Relevant bibliographies by topics / Numerical Optimization Techniques

Contents

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

Academic literature on the topic 'Numerical Optimization Techniques'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Numerical Optimization Techniques"

1

Coleman, Tom. "Numerical Optimization Techniques (Yurij G. Evtushenko)." SIAM Review 29, no. 2 (1987): 309–10. http://dx.doi.org/10.1137/1029056.

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Hartmann, D., and K. Lehner. "Non-numerical modeling techniques in structural optimization." Structural Optimization 4, no. 3-4 (1992): 172–78. http://dx.doi.org/10.1007/bf01742740.

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Zhang, Guo Hong, Rong De Li, Chang Tian, Ke Qiang Qiu, and Jun Xian Ma. "Optimization Techniques in Numerical Simulation of Casting Process." Applied Mechanics and Materials 457-458 (October 2013): 463–66. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.463.

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This paper presents an overview and example using optimization techniques in casting numerical simulation. Most of the design work can fulfill with the software without human intervention. It really frees the engineer from the amount of trial-and-error that is necessary in traditional modeling.
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Kimmich, S., R. Reitinger, and E. Ramm. "Integration of different numerical techniques in shape optimization." Structural Optimization 4, no. 3-4 (1992): 149–55. http://dx.doi.org/10.1007/bf01742736.

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Gireesha. B, Mr, and . "A Literature Survey on Artificial Swarm Intelligence based Optimization Techniques." International Journal of Engineering & Technology 7, no. 4.5 (2018): 455. http://dx.doi.org/10.14419/ijet.v7i4.5.20205.

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From few decades’ optimizations techniques plays a key role in engineering and technological field applications. They are known for their behaviour pattern for solving modern engineering problems. Among various optimization techniques, heuristic and meta-heuristic algorithms proved to be efficient. In this paper, an effort is made to address techniques that are commonly used in engineering applications. This paper presents a basic overview of such optimization algorithms namely Artificial Bee Colony (ABC) Algorithm, Ant Colony Optimization (ACO) Algorithm, Fire-fly Algorithm (FFA) and Particle
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Ferguson, David R. "Construction of curves and surfaces using numerical optimization techniques." Computer-Aided Design 18, no. 1 (1986): 15–21. http://dx.doi.org/10.1016/s0010-4485(86)80004-5.

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Mengali, Giovanni. "Ride quality improvements by means of numerical optimization techniques." Journal of Guidance, Control, and Dynamics 17, no. 5 (1994): 1037–41. http://dx.doi.org/10.2514/3.21306.

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Al-Shimari, Nofl Sh, and Ahmed Sabah Al-Jilawi. "Improving Theoretical Line Search Techniques of Practical Numerical Optimization." Journal of Physics: Conference Series 1818, no. 1 (2021): 012140. http://dx.doi.org/10.1088/1742-6596/1818/1/012140.

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Shen, Hai, Yunlong Zhu, and Xiaodan Liang. "Lifecycle-Based Swarm Optimization Method for Numerical Optimization." Discrete Dynamics in Nature and Society 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/892914.

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Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO). Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization opera
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Foumani, Mahmoud S., Amir Khajepour, and Mohammad Durali. "Optimization of Engine Mount Characteristics Using Experimental/Numerical Analysis." Journal of Vibration and Control 9, no. 10 (2003): 1121–39. http://dx.doi.org/10.1177/107754603030697.

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In this paper an experimental/numerical technique is developed for engine mount optimization. The method is general and can be applied to optimize active and passive vibration isolators or absorbers in any mechanical systems or civil structures. Engine mount optimization techniques mostly rely on an accurate mathematical model of the whole vehicle, which in most cases is not available or is too difficult to develop. As a result, the current approach for selecting engine mounts for a vehicle is based upon trial and error which is very time-consuming and expensive. The proposed technique counts
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Dissertations / Theses on the topic "Numerical Optimization Techniques"

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Urlea, Maria. "Numerical Optimization Techniques for Secure Communications Over MIMO Channels." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31859.

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As multimedia applications become more popular, wireless communication systems are expected to reliably provide increased data rates. Multiple Input Multiple Output (MIMO) technologies can meet this demand without using additional bandwidth or transmit power. MIMO is part of modern wireless communication standards. Another critical aspect of communications is to secure the confidentiality of data transmission. Cryptography accomplishes this at the upper layers of the protocol stack. At the physical layer, data travels unencrypted and can be secured by using the channel characteristics to ``hi
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Meister, Neil E. "Application of numerical optimization techniques to surface combatant design synthesis." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA355524.

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Thesis (M.S. in Mechanical Engineering) Naval Postgraduate School, September 1998.<br>"September 1998." Thesis advisor(s): Matthew D. Kelleher, C.N. Calvano. Includes bibliographical references (p. 229-230). Also available online.
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McCollum, Clarence B. "Numerical analysis of the magnetic slider/disk interface using optimization techniques." Thesis, Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/16083.

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Noreland, Daniel. "Numerical Techniques for Acoustic Modelling and Design of Brass Wind Instruments." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3507.

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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
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Davis, Denny E. "Optimization of transducers for active structural acoustic control of complex structures using numerical techniques." Thesis, Virginia Tech, 1995. http://hdl.handle.net/10919/40657.

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Chalas, Jeffrey Michael. "Design and Location Optimization of Electrically Small Antennas Using Modal Techniques." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1420798842.

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Eggeling, Eva [Verfasser]. "Meteorological Data Assimilation – Local Analysis and Efficient Numerical Techniques for Constrained Differential Optimization / Eva Eggeling." Aachen : Shaker, 2004. http://d-nb.info/1170543049/34.

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Ghazisaeidi, Amirhossein. "Advanced Numerical Techniques for Design and Optimization of Optical Links Employing Nonlinear Semiconductor Optical Amplifiers." Thesis, Université Laval, 2011. http://www.theses.ulaval.ca/2011/27541/27541.pdf.

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Romero, García Vicente. "On the control of propagating acoustic waves in sonic crystals: analytical, numerical and optimization techniques." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/8982.

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El control de las propiedades acústicas de los cristales de sonido (CS) necesita del estudio de la distribución de dispersores en la propia estructura y de las propiedades acústicas intrínsecas de dichos dispersores. En este trabajo se presenta un estudio exhaustivo de diferentes distribuciones, así como el estudio de la mejora de las propiedades acústicas de CS constituidos por dispersores con propiedades absorbentes y/o resonantes. Estos dos procedimientos, tanto independientemente como conjuntamente, introducen posibilidades reales para el control de la propagación de ondas acústicas a trav
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Books on the topic "Numerical Optimization Techniques"

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Numerical optimization techniques. Optimization Software, Inc., Publications Division, 1985.

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Evtushenko, Yurij G. Numerical Optimization Techniques. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5022-7.

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Evtushenko, Yurii G. Numerical optimization Techniques. Optimization Software, Inc. Publications Division, 1985.

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Ermoliev, Yuri, and Roger J. B. Wets, eds. Numerical Techniques for Stochastic Optimization. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-61370-8.

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Tost, Gerard Olivar, and Olga Vasilieva, eds. Analysis, Modelling, Optimization, and Numerical Techniques. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12583-1.

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1943-, Marti Kurt, ed. Stochastic optimization techniques: Numerical methods and technical applications. Springer, 2002.

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Meister, Neil E. Application of numerical optimization techniques to surface combatant design synthesis. Naval Postgraduate School, 1998.

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1943-, Marti Kurt, and Kall Peter, eds. Stochastic programming: Numerical techniques and engineering applications : proceedings of the 2nd GAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications", held at the Federal Armed Forces University Munich, Neubiberg/München, Germany, June 15-17, 1993. Springer, 1995.

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1931-, Waśniewski Jerzy, ред. Applied parallel computing: Industrial-strength computation and optimization : Third International Workshop, PARA ʼ96, Lyngby, Denmark, August 18-21, 1996 : proceedings. Springer, 1996.

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Gomes, Carla. Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems: 10th International Conference, CPAIOR 2013, Yorktown Heights, NY, USA, May 18-22, 2013. Proceedings. Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "Numerical Optimization Techniques"

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Vanderplaats, Garret N. "Numerical Optimization Techniques." In Computer Aided Optimal Design: Structural and Mechanical Systems. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83051-8_5.

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Evtushenko, Yurij G. "Notation." In Numerical Optimization Techniques. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5022-7_1.

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Evtushenko, Yurij G. "An Introduction to Optimization Theory." In Numerical Optimization Techniques. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5022-7_2.

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Evtushenko, Yurij G. "Convergence Theorems and their Application to the Investigation of Numerical Methods." In Numerical Optimization Techniques. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5022-7_3.

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Evtushenko, Yurij G. "The Penalty Function Method." In Numerical Optimization Techniques. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5022-7_4.

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Evtushenko, Yurij G. "Numerical Methods for Solving Nonlinear Programming Problems Using Modified Lagrangians." In Numerical Optimization Techniques. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5022-7_5.

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Evtushenko, Yurij G. "Relaxation Methods for Solving Nonlinear Programming Problems." In Numerical Optimization Techniques. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5022-7_6.

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Evtushenko, Yurij G. "Numerical Methods for Solving Optimal Control Problems." In Numerical Optimization Techniques. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5022-7_7.

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Evtushenko, Yurij G. "Search for Global Solutions." In Numerical Optimization Techniques. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5022-7_8.

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Zhao, Zhiye. "Basic Numerical Optimization Techniques." In Lecture Notes in Engineering. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84382-2_2.

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Conference papers on the topic "Numerical Optimization Techniques"

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"Numerical and optimization techniques [breaker page]." In 2020 IEEE XXVth International Seminar/Workshop Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED). IEEE, 2020. http://dx.doi.org/10.1109/diped49797.2020.9273351.

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Dévai, Gergely, Zoltán Gera, and Zoltán Kelemen. "Language abstractions for low level optimization techniques." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756166.

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de Wit, Albert, and Fred van Keulen. "Numerical Comparison of Multi-Level Optimization Techniques." In 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-1895.

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Matus-Vargas, Antonio, Gustavo Rodriguez-Gomez, Jose Martinez-Carranza, and Arturo Munoz-Silva. "Numerical optimization techniques for nonlinear quadrotor control." In 2017 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2017. http://dx.doi.org/10.1109/icuas.2017.7991483.

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LUNDBERG, BRUCE, and AUBREY POORE. "Numerical continuation and bifurcation techniques for parametric nonlinear programming." In 4th Symposium on Multidisciplinary Analysis and Optimization. American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-4785.

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Sergeyev, Yaroslav D., Dmitri E. Kvasov, Marat S. Mukhametzhanov, and Angela De Franco. "Acceleration techniques in the univariate Lipschitz global optimization." In 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/10.1063/1.4965415.

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Zhang, Richard Y., Cedric Josz, and Somayeh Sojoudi. "Conic Optimization Theory: Convexification Techniques and Numerical Algorithms." In 2018 Annual American Control Conference (ACC). IEEE, 2018. http://dx.doi.org/10.23919/acc.2018.8430887.

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Bosman, Peter A. N., and Edwin D. de Jong. "Combining gradient techniques for numerical multi-objective evolutionary optimization." In the 8th annual conference. ACM Press, 2006. http://dx.doi.org/10.1145/1143997.1144111.

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Connell, Ken O., and Andrew Cashman. "Development of a numerical wave tank with reduced discretization error." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7755252.

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Perwez, Ashif, Kanishk Patel, and Rakesh Kumar. "Numerical investigation of conduction in rectangular plate Subjected to different boundary conditions." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7754965.

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Reports on the topic "Numerical Optimization Techniques"

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Barnard, J. C., H. L. Wegley, and T. R. Hiester. Improving the performance of mass-consistent numerical models using optimization techniques. Office of Scientific and Technical Information (OSTI), 1985. http://dx.doi.org/10.2172/5154136.

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Allen, Luke, Joon Lim, Robert Haehnel, and Ian Detwiller. Rotor blade design framework for airfoil shape optimization with performance considerations. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41037.

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A framework for optimizing rotor blade airfoil shape is presented. The framework uses two digital workflows created within the Galaxy Simulation Builder (GSB) software package. The first is a workflow enabling the automated creation of a surrogate model for predicting airfoil performance coefficients. An accurate surrogate model for the rapid generation of airfoil coefficient tables has been developed using linear interpolation techniques that is based on C81Gen and ARC2D CFD codes. The second workflow defines the rotor blade optimization problem using GSB and the Dakota numerical optimization
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