Relevant bibliographies by topics / Numerical optimisation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Numerical optimisation'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Numerical optimisation"

1

Diouf, Serigne, Mamadou M. Diop, and Alassane Sy. "NUMERICAL IMAGING USING NONLINEAR OPTIMISATION." International Journal of Numerical Methods and Applications 20, no. 1 (2021): 1–16. http://dx.doi.org/10.17654/nm020010001.

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Theodore, N. D., and K. A. Padmanabhan. "Numerical optimisation of superplastic deformation." Journal of Materials Science 25, no. 4 (1990): 2133–43. http://dx.doi.org/10.1007/bf01045779.

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Wang, Wenjun, and Hui Wang. "An improved diversity-guided particle swarm optimisation for numerical optimisation." International Journal of Computing Science and Mathematics 5, no. 1 (2014): 16. http://dx.doi.org/10.1504/ijcsm.2014.059381.

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Tovar, Andres, Nelson Arzola de la Peña, and Alexander Gómez Cassab. "Multidisciplinary design optimisation techniques." Ingeniería e Investigación 27, no. 1 (2007): 84–92. http://dx.doi.org/10.15446/ing.investig.v27n1.14785.

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Design optimisation of a multidisciplinary project in engineering involves the decomposition of a system into disciplines and the subsequent association of their contributions. This work was aimed at presenting the most common decomposition and association techniques currently used in multidisciplinary design optimisation (MDO). Amongst the decomposition techniques this work includes hierarchical and non-hierarchical approaches as well as the most popular numerical procedures. The association techniques include: one-level methods (e.g. all-at-once optimisation and simultaneous analysis and des
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Grundmann, Jens, Niels Schütze, and Franz Lennartz. "Sustainable management of a coupled groundwater–agriculture hydrosystem using multi-criteria simulation based optimisation." Water Science and Technology 67, no. 3 (2013): 689–98. http://dx.doi.org/10.2166/wst.2012.602.

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In this paper we present a new simulation-based integrated water management tool for sustainable water resources management in arid coastal environments. This tool delivers optimised groundwater withdrawal scenarios considering saltwater intrusion as a result of agricultural and municipal water abstraction. It also yields a substantially improved water use efficiency of irrigated agriculture. To allow for a robust and fast operation we unified process modelling with artificial intelligence tools and evolutionary optimisation techniques. The aquifer behaviour is represented using an artificial
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Giunta, A. A., V. Balabanov, D. Haim, et al. "Multidisciplinary optimisation of a supersonic transport using design of experiments theory and response surface modelling." Aeronautical Journal 101, no. 1008 (1997): 347–56. http://dx.doi.org/10.1017/s0001924000066045.

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AbstractThe presence of numerical noise in engineering design optimisation problems inhibits the use of many gradient-based optimisation methods. This numerical noise may result in the inaccurate calculation of gradients which in turn slows or prevents convergence during optimisation, or it may promote convergence to spurious local optima. The problems created by numerical noise are particularly acute in aircraft design applications where a single aerodynamic or structural analysis of a realistic aircraft configuration may require tens of CPU hours on a supercomputer. The computational expense
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Ransing, R. S., S. Savino, and R. W. Lewis. "Numerical optimisation of tilt casting process." International Journal of Cast Metals Research 18, no. 2 (2005): 109–18. http://dx.doi.org/10.1179/136404605225022901.

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van der Heijden, F., W. Apperloo, and L. J. Spreeuwers. "Numerical optimisation in spot detector design." Pattern Recognition Letters 18, no. 11-13 (1997): 1091–97. http://dx.doi.org/10.1016/s0167-8655(97)00086-x.

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Krenek, Thorsten, Christopher Bacher, Günther Raidl, and Thomas Lauer. "Numerical Optimisation of Electro Hybrid Powertrains." MTZ worldwide 76, no. 3 (2015): 46–52. http://dx.doi.org/10.1007/s38311-015-0007-9.

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Simić, Miloš. "Is the Bee Colony Optimisation algorithm suitable for continuous numerical optimisation?" International Journal of Metaheuristics 6, no. 4 (2017): 279. http://dx.doi.org/10.1504/ijmheur.2017.086978.

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Dissertations / Theses on the topic "Numerical optimisation"

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Routley, Paul Richard. "BiCMOS circuit optimisation." Thesis, University of Southampton, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242271.

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Penev, Kalin. "Adaptive search heuristics applied to numerical optimisation." Thesis, Southampton Solent University, 2004. http://ssudl.solent.ac.uk/598/.

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The objective of the research project involves investigation of evolutionary computational methods, in particular analysis of population-based search heuristics, and abstraction of core cognition, which may lead to the design of a novel adaptive search algorithm capable of high performance and reliability. The thesis proposes a novel adaptive heuristic method called Free Search (FS). Free Search can be classified as a population-based evolutionary computational method. It gradually changes a set of solutions until satisfaction of certain criteria. The algorithm operates with real-value numbers
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Tian, Na. "Novel optimisation methods for numerical inverse problems." Thesis, University of Greenwich, 2011. http://gala.gre.ac.uk/9099/.

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Inverse problems involve the determination of one or more unknown quantities usually appearing in the mathematical formulation of a physical problem. These unknown quantities may be boundary heat flux, various source terms, thermal and material properties, boundary shape and size. Solving inverse problems requires additional information through in-situ data measurements of the field variables of the physical problems. These problems are also ill-posed because the solution itself is sensitive to random errors in the measured input data. Regularisation techniques are usually used in order to dea
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Yang, Yong. "Efficient parallel genetic algorithms applied to numerical optimisation." Thesis, Southampton Solent University, 2008. http://ssudl.solent.ac.uk/631/.

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This research is concerned with the optimisation of multi-modal numerical problems using genetic algorithms (GAs). GAs use randomised operators operating over a population of candidate solutions to generate new points in the search space. As the scale and complexity of target applications increase, run time becomes a major inhibitor. Parallel genetic algorithms (PGAs) have therefore become an important area of research. Coarse-grained implementations are one of the most popular models and many researchers are concerned primarily with this area. The island model was the only one class of parall
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Joubert, N. J. D. "Numerical design optimisation for the Karoo Array Telescope." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/2727.

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Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2009.<br>Although mass minimisation is an important application within structural optimisation, other applications include: (1) concept generation, (2) concept evaluation, (3) design for structural feasibility and (4) data matching. These applications, except data matching, are discussed and illustrated on a prototype design of the Karoo Array Telescope (KAT) antenna. The KAT passed through the design process and a full scale prototype was built, but was found to be too expensive. A detailed finite elem
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Alqahtani, Mohammed Aeyed M. "Numerical Algorithms for Polynomial Optimisation Problems with Applications." Thesis, Curtin University, 2019. http://hdl.handle.net/20.500.11937/73582.

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In this thesis, we study tensor eigenvalue problems and polynomial optimization problems. In particular, we present a fast algorithm for computing the spectral radii of symmetric nonnegative tensors without requiring the partition of the tensors. We also propose some polynomial time approximation algorithms with new approximation bounds for nonnegative polynomial optimization problems over unit spheres. Furthermore, we develop an efficient and effective algorithm for the maximum clique problem.
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Rahbary, Asr M. A. "Computer assisted machine tool part-program optimisation." Thesis, Coventry University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.279418.

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Fraser-Andrews, G. "Numerical techniques for singular optimal trajectories." Thesis, University of Hertfordshire, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372080.

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The objectives of the subject-matter of this thesis were to appraise some methods of solving non-singular optimal control problems by their degree of success in tackling four chosen problems and then to try the most promising methods on chosen singular problems. In Part I of this thesis, the chosen problems are attempted by quasilinearisation, two versions of shooting, Miels's method, differential dynamic programming and two versions of parameterisation . Conclusions on the various methods are given. NOC shooting, developed by the Numerical Optimisation Centre of The Hatfield Polytechnic, and
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Jones, R. "Numerical optimisation techniques applied to problems in continuum mechanics." Thesis, University of Nottingham, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378760.

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Wang, Tao. "Numerical simulation and optimisation for shot peen forming processes." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620031.

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Books on the topic "Numerical optimisation"

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D, Meegan, and Sprevak D, eds. An introduction to unconstrained optimisation. A. Hilger, 1990.

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Treanţă, Savin. Variational analysis with applications in optimisation and control. Cambridge Scholars Publishing, 2019.

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Bamberger, Alain. Analyse, optimisation et filtrage numériques: Anaylse numérique de l'équation de la chaleur. Ecole polytechnique, Département de mathématiques appliquées, 1991.

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Pardalos, P. M. (Panos M.), 1954- author, ed. Numerical methods and optimization: An introduction. CRC Press,Taylor & Francis Group, 2014.

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1946-, Woźniakowski Henryk, and European Mathematical Society, eds. Essays on the complexity of continuous problems. European Mathematical Society, 2009.

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Ciarlet, Philippe G. Introduction à l'analyse numérique matricielle et à l'optimisation. Masson, 1985.

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1966-, Chen Yangquan, ed. Solving applied mathematical problems with MATLAB. Taylor & Francis, 2009.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures =: Evolution Programs. Springer Berlin Heidelberg, 1994.

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Michalewicz, Zbigniew. Algorytmy genetyczne + struktury danych = programy ewolucyjne. 3rd ed. Wydawnictwa Naukowo-Techniczne, 2003.

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International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (3rd 2000 Saarbrücken, Germany). Approximation algorithms for combinatorial optimization: Third international workshop, APPROX 2000, Saarbrucken, Germany, September 5-8, 2000 : proceedings. Springer, 2000.

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Book chapters on the topic "Numerical optimisation"

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Antia, H. M. "Optimisation." In Numerical Methods for Scientists and Engineers. Hindustan Book Agency, 2012. http://dx.doi.org/10.1007/978-93-86279-52-1_8.

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Woodford, C., and C. Phillips. "Optimisation." In Numerical Methods with Worked Examples: Matlab Edition. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-1366-6_8.

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Phan, Chi M. "One-Dimensional Optimisation." In Numerical Calculations for Process Engineering Using Excel VBA. CRC Press, 2023. http://dx.doi.org/10.1201/9781003364511-4.

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Penev, Kalin, and Anton Ruzhekov. "Adaptive Intelligence Applied to Numerical Optimisation." In Numerical Methods and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18466-6_33.

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Penev, Kalin. "Demanded Scale for Modern Numerical Optimisation." In Numerical Methods and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32412-3_24.

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Mezura-Montes, Efrén, and Jorge Isacc Flores-Mendoza. "Improved Particle Swarm Optimization in Constrained Numerical Search Spaces." In Nature-Inspired Algorithms for Optimisation. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00267-0_11.

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Lutz, Thorsten, Andreas Sommerer, and Siegfried Wagner. "Parallel Numerical Optimisation of Adaptive Transonic Airfoils." In IUTAM Symposium Transsonicum IV. Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0017-8_40.

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Tavares, Rodrigo Machado, and Edwin R. Galea. "Numerical Optimisation Techniques Applied to Evacuation Analysis." In Pedestrian and Evacuation Dynamics 2008. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04504-2_49.

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Dobrinkova, Nina, Momchil Panayotov, and Peter Boyvalenkov. "Optimisation Techniques in Wildfire Simulations. Test Case Kresna Fire August 2017." In Numerical Methods and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10692-8_8.

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Vassiliadis, Panos, and Spiros Skiadopoulos. "Modelling and Optimisation Issues for Multidimensional Databases." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design. Springer International Publishing, 2000. http://dx.doi.org/10.1007/3-540-45140-4_32.

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Conference papers on the topic "Numerical optimisation"

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Keep., Joshua A., and Ingo H. Jahn. "Numerical Simulation of Blast Nozzles." In Coatings+ 2020. SSPC, 2020. https://doi.org/10.5006/s2020-00027.

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Abstract In recent decades, lower density blasting media, such as garnet has become widely used. This has been in conjunction with existing blast nozzles, which are of a simple, un-optimised geometry. One means to design a more efficient blast nozzle is through the use of computational fluid dynamics (CFD) in conjunction with automated optimisation. This paper describes the development of a CFD model for simulating blast nozzles in the OpenFOAM environment, and quantifies the performance of two #6 blasting nozzle geometries operating with garnet. Simulation results indicate that the developed
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Banerjee, Siddhartha, and Jolly Xavier. "Optimisation of Nano Urchin Geometry for the Generation of Hot Electrons for Sensing: A Computational Study." In 2024 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD). IEEE, 2024. http://dx.doi.org/10.1109/nusod62083.2024.10723561.

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Willich, Viktor, Niklas Himker, and Axel Mertens. "Dynamic Position Estimator for a PMSM Using Regular Current Sampling and Numerical Optimisation." In 2024 27th International Conference on Electrical Machines and Systems (ICEMS). IEEE, 2024. https://doi.org/10.23919/icems60997.2024.10921281.

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Sharp, Andrew, Julian M. Niedermayer, Mobayode O. Akinsolu, Yuriy Vagapov, Shafiul Monir, and Anton Dianov. "Numerical Optimisation of Air-cooled Heat Sink Geometry to Improve Temperature Gradient of Power Semiconductor Modules." In 2024 XV International Symposium on Industrial Electronics and Applications (INDEL). IEEE, 2024. https://doi.org/10.1109/indel62640.2024.10772679.

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Elibol, Armagan, Nuno Gracia, and Rafael Garcia. "Reducing the optimisation effort in batch mosaicing." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756281.

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Groot, J. A. W. M., C. G. Giannopapa, and R. M. M. Mattheij. "Numerical Optimisation of Blowing Glass Parison Shapes." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77946.

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Industrial glass blowing is an essential stage of manufacturing glass containers, i.e. bottles or jars. An initial glass preform is brought into a mould and subsequently blown into the mould shape. Over the last few decades, a wide range of numerical models for forward glass blow process simulation have been developed. A considerable challenge is the inverse problem: to determine an optimal preform from the desired container shape. A simulation model for blowing glass containers based on finite element methods has previously been developed [1, 2]. This model uses level set methods to track the
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Zhan, Jie, Yifu Han, Allan Fogwill, et al. "A Novel Integrated Numerical Simulation Scheme for Transient Gas Flow in Shale Matrix." In SPE Symposium: Production Enhancement and Cost Optimisation. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/189199-ms.

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Seyr, Martin, and Stefan Jakubek. "Dynamic trajectory generation via numerical multi-objective optimisation." In 2007 American Control Conference. IEEE, 2007. http://dx.doi.org/10.1109/acc.2007.4282153.

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Keskin, Akin. "Application of Numerical Optimisation to Support Engineering Design." In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-1545.

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Huétink, J., A. H. van den Boogaard, H. J. M. Geijselears, and T. Meinders. "Numerical Forming Simulations and Optimisation in Advanced Materials." In MATERIALS PROCESSING AND DESIGN; Modeling, Simulation and Applications; NUMIFORM '07; Proceedings of the 9th International Conference on Numerical Methods in Industrial Forming Processes. AIP, 2007. http://dx.doi.org/10.1063/1.2740825.

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Reports on the topic "Numerical optimisation"

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Nobile, F., Q. Ayoul-Guilmard, S. Ganesh, et al. D6.5 Report on stochastic optimisation for wind engineering. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.04.

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This report presents the latest methods of optimisation under uncertainties investigated in the ExaQUte project, and their applications to problems related to civil and wind engineering. The measure of risk throughout the report is the conditional value at risk. First, the reference method is presented: the derivation of sensitivities of the risk measure; their accurate computation; and lastly, a practical optimisation algorithm with adaptive statistical estimation. Second, this method is directly applied to a nonlinear relaxation oscillator (FitzHugh–Nagumo model) with numerical experiments t
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