Academic literature on the topic 'Multi-Objective Estimation'
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Journal articles on the topic "Multi-Objective Estimation"
R, Rakesh. "Nonlinear Analysis for Parameter Estimation by Multi Objective Single Variable Inverse Analysis." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 529–38. http://dx.doi.org/10.5373/jardcs/v12sp7/20202136.
Full textMitra, Amitava, and Jayprakash G. Patankar. "A multi-objective model for warranty estimation." European Journal of Operational Research 45, no. 2-3 (April 1990): 347–55. http://dx.doi.org/10.1016/0377-2217(90)90198-k.
Full textAlberdi, Iciar, Roberto Vallejo, Juan G. Álvarez-González, Sonia Condés, Eduardo González-Ferreiro, Silvia Guerrero, Laura Hernández, et al. "The multi-objective Spanish National Forest Inventory." Forest Systems 26, no. 2 (August 3, 2017): e04S. http://dx.doi.org/10.5424/fs/2017262-10577.
Full textSilva, Cláudia M., and Evaristo C. Biscaia. "Multi-Objective parameter estimation problems: an improved strategy." Inverse Problems in Science and Engineering 12, no. 3 (June 2004): 297–316. http://dx.doi.org/10.1080/10682760310001598715.
Full textBarroso, Márcio F. S., Ricardo H. C. Takahashi, and Luis A. Aguirre. "Multi-objective parameter estimation via minimal correlation criterion." Journal of Process Control 17, no. 4 (April 2007): 321–32. http://dx.doi.org/10.1016/j.jprocont.2006.10.005.
Full textvan Brummelen, E. H., S. Zhuk, and G. J. van Zwieten. "Worst-case multi-objective error estimation and adaptivity." Computer Methods in Applied Mechanics and Engineering 313 (January 2017): 723–43. http://dx.doi.org/10.1016/j.cma.2016.10.007.
Full textPapaioannidis, Christos, and Ioannis Pitas. "3D Object Pose Estimation Using Multi-Objective Quaternion Learning." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 8 (August 2020): 2683–93. http://dx.doi.org/10.1109/tcsvt.2019.2929600.
Full textEmami Niri, Mohammad, and David E. Lumley. "Estimation of subsurface geomodels by multi-objective stochastic optimization." Journal of Applied Geophysics 129 (June 2016): 187–99. http://dx.doi.org/10.1016/j.jappgeo.2016.03.031.
Full textMartins, Marcella S. R., Myriam R. B. S. Delgado, Ricardo Lüders, Roberto Santana, Richard A. Gonçalves, and Carolina P. de Almeida. "Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem." Journal of Heuristics 24, no. 1 (September 2, 2017): 25–47. http://dx.doi.org/10.1007/s10732-017-9356-7.
Full textDörgő, Gyula, and János Abonyi. "Group Contribution Method-based Multi-objective Evolutionary Molecular Design." Hungarian Journal of Industry and Chemistry 44, no. 1 (October 1, 2016): 39–50. http://dx.doi.org/10.1515/hjic-2016-0005.
Full textDissertations / Theses on the topic "Multi-Objective Estimation"
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.
Full textNowadays, 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.
Full textDepartment 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.
Full textMonteagudo, 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.
Full textPetrlík, 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.
Full textXu, Weili. "An Energy and Cost Performance Optimization Platform for Commercial Building System Design." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/956.
Full textHartikka, Alice, and 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.
Full textSkolpadungket, 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.
Full textThenon, 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.
Full textPerforming 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.
Full textA 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
Book chapters on the topic "Multi-Objective Estimation"
Peng, Yiming, and Hisao Ishibuchi. "Niching Diversity Estimation for Multi-modal Multi-objective Optimization." In Lecture Notes in Computer Science, 323–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72062-9_26.
Full textPunnapala, Sameer, Francisco M. Vargas, and Ali Elkamel. "Parameter Estimation in Phase Equilibria Calculations Using Multi-Objective Evolutionary Algorithms." In Multi-Objective Optimization in Chemical Engineering, 247–65. Oxford, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118341704.ch9.
Full textPeriasamy, Karthik Raja, and S. Lakshminarayanan. "Estimation of Crystal Size Distribution: Image Thresholding Based on Multi-Objective Optimization." In Multi-Objective Optimization in Chemical Engineering, 399–422. Oxford, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118341704.ch14.
Full textRao, G. Sivanageswara, Ch V. Phani Krishna, and K. Rajasekhara Rao. "Multi Objective Particle Swarm Optimization for Software Cost Estimation." In ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I, 125–32. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03107-1_15.
Full textWhigham, Peter A., and Caitlin Owen. "Multi-objective Optimisation, Software Effort Estimation and Linear Models." In Lecture Notes in Computer Science, 263–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13563-2_23.
Full textPalar, Pramudita Satria, and Koji Shimoyama. "Multiple Metamodels for Robustness Estimation in Multi-objective Robust Optimization." In Lecture Notes in Computer Science, 469–83. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54157-0_32.
Full textHao, Xinchang, Lu Sun, and Mitsuo Gen. "Multi-objective Job Shop Rescheduling with Estimation of Distribution Algorithm." In Proceedings of the Eleventh International Conference on Management Science and Engineering Management, 35–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59280-0_3.
Full textArya, Anoop, Yogendra Kumar, Manisha Dubey, and Radharaman Gupta. "Multi-Objective Fault Section Estimation in Distribution Systems Using Elitist NSGA." In Advances in Intelligent Systems and Computing, 211–19. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-1041-2_18.
Full textMonzón, Hugo, Hernán Aguirre, Sébastien Verel, Arnaud Liefooghe, Bilel Derbel, and Kiyoshi Tanaka. "Dynamic Compartmental Models for Large Multi-objective Landscapes and Performance Estimation." In Evolutionary Computation in Combinatorial Optimization, 99–113. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43680-3_7.
Full textVander Biest, Alexis, Alienor Richard, Dragomir Milojevic, and Frederic Robert. "A Multi-objective and Hierarchical Exploration Tool for SoC Performance Estimation." In Lecture Notes in Computer Science, 85–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-70550-5_10.
Full textConference papers on the topic "Multi-Objective Estimation"
Sarro, Federica, Alessio Petrozziello, and Mark Harman. "Multi-objective software effort estimation." In ICSE '16: 38th International Conference on Software Engineering. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2884781.2884830.
Full textZangl, H., and G. Steiner. "Optimal design of multi-objective multi-sensor systems." In Proceedings of the 2005 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement. IEEE, 2005. http://dx.doi.org/10.1109/amuem.2005.1594616.
Full textAscher, Dominik, and Georg Hackenberg. "Early estimation of multi-objective traffic flow." In 2014 International Conference on Connected Vehicles and Expo (ICCVE). IEEE, 2014. http://dx.doi.org/10.1109/iccve.2014.7297511.
Full textTakagi, Tomoaki, Keiki Takadama, and Hiroyuki Sato. "Supervised Multi-Objective Optimization Algorithm Using Estimation." In 2022 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2022. http://dx.doi.org/10.1109/cec55065.2022.9870375.
Full textMensah, Solomon, Jacky Keung, Kwabena Ebo Bennin, and Michael Franklin Bosu. "Multi-Objective Optimization for Software Testing Effort Estimation." In The 28th International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc. and Knowledge Systems Institute Graduate School, 2016. http://dx.doi.org/10.18293/seke2016-163.
Full textMendoza-Gonzalez, Alfredo, Eunice Ponce-de-Leon, and Elva Diaz-Diaz. "Classification scheme of multi-objective Estimation of Distribution Algorithms." In 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557941.
Full textDi Fina, Dario, Svebor Karaman, Andrew D. Bagdanov, and Alberto Del Bimbo. "MORF: Multi-Objective Random Forests for face characteristic estimation." In 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2015. http://dx.doi.org/10.1109/avss.2015.7301793.
Full textZhong, Xiaoping, and Weiji Li. "A Decision-Tree-Based Multi-objective Estimation of Distribution Algorithm." In 2007 International Conference on Computational Intelligence and Security (CIS 2007). IEEE, 2007. http://dx.doi.org/10.1109/cis.2007.136.
Full textNayeem, Muhammad Ali, Md Shamsuzzoha Bayzid, Sakshar Chakravarty, Mohammad Saifur Rahman, and M. Sohel Rahman. "A Multi-objective Metaheuristic Approach for Accurate Species Tree Estimation." In 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2020. http://dx.doi.org/10.1109/bibe50027.2020.00021.
Full textAgrawal, Gautam, Sumeet Parashar, and Christina Bloebaum. "Estimation of Multi-Objective Pareto Frontier using Hyperspace Diagonal Counting." In 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-6959.
Full textReports on the topic "Multi-Objective Estimation"
Waddell, Lucas, John Gauthier, Matthew Hoffman, Denise Padilla, Stephen Henry, Alexander Dessanti, and Adam Pierson. Estimating the Adequacy of a Multi-Objective Optimization . Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1833178.
Full textEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Full textGur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600047.bard.
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