Thèses sur le sujet « Multi-Objective Estimation »
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Martins, Marcella Scoczynski Ribeiro. « A hybrid multi-objective bayesian estimation of distribution algorithm ». Universidade Tecnológica Federal do Paraná, 2017. http://repositorio.utfpr.edu.br/jspui/handle/1/2806.
Texte intégralNowadays, a number of metaheuristics have been developed for dealing with multiobjective optimization problems. Estimation of distribution algorithms (EDAs) are a special class of metaheuristics that explore the decision variable space to construct probabilistic models from promising solutions. The probabilistic model used in EDA captures statistics of decision variables and their interdependencies with the optimization problem. Moreover, the aggregation of local search methods can notably improve the results of multi-objective evolutionary algorithms. Therefore, these hybrid approaches have been jointly applied to multi-objective problems. In this work, a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), which is based on a Bayesian network, is proposed to multi and many objective scenarios by modeling the joint probability of decision variables, objectives, and configuration parameters of an embedded local search (LS). We tested different versions of HMOBEDA using instances of the multi-objective knapsack problem for two to five and eight objectives. HMOBEDA is also compared with five cutting edge evolutionary algorithms (including a modified version of NSGA-III, for combinatorial optimization) applied to the same knapsack instances, as well to a set of MNK-landscape instances for two, three, five and eight objectives. An analysis of the resulting Bayesian network structures and parameters has also been carried to evaluate the approximated Pareto front from a probabilistic point of view, and also to evaluate how the interactions among variables, objectives and local search parameters are captured by the Bayesian networks. Results show that HMOBEDA outperforms the other approaches. It not only provides the best values for hypervolume, capacity and inverted generational distance indicators in most of the experiments, but it also presents a high diversity solution set close to the estimated Pareto front.
Morcos, Karim M. « Genetic network parameter estimation using single and multi-objective particle swarm optimization ». Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/9207.
Texte intégralDepartment of Electrical and Computer Engineering
Sanjoy Das
Stephen M. Welch
Multi-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optimal solutions due to the huge size of the search space. At the same time, the solution set produced by most of the currently used evolutionary algorithms lacks diversity. The present study investigates a new optimization method to solve multi-objective problems based on the widely used swarm-intelligence approach, Particle Swarm Optimization (PSO). Compared to other approaches, the proposed algorithm converges relatively fast while maintaining a diverse set of solutions. The investigated algorithm, Partially Informed Fuzzy-Dominance (PIFD) based PSO uses a dynamic network topology and fuzzy dominance to guide the swarm of dominated solutions. The proposed algorithm in this study has been tested on four benchmark problems and other real-world applications to ensure proper functionality and assess overall performance. The multi-objective gene regulatory network (GRN) problem entails the minimization of the coefficient of variation of modified photothermal units (MPTUs) across multiple sites along with the total sum of similarity background between ecotypes. The results throughout the current research study show that the investigated algorithm attains outstanding performance regarding optimization aspects, and exhibits rapid convergence and diversity.
Monteagudo, Maykel Cruz. « Multi-Objective Optimization Based on Desirability Estimation of Several Interrelated Responses (MOOp-DESIRe) : A Computer-Aided Methodology for Multi-Criteria Drug Discovery ». Doctoral thesis, Faculdade de Farmácia da Universidade do Porto, 2009. http://hdl.handle.net/10216/63799.
Texte intégralMonteagudo, Maykel Cruz. « Multi-Objective Optimization Based on Desirability Estimation of Several Interrelated Responses (MOOp-DESIRe) : A Computer-Aided Methodology for Multi-Criteria Drug Discovery ». Tese, Faculdade de Farmácia da Universidade do Porto, 2009. http://hdl.handle.net/10216/63799.
Texte intégralPetrlík, Jiří. « Multikriteriální genetické algoritmy v predikci dopravy ». Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-412573.
Texte intégralXu, Weili. « An Energy and Cost Performance Optimization Platform for Commercial Building System Design ». Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/956.
Texte intégralHartikka, Alice, et Simon Nordenhög. « Emission Calculation Model for Vehicle Routing Planning : Estimation of emissions from heavy transports and optimization with carbon dioxide equivalents for a route planning software ». Thesis, Linköpings universitet, Energisystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178065.
Texte intégralSkolpadungket, Prisadarng. « Portfolio management using computational intelligence approaches : forecasting and optimising the stock returns and stock volatilities with fuzzy logic, neural network and evolutionary algorithms ». Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6306.
Texte intégralThenon, Arthur. « Utilisation de méta-modèles multi-fidélité pour l'optimisation de la production des réservoirs ». Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066100/document.
Texte intégralPerforming flow simulations on numerical models representative of oil deposits is usually a time consuming task in reservoir engineering. The substitution of a meta-model, a mathematical approximation, for the flow simulator is thus a common practice to reduce the number of calls to the flow simulator. It permits to consider applications such as sensitivity analysis, history-matching, production estimation and optimization. This thesis is about the study of meta-models able to integrate simulations performed at different levels of accuracy, for instance on reservoir models with various grid resolutions. The goal is to speed up the building of a predictive meta-model by balancing few expensive but accurate simulations, with numerous cheap but approximated ones. Multi-fidelity meta-models, based on co-kriging, are thus compared to kriging meta-models for approximating different flow simulation outputs. To deal with vectorial outputs without building a meta-model for each component of the vector, the outputs can be split on a reduced basis using principal component analysis. Only a few meta-models are then needed to approximate the main coefficients in the new basis. An extension of this approach to the multi-fidelity context is proposed. In addition, it can provide an efficient meta-modelling of the objective function when used to approximate each production response involved in the objective function definition. The proposed methods are tested on two synthetic cases derived from the PUNQ-S3 and Brugge benchmark cases. Finally, sequential design algorithms are introduced to speed-up the meta-modeling process and exploit the multi-fidelity approach
Song, Yingying. « Amélioration de la résolution spatiale d’une image hyperspectrale par déconvolution et séparation-déconvolution conjointes ». Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0207/document.
Texte intégralA hyperspectral image is a 3D data cube in which every pixel provides local spectral information about a scene of interest across a large number of contiguous bands. The observed images may suffer from degradation due to the measuring device, resulting in a convolution or blurring of the images. Hyperspectral image deconvolution (HID) consists in removing the blurring to improve the spatial resolution of images at best. A Tikhonov-like HID criterion with non-negativity constraint is considered here. This method considers separable spatial and spectral regularization terms whose strength are controlled by two regularization parameters. First part of this thesis proposes the maximum curvature criterion MCC and the minimum distance criterion MDC to automatically estimate these regularization parameters by formulating the deconvolution problem as a multi-objective optimization problem. The second part of this thesis proposes the sliding block regularized (SBR-LMS) algorithm for the online deconvolution of hypserspectral images as provided by whiskbroom and pushbroom scanning systems. The proposed algorithm accounts for the convolution kernel non-causality and including non-quadratic regularization terms while maintaining a linear complexity compatible with real-time processing in industrial applications. The third part of this thesis proposes joint unmixing-deconvolution methods based on the Tikhonov criterion in both offline and online contexts. The non-negativity constraint is added to improve their performances
Guerra, Jonathan. « Optimisation multi-objectif sous incertitudes de phénomènes de thermique transitoire ». Thesis, Toulouse, ISAE, 2016. http://www.theses.fr/2016ESAE0024/document.
Texte intégralThis work aims at solving multi-objective optimization problems in the presence of uncertainties and costly numerical simulations. A validation is carried out on a transient thermal test case. First of all, we develop a multi-objective optimization algorithm based on kriging and requiring few calls to the objective functions. This approach is adapted to the distribution of the computations and favors the restitution of a regular approximation of the complete Pareto front. The optimization problem under uncertainties is then studied by considering the worst-case and probabilistic robustness measures. The superquantile integrates every event on which the output value is between the quantile and the worst case. However, it requires an important number of calls to the uncertain objective function to be accurately evaluated. Few methods give the possibility to approach the superquantile of the output distribution of costly functions. To this end, we have developed an estimator based on importance sampling and kriging. It enables to approach superquantiles with little error and using a limited number of samples. Moreover, the setting up of a coupling with the multi-objective algorithm allows to reuse some of those evaluations. In the last part, we build spatio-temporal surrogate models capable of predicting non-linear, dynamic and long-term in time phenomena by using few learning trajectories. The construction is based on recurrent neural networks and a construction facilitating the learning is proposed
Vigliassi, Marcos Paulo. « Algoritmo evolutivo multiobjetivo em tabelas e matriz H&Delta ; para projeto de sistemas de medição para estimação de estado ». Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-19052017-154501/.
Texte intégralMetering system planning for power system state estimation is a multi-objective, combinatorial optimization problem that may require the investigation of many possible solutions. As a consequence, meta-heuristics have been employed to solve the problem. However in the majority of them the multi-objective problem is converted in a mono-objective problem and those few considering a multi-objective formulation do not consider all the performance requirements that must be attended in order to obtain a Reliable Metering System (RMS) (system observability and absence of Critical Measurements, Critical Sets, Critical Remote Terminal Units and Critical Phasor Measurement Units). This thesis proposes a multi-objective formulation for the metering system planning problem in a wide way, that is, considering all the performance requirements that must be attended to obtain a RMS. This thesis also proposes the development and implementation, in computer, of a method to solve the metering system planning problem, considering the trade-off between the two conflicting objectives of the problem (minimizing cost while maximizing the performance requirements) making use of the concept of Pareto Frontier. The method allows, in only one execution, the project of four types of metering systems, from the analysis of non-dominated solutions. The method enable the design of new metering systems as well as the improvement of existing ones, considering the existence of only conventional SCADA measurements, or only synchronized phasor measurements or the existence of both types of measurements. The proposed method combines a multi-objective evolutionary algorithm based on subpopulation tables with the properties of the so-called HΔ matrix. The subpopulations tables adequately model several metering system performance requirements enabling a better exploration of the solution space. On the other hand, the properties of the HΔ matrix enable a local search that improves the evolutionary process and minimizes the computational effort. Simulations results with IEEE 6, 14, 30, 118 and 300-bus test systems and with a 61-bus system of Eletropaulo illustrate the efficiency of the proposed method. Some of the results of these simulations will be compared with those published in literature.
Ebadi, Nasim. « Estimating Costs of Reducing Environmental Emissions From a Dairy Farm : Multi-objective epsilon-constraint Optimization Versus Single Objective Constrained Optimization ». Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99304.
Texte intégralMaster of Science
Human activities often damage and deplete the environment. For instance, nutrient pollution into air and water, which mostly comes from agricultural and industrial activ- ities, results in water quality degradation. Thus, mitigating the detrimental impacts of human activities is an important step toward environmental sustainability. Reducing environmental impacts of nutrient pollution from agriculture is a complicated problem, which needs a comprehensive understanding of types of pollution and their reduction strategies. Reduction strategies need to be both feasible and financially viable. Con- sequently, practices must be carefully selected to allow farmers to maximize their net return while reducing pollution levels to reach a satisfactory level. Thus, this paper conducts a study to evaluate the trade-offs associated with farm net return and re- ducing the most important pollutants generated by agricultural activities. The results of this study show that reducing N and GHG emissions from a representative dairy farm is less costly than reducing P and ammonia emissions, respectively. In addition, reducing one pollutant may result in reduction of other pollutants. In general, for N and P emissions reduction land retirement and varying crop rotations are the most effective strategies. However, for reducing ammonia and GHG emissions focusing on cow diet changes involving less forage is the most effective strategy.
Pottier, Claire. « Combinaison multi-capteurs de données de couleur de l'eau : application en océanographie opérationnelle ». Phd thesis, Université Paul Sabatier - Toulouse III, 2006. http://tel.archives-ouvertes.fr/tel-00179729.
Texte intégralL'intérêt d'utiliser des données combinées a été montré à travers la mise en évidence des modes de variabilité dominants de la dynamique océanographique et biologique dans l'Océan Austral, en utilisant les données combinées SeaWiFS + MODIS/Aqua de la ceinture circumpolaire pour la période 2002-2006.
Silva, Tiago Vieira da. « Algoritmos evolutivos como estimadores de frequência e fase de sinais elétricos : métodos multiobjetivos e paralelização em FPGAs ». Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-14012014-105606/.
Texte intégralThis work proposes the development of Evolutionary Algorithms (EAs) for the estimation of the basic parameters from electrical signals (frequency, phase and amplitude) in real time. The proposed approach must be robust to noise and harmonics in signals distorted, for example, due to the presence of faults in the electrical network. EAs show advantages for dealing with these types of signals. On the other hand, these algorithms when implemented in software cant produce real-time responses in order to use their estimations as frequency relay or Phasor Measurement Unit. The approach developed on FPGA proposed in this work parallelizes in hardware the process of estimation, enabling analyses of electrical signals in real time. Furthermore, it is shown that multi-objective EAs can extract non-evident information from the three phases of the system and properly estimate parameters even when the phase estimates diverge from each other. This research proposes: the parallelization of an EA in hardware through its design on FPGA circuit optimized at level of basic logic operations and the modeling of the problem enabling multi-objective analyses of the signals from each phase in both independent and aggregate ways. Experimental results show the superiority of the proposed method compared to an estimator based on Fourier transform for determining frequency and phase
Lai, Wei-Cheng, et 賴韋誠. « Application of Rational Fraction Polynomials and Multi-objective Genetic Algorithm to Modal Parameter Estimation ». Thesis, 2017. http://ndltd.ncl.edu.tw/handle/7gn2ba.
Texte intégral國立臺灣大學
機械工程學研究所
105
Modal testing is essential for the identification of important dynamical parameters of a mechanical system. However, commercially available modal testing packages are expensive and nonflexible. This thesis aims to employ the popular and powerful package MATLAB as the environment to develop a modal testing program that can be customized to meet the user’s needs. The efficiency of a modal testing program highly depends on the curve fitting algorithm used. Two different curve fitting algorithms, the rational fraction polynomials (RFP) method and the multi-objective genetic algorithms, are adopted. The effectiveness of these two methods on parameter identification is compared. The RFP method is based on the fact that the frequency response function (FRF) of a linear time-invariant system is a rational function in frequency. The lease squares method is used to determine the coefficients of the numerator and denominator polynomials. The RFP method can be classified into two different types, called the local curve fitting and global curve fitting, according to whether the FRFs are processed sequentially or simultaneously. The non-dominated sorting genetic algorithm-II (NSGA-II) is used to realize the multi-objective optimization. This algorithm employs the non-dominated sorting and crowding distance to select elite individuals for the next generation. In this case, the genetic diversity is maintained, early convergence to a local extrema is avoided, and high computational efficiency is achieved. In this thesis, we develop programs based on RFP and NSGAII. Some benchmark tests, for example, modes with nodes, high damping ratios, and double roots, which may present difficulties for parameter identification are used to evaluate these two methods. Possible guidelines to improve these two methods are proposed.
DI, FINA DARIO. « Multi-Target Tracking and Facial Attribute Estimation in Smart Environments ». Doctoral thesis, 2016. http://hdl.handle.net/2158/1029030.
Texte intégralChang, Chih Yao, et 張智堯. « Estimating CO2 Emissions from the Perspective of Domestic Consumption in Taiwan with a Multi-objective Programming Model ». Thesis, 2007. http://ndltd.ncl.edu.tw/handle/54021221247405725312.
Texte intégral國立政治大學
經濟研究所
95
This paper aims at estimating the CO2 emissions of Taiwan from the perspective of domestic consumption side. Since the developed countries would achieve the emission reduction goal by transferring their emission-intensive industries form their lands to the developing countries, we would neglect the true CO2 emissions of nations if we only estimate their CO2 emissions from the perspective of domestic production side, therefore reduce the significance of the Kyoto Protocol, which aims at reducing emissions. On the contrary, If we estimate the CO2 emissions of nations through the consumption side, we can provide the incentives for emission reduction more effectively, prompting the development of the technology of emission reduction or inducing consumers to conserve the use of energy. Consequently, this paper first estimates the CO2 emissions of Taiwan from the perspective of domestic consumption side through an input-output model, then estimates the import and export emissions of industry sectors, finally it analyzes the policies for CO2 emission reduction by a multi-objective programming model and provides suggestions for the development of industries.