurate results for several domains without manually labeled data for the training step or tools which are language dependent. Three swarm algorithms based on Particle Swarm Optimization (PSO) and Cuckoo Search (CS) are proposed: the DPSOMUT which is based on a discrete PSO binary version, the IDPSOMUT that is based on an Improved Self-Adaptive PSO algorithm with detection function, and the IDPSOMUT/CS that is a hybrid version of IDPSOMUT and CS. Several experiments were conducted with different corpora types, domains, text language, class balancing, fitness function, and pre-processing techniques. The effectiveness of the clustering algorithms was evaluated with external measures such as accuracy, precision, recall, and F-score. From the statistical analysis, it was possible to observe that the swarm-based algorithms, especially the PSO ones, were able to find better solutions than conventional grouping techniques, such as K-means and Agglomerative. The PSO-based algorithms achieved better accuracy using a word bigram pre-processing and the Global Silhouette as fitness function. The OBCC corpus is also another contribution of this Thesis and contains a gold collection with 2,940 tweets in Brazilian Portuguese with opinions of consumers about products and services.
A mineração de opinião, também conhecida como análise de sentimento, é um campo de estudo que analisa os sentimentos, opiniões, atitudes e emoções das pessoas sobre diferentes entidades, expressos de forma textual. Tal análise é obtida através da classificação das opiniões em categorias, tais como positiva, negativa ou neutra. As abordagens de aprendizado supervisionado e baseadas em léxico são mais comumente utilizadas na mineração de opinião. No entanto, tais abordagens requerem um esforço considerável para preparação da base de dados de treinamento e para construção dos léxicos de opinião, respectivamente. A fim de minimizar as desvantagens das abordagens apresentadas, esta Tese propõe o uso de uma abordagem de agrupamento não supervisionada para a tarefa de mineração de opinião, a qual é capaz de produzir resultados precisos para diversos domínios sem a necessidade de dados rotulados manualmente para a etapa treinamento e sem fazer uso de ferramentas dependentes de língua. Três algoritmos de agrupamento não-supervisionado baseados em otimização de partícula de enxame (Particle Swarm Optimization - PSO) são propostos: o DPSOMUT, que é baseado em versão discreta do PSO; o IDPSOMUT, que é baseado em uma versão melhorada e autoadaptativa do PSO com função de detecção; e o IDPSOMUT/CS, que é uma versão híbrida do IDPSOMUT com o Cuckoo Search (CS). Diversos experimentos foram conduzidos com diferentes tipos de corpora, domínios, idioma do texto, balanceamento de classes, função de otimização e técnicas de pré-processamento. A eficácia dos algoritmos de agrupamento foi avaliada com medidas externas como acurácia, precisão, revocação e f-medida. A partir das análises estatísticas, os algortimos baseados em inteligência coletiva, especialmente os baseado em PSO, obtiveram melhores resultados que os algortimos que utilizam técnicas convencionais de agrupamento como o K-means e o Agglomerative. Os algoritmos propostos obtiveram um melhor desempenho utilizando o pré-processamento baseado em n-grama e utilizando a Global Silhouete como função de otimização. O corpus OBCC é também uma contribuição desta Tese e contem uma coleção dourada com 2.940 tweets com opiniões de consumidores sobre produtos e serviços em Português brasileiro.

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Ren, Zhiwei. "Portfolio Construction using Clustering Methods". Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-042605-092010/.

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Schütze, Oliver. "Set oriented methods for global optimization". [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=976566982.

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Gutmann, H. M. "Radial basis function methods for global optimization". Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599804.

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In many real world optimization problems it is essential or desirable to determine the global minimum of the objective function. The subject of this dissertation is a new class of methods that tackle such problems. In particular, we have in mind problems where function evaluations are expensive and no additional information is available. The methods employ radial basis functions that have been proved to be useful for interpolation problems. Examples include thin plate splines and multiquadrics. Specifically, in each iteration, radial basis function interpolation is used to define a utility function. A maximizer of this function is chosen to be the next point where the objective function is evaluated. Relations to similar optimization methods are established, and a general framework is presented that combines these methods and our methods. A large part of the dissertation is devoted to the convergence theory. We show that convergence can be achieved for most types of basis functions without further assumptions on the objective function. For other types, however, a similar results could not be obtained. This is due to the properties of the so-called native space that is associated with a basis function. In particular, it is of interest whether this space contains sufficiently smooth functions with compact support. In order to address this question, we present two approaches. First, we establish a characterization of the native space in terms of generalized Fourier transforms. For many types, for example thin plate splines, this helps to derive conditions on the smoothness of a function that guarantee that it is in the native space. For other types, for example multiquadrics, however, we show that the native space does not contain a nonzero function with compact support. The second approach we present gives slightly weaker results, but it employs some new theory using interpolation on an infinite regular grid.

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Stepanenko, Svetlana. "Global Optimization Methods based on Tabu Search". Doctoral thesis, kostenfrei, 2008. http://www.opus-bayern.de/uni-wuerzburg/volltexte/2008/3060/.

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Pettersson, Tobias. "Global optimization methods for estimation of descriptive models". Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11781.

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Using mathematical models with the purpose to understand and store knowlegde about a system is not a new field in science with early contributions dated back to, e.g., Kepler’s laws of planetary motion.

The aim is to obtain such a comprehensive predictive and quantitative knowledge about a phenomenon so that mathematical expressions or models can be used to forecast every relevant detail about that phenomenon. Such models can be used for reducing pollutions from car engines; prevent aviation incidents; or developing new therapeutic drugs. Models used to forecast, or predict, the behavior of a system are refered to predictive models. For such, the estimation problem aims to find one model and is well known and can be handeled by using standard methods for global nonlinear optimization.

Descriptive models are used to obtain and store quantitative knowledge of system. Estimation of descriptive models has not been much described by the literature so far; instead the methods used for predictive models have beed applied. Rather than finding one particular model, the parameter estimation for descriptive models aims to find every model that contains descriptive information about the system. Thus, the parameter estimation problem for descriptive models can not be stated as a standard optimization problem.

The main objective for this thesis is to propose methods for estimation of descriptive models. This is made by using methods for nonlinear optimization including both new and existing theory.

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McMeen, John Norman Jr. "Ranking Methods for Global Optimization of Molecular Structures". Digital Commons @ East Tennessee State University, 2014. https://dc.etsu.edu/etd/2447.

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This work presents heuristics for searching large sets of molecular structures for low-energy, stable systems. The goal is to find the globally optimal structures in less time or by consuming less computational resources. The strategies intermittently evaluate and rank structures during molecular dynamics optimizations, culling possible weaker solutions from evaluations earlier, leaving better solutions to receive more simulation time. Although some imprecision was introduced from not allowing all structures to fully optimize before ranking, the strategies identify metrics that can be used to make these searches more efficient when computational resources are limited.

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Akteke, Basak. "Derivative Free Optimization Methods: Application In Stirrer Configuration And Data Clustering". Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606591/index.pdf.

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Recent developments show that derivative free methods are highly demanded by researches for solving optimization problems in various practical contexts. Although well-known optimization methods that employ derivative information can be very effcient, a derivative free method will be more effcient in cases where the objective function is nondifferentiable, the derivative information is not available or is not reliable. Derivative Free Optimization (DFO) is developed for solving small dimensional problems (less than 100 variables) in which the computation of an objective function is relatively expensive and the derivatives of the objective function are not available. Problems of this nature more and more arise in modern physical, chemical and econometric measurements and in engineering applications, where computer simulation is employed for the evaluation of the objective functions. In this thesis, we give an example of the implementation of DFO in an approach for optimizing stirrer configurations, including a parametrized grid generator, a flow solver, and DFO. A derivative free method, i.e., DFO is preferred because the gradient of the objective function with respect to the stirrer&rsquo
s design variables is not directly available. This nonlinear objective function is obtained from the flow field by the flow solver. We present and interpret numerical results of this implementation. Moreover, a contribution is given to a survey and a distinction of DFO research directions, to an analysis and discussion of these. We also state a derivative free algorithm used within a clustering algorithm in combination with non-smooth optimization techniques to reveal the effectiveness of derivative free methods in computations. This algorithm is applied on some data sets from various sources of public life and medicine. We compare various methods, their practical backgrounds, and conclude with a summary and outlook. This work may serve as a preparation of possible future research.

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Stolpe, Mathias. "On Models and Methods for Global Optimization of Structural Topology". Doctoral thesis, KTH, Mathematics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3478.

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This thesis consists of an introduction and sevenindependent, but closely related, papers which all deal withproblems in structural optimization. In particular, we considermodels and methods for global optimization of problems intopology design of discrete and continuum structures.

In the first four papers of the thesis the nonconvex problemof minimizing the weight of a truss structure subject to stressconstraints is considered. First itis shown that a certainsubclass of these problems can equivalently be cast as linearprograms and thus efficiently solved to global optimality.Thereafter, the behavior of a certain well-known perturbationtechnique is studied. It is concluded that, in practice, thistechnique can not guarantee that a global minimizer is found.Finally, a convergent continuous branch-and-bound method forglobal optimization of minimum weight problems with stress,displacement, and local buckling constraints is developed.Using this method, several problems taken from the literatureare solved with a proof of global optimality for the firsttime.

The last three papers of the thesis deal with topologyoptimization of discretized continuum structures. Theseproblems are usually modeled as mixed or pure nonlinear 0-1programs. First, the behavior of certain often usedpenalization methods for minimum compliance problems isstudied. It is concluded that these methods may fail to producea zero-one solution to the considered problem. To remedy this,a material interpolation scheme based on a rational functionsuch that compli- ance becomes a concave function is proposed.Finally, it is shown that a broad range of nonlinear 0-1topology optimization problems, including stress- anddisplacement-constrained minimum weight problems, canequivalently be modeled as linear mixed 0-1 programs. Thisresult implies that any of the standard methods available forgeneral linear integer programming can now be used on topologyoptimization problems.

Keywords:topology optimization, global optimization,stress constraints, linear programming, mixed integerprogramming, branch-and-bound.

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Robertson, Blair Lennon. "Direct Search Methods for Nonsmooth Problems using Global Optimization Techniques". Thesis, University of Canterbury. Mathematics and Statistics, 2010. http://hdl.handle.net/10092/5060.

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This thesis considers the practical problem of constrained and unconstrained local optimization. This subject has been well studied when the objective function f is assumed to smooth. However, nonsmooth problems occur naturally and frequently in practice. Here f is assumed to be nonsmooth or discontinuous without forcing smoothness assumptions near, or at, a potential solution. Various methods have been presented by others to solve nonsmooth optimization problems, however only partial convergence results are possible for these methods. In this thesis, an optimization method which use a series of local and localized global optimization phases is proposed. The local phase searches for a local minimum and gives the methods numerical performance on parts of f which are smooth. The localized global phase exhaustively searches for points of descent in a neighborhood of cluster points. It is the localized global phase which provides strong theoretical convergence results on nonsmooth problems. Algorithms are presented for solving bound constrained, unconstrained and constrained nonlinear nonsmooth optimization problems. These algorithms use direct search methods in the local phase as they can be applied directly to nonsmooth problems because gradients are not explicitly required. The localized global optimization phase uses a new partitioning random search algorithm to direct random sampling into promising subsets of ℝⁿ. The partition is formed using classification and regression trees (CART) from statistical pattern recognition. The CART partition defines desirable subsets where f is relatively low, based on previous sampling, from which further samples are drawn directly. For each algorithm, convergence to an essential local minimizer of f is demonstrated under mild conditions. That is, a point x* for which the set of all feasible points with lower f values has Lebesgue measure zero for all sufficiently small neighborhoods of x*. Stopping rules are derived for each algorithm giving practical convergence to estimates of essential local minimizers. Numerical results are presented on a range of nonsmooth test problems for 2 to 10 dimensions showing the methods are effective in practice.

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Libros sobre el tema "Global Optimization, Clustering Methods"

M, Pardalos Panos y Rosen J. B, eds. Computational methods in global optimization. Basel: J.C. Baltzer, 1990.

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1948-, Stoffa Paul L., ed. Global optimization methods in geophysical inversion. Amsterdam: Elsevier, 1995.

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1941-, Rokne J., ed. New computer methods for global optimization. Chichester, West Sussex, England: Horwood, 1988.

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Floudas, Christodoulos A. Deterministic global optimization: Theory, methods, and applications. Dordrecht: Kluwer Academic Publishers, 2000.

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Floudas, Christodoulos A. Deterministic Global Optimization: Theory, Methods and Applications. Boston, MA: Springer US, 2000.

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Bruhn, Andrés, Thomas Pock y Xue-Cheng Tai, eds. Efficient Algorithms for Global Optimization Methods in Computer Vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54774-4.

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Global methods in optimal control theory. New York: M. Dekker, 1996.

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Akulenko, Leonid D. Problems and Methods of Optimal Control. Dordrecht: Springer Netherlands, 1994.

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A, Floudas Christodoulos y Pardalos P. M. 1954-, eds. State of the art in global optimization: Computational methods and applications. Dordrecht: Kluwer Academic Publishers, 1996.

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Bobylev, N. A. Geometrical Methods in Variational Problems. Dordrecht: Springer Netherlands, 1999.

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Capítulos de libros sobre el tema "Global Optimization, Clustering Methods"

Liu, Kai, Duane Detwiler y Andres Tovar. "Metamodel-Based Global Optimization of Vehicle Structures for Crashworthiness Supported by Clustering Methods". En Advances in Structural and Multidisciplinary Optimization, 1545–57. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67988-4_116.

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Horst, Reiner y Hoang Tuy. "Cutting Methods". En Global Optimization, 175–218. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02598-7_5.

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Horst, Reiner y Hoang Tuy. "Cutting Methods". En Global Optimization, 175–218. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-02947-3_5.

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Horst, Reiner y Hoang Tuy. "Cutting Methods". En Global Optimization, 181–224. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03199-5_5.

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Horst, Reiner y Hoang Tuy. "Successive Approximation Methods". En Global Optimization, 219–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02598-7_6.

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Horst, Reiner y Hoang Tuy. "Successive Partition Methods". En Global Optimization, 286–370. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02598-7_7.

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Horst, Reiner y Hoang Tuy. "Successive Approximation Methods". En Global Optimization, 219–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-02947-3_6.

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Horst, Reiner y Hoang Tuy. "Successive Partition Methods". En Global Optimization, 286–370. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-02947-3_7.

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Horst, Reiner y Hoang Tuy. "Successive Approximation Methods". En Global Optimization, 225–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03199-5_6.

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Horst, Reiner y Hoang Tuy. "Successive Partition Methods". En Global Optimization, 295–380. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03199-5_7.

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Actas de conferencias sobre el tema "Global Optimization, Clustering Methods"

Bifulco, Ida y Stefano Cirillo. "Discovery Multiple Data Structures in Big Data through Global Optimization and Clustering Methods". En 2018 22nd International Conference Information Visualisation (IV). IEEE, 2018. http://dx.doi.org/10.1109/iv.2018.00030.

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Xiaosong, GUO, Teng Long, Di Wu, Zhu Wang y Li Liu. "RBF Metamodel Assisted Global Optimization Method Using Particle Swarm Evolution and Fuzzy Clustering for Sequential Sampling". En 15th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-2305.

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Liu, Kai, Andrés Tovar, Emily Nutwell y Duane Detwiler. "Towards Nonlinear Multimaterial Topology Optimization Using Unsupervised Machine Learning and Metamodel-Based Optimization". En ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46534.

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This work introduces a multimaterial density-based topology optimization method suitable for nonlinear structural problems. The proposed method consists of three stages: continuous density distribution, clustering, and metamodel-based optimization. The initial continuous density distribution is generated following a synthesis strategy without penalization, e.g., the hybrid cellular automaton (HCA) method. In the clustering stage, unsupervised machine learning (e.g., K-means clustering) is used to optimally classify the continuous density distribution into a finite number of clusters based on their similarity. Finally, a metamodel (e.g., Kriging interpolation) is generated and iteratively updated following a global optimization algorithm (e.g., genetic algorithms) to ultimately converge to an optimal material distribution. The proposed methodology is demonstrated with the design of multimaterial stiff (minimum compliance) structures, compliant mechanisms, and a thin-walled S-rail structure for crashworthiness.

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Hoeltzel, D. A. y W. H. Chieng. "Statistical Machine Learning for the Cognitive Selection of Nonlinear Programming Algorithms in Engineering Design Optimization". En ASME 1987 Design Technology Conferences. American Society of Mechanical Engineers, 1987. http://dx.doi.org/10.1115/detc1987-0009.

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Abstract In order to overcome the problem of lack of generality in nonlinear programming (NLP) test problem formulation and to introduce the concept of cognitive NLP method switching, statistical machine learning has been applied to a sample data base of nonlinear programming problems. Reasonable conclusions have been drawn about an optimization problem type and a corresponding sequence of NLP solution algorithms, using statistical pattern recognition applied to local (vs. global) design information. A program, referred to as OPTDEX-OLDM, with the capability of learning from statistical pattern recognition is discussed. The statistical aspects and algorithmic optimization of the nonlinear programming problem are emphasized in this discussion. A clustering process has been performed on attributes assigned to the NLP problem sample data base, and an example which describes this statistical clustering process is discussed.

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Gimbutienė, Gražina y Antanas Žilinskas. "Clustering-based statistical global optimization". En NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2016): Proceedings of the 2nd International Conference “Numerical Computations: Theory and Algorithms”. Author(s), 2016. http://dx.doi.org/10.1063/1.4965342.

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Haas, Kyle. "Prediction of Structural Reliability Through an Alternative Variability-Based Methodology". En ASME 2019 Verification and Validation Symposium. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/vvs2019-5150.

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Abstract Astonishing increases in computational power have fueled the engineering community’s drive to seek increasingly optimized solutions to structural design problems. Although structural optimization can be critical to achieve a practical and cost-effective design, optimization often comes at a cost to reliability. The competing goals of optimization and reliability amplify the importance of validation, verification, and uncertainty quantification efforts to achieve sufficiently reliable performance. Evaluation of a structural system’s reliability presents a practical challenge to designers given the potentially large number of permutations of conditions that may exist over the full operational lifecycle. A direct prediction of performance and the prediction’s corresponding likelihood is often achieved via deterministic analysis techniques in conjunction with Monte Carlo analysis. Such methods can be overly cumbersome and often do not provide a complete picture of the system’s global reliability due to the practical limits of performing the necessary number of analyses. At the point of incipient structural failure, the structural response becomes highly variable and sensitive to minor perturbations in conditions. This characteristic provides a powerful opportunity to determine the critical failure conditions and to assess the resulting structural reliability through an alternative, but more expedient variability-based method. Non-hierarchical clustering, proximity analysis, and the use of stability indicators are combined to identify the loci of conditions that lead to a rapid evolution of the structural response toward a failure condition. The utility of the proposed method is demonstrated through its application to a simple nonlinear dynamic single-degree-of-freedom structural model. A feedforward artificial neural network is trained from numerically-generated data to provide an expedient means of assessing the system’s behavior under perturbed conditions. In addition to the L2-norm, a new stability indicator is proposed called the “Instability Index”, which is a function of both the L2-norm and the calculated proximity to adjacent loci of conditions with differing structural response. The Instability Index provides a rapidly achieved quantitative measure of the relative stability of the system for all possible loci of conditions.

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Bifulco, Ida, Carmine Fedullo, Francesco Napolitano, Giancarlo Raiconi y Roberto Tagliaferri. "Global optimization, Meta Clustering and consensus clustering for class prediction". En 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178789.

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Johnson, Ryan K. y Ferat Sahin. "Particle swarm optimization methods for data clustering". En 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control (ICSCCW). IEEE, 2009. http://dx.doi.org/10.1109/icsccw.2009.5379452.

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L. Stoffa, P., M. K. Sen, C. Varela y R. K. Chunduru. "Geophysical applications of global optimization methods". En 56th EAEG Meeting. European Association of Geoscientists & Engineers, 1994. http://dx.doi.org/10.3997/2214-4609.201410084.

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Sen, M. K., P. L. Stoffa y R. K. Chunduru. "Geophysical Application of Global Optimization Methods". En 3rd International Congress of the Brazilian Geophysical Society. European Association of Geoscientists & Engineers, 1993. http://dx.doi.org/10.3997/2214-4609-pdb.324.91.

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Informes sobre el tema "Global Optimization, Clustering Methods"

Dunlavy, Daniel M. y Dianne P. O'Leary. Homotopy optimization methods for global optimization. Office of Scientific and Technical Information (OSTI), diciembre de 2005. http://dx.doi.org/10.2172/876373.

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Glover, Fred. Probabilistic Methods for Global Optimization in Continuous Variables. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 1995. http://dx.doi.org/10.21236/ada304297.

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Glover, Fred. Probabilistic Methods or Global Optimization in Continuous Variables. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 1995. http://dx.doi.org/10.21236/ada311405.

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Dennis, John E., Shou-Bai B. Li y Richard A. Tapia. A Unified Approach to Global Convergence of Trust-Region Methods for Nonsmooth Optimization. Fort Belvoir, VA: Defense Technical Information Center, julio de 1993. http://dx.doi.org/10.21236/ada455260.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak y Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, julio de 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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McElwain, Terry, Eugene Pipano, Guy Palmer, Varda Shkap, Stephen Hines y Douglas Jasmer. Protection of Cattle Against Babesiosis: Immunization with Recombinant DNA Derived Apical Complex Antigens of Babesia bovis. United States Department of Agriculture, junio de 1995. http://dx.doi.org/10.32747/1995.7612835.bard.

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Bovine babesiosis caused by Babesia bovis continues to be a significant deterrent to global livestock production. Current control methods have both biological and technical drawbacks that have stimulated research on improved methods of vaccination. This BARD project has focused on characterization of candidate Babesia bovis vaccine antigens located in the apical complex, a unique group of subcellular organelles - including rhoptries, micronemes, and spherical bodies - involved in the invation of erythrocytes. Spherical bodies and rhoptries were partially purified and their contents characterized using monoclonal antibodies. Existing and newly developed monoclonal antibodies bound to antigens in the spherical body, rhoptry, merozoite membrane, and infected erythrocyte membrane. In an initial immunization study using biologically cloned strains, it was demonstrated that strain-common epitopes are important for inducing immune protection against heterologous challenge. Rhoptry-associated antigen 1 (RAP-1) had been demonstrated previously to induce partial immune protection, fulfilled criteria of broad interstrain B and T cell epitope conservation, and thus was further characterized. The RAP-1 gene family consists of at least two gene copies, is homologous to the RAP-1 gene family in B. bigemina, and contains significant sequence similarity to other erythroparasitic protozoan candidate vaccine antigens, including the apical membrane antigen of Plasmodium falciparum. A new RAP-1 monoclonal antibody was developed that inhibits merozoite growth in vitro, demonstrating the presence of a RAP-1 neutralization sensitive domain. Based on these observations, cattle were immunized with Mo7 (Mexico) strain recombinant RAP-1 representing one of the two gene copies. All cattle responded with variable levels of serum antibodies inhibitory to heterologous Israel strain merozoite growth in vitro, and RAP-1 specific T lymphocytes that proliferated when stimulated with either homologous or heterologous native parasite antigen. Minimal protection from clinical disease was present after virulent Israel (heterologous) strain B. bovis challenge. In total, the results support the continued development of RAP-1 as a vaccine antigen, but indicate that additional information about the native structure and function of both RAP-1 gene copies, including the relationship of conserved and polymorphic sequences to B and T cell lepitopes relevant for protection, is necessary for optimization of RAP-1 as a vaccine component.

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