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Journal articles on the topic 'Numerical optimisation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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.2017.10006778.

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12

Caseiro, João F. M., Robertt Angelo Fontes Valente, António Andrade-Campos, and Renato Natal Jorge. "Optimisation of Tubular Hydroforming Processes for Wrinkling and Thinning Control." Key Engineering Materials 473 (March 2011): 159–67. http://dx.doi.org/10.4028/www.scientific.net/kem.473.159.

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This paper is focused on the development and implementation of an innovative optimisation algorithm for the wrinkling and thinning control during the hydroforming of complex metallic parts. A straightforward numerical algorithm for simultaneous tracking and evaluation of the initiation of wrinkling/thinning defects was implemented, together with a numerical simulation program based on the Finite Element Method (FEM). After undesirable wrinkling/thinning patterns are identified during FEM simulation, the developed optimisation procedure (called Hybrid Differential Evolution Particle Swarm Optim

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13

Godec, Damir, Mislav Sever, Mislav Tujmer, and Bruno Krajačić. "Numerical Optimisation of Mould and Injection Moulding Process." Tehnički glasnik 19, Si1 (2025): 71–76. https://doi.org/10.31803/tg-20250326120249.

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The paper presents the optimisation of mould for injection moulding as well as injection moulding process parameters using numerical computer simulation in the Moldex3D software package. The optimisation process will be applied to a specific moulded part – branching terminal block – and an already manufactured mould. The paper includes evaluation of the existing mould for injection moulding designed and manufactured without application of numerical simulation of injection moulding process. According to the shortcomings found out in evaluation process, by application of numerical simulation of

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14

Susantez, Çiğdem, and Aldélio Bueno Caldeira. "Genetic Algorithm Optimisation of a TNT Solidification Model." Defence Science Journal 69, no. 6 (2019): 545–49. http://dx.doi.org/10.14429/dsj.69.14037.

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The control of the solidification process of energetic materials is important to prevent manufacturing defects in high explosive ammunitions. The present work aims to propose an optimisation procedure to determine the value of the model parameter, avoiding the traditional trial and error approach. In this work, the solidification of TNT has been numerically modelled employing apparent heat capacity method and the model parameter was optimised using genetic algorithm. One dimensional numerical model has been solved in Comsol Multiphysics Modeling Software and the genetic algorithm code was writ

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15

Zhang, Meng Jian, Dao Yin Long, Dan Dan Li, Xiao Wang, Tao Qin, and Jing Yang. "A novel chaotic grey wolf optimisation for high-dimensional and numerical optimisation." International Journal of Computer Applications in Technology 67, no. 2/3 (2021): 194. http://dx.doi.org/10.1504/ijcat.2021.121524.

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16

Yang, Jing, Xiao Wang, Tao Qin, Dao Yin Long, Dan Dan Li, and Meng Jian Zhang. "A novel chaotic grey wolf optimisation for high-dimensional and numerical optimisation." International Journal of Computer Applications in Technology 67, no. 2/3 (2021): 194. http://dx.doi.org/10.1504/ijcat.2021.10045758.

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17

Pinto, Mark Christhian Barrueta, and Yongbo Li. "Numerical investigation on welding process parameters optimisation using multi-objective optimisation technique." International Journal of Advanced Operations Management 12, no. 3 (2020): 195. http://dx.doi.org/10.1504/ijaom.2020.10032145.

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18

Pinto, Mark Christhian Barrueta, and Yongbo Li. "Numerical investigation on welding process parameters optimisation using multi-objective optimisation technique." International Journal of Advanced Operations Management 12, no. 3 (2020): 195. http://dx.doi.org/10.1504/ijaom.2020.109788.

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19

Berteau, J. Ph, J. Ph Pastre, S. Mesure, M. Pithioux, G. Bolini, and P. Chabrand. "Optimisation of brace treatment with numerical modelling." Computer Methods in Biomechanics and Biomedical Engineering 12, sup1 (2009): 45–46. http://dx.doi.org/10.1080/10255840903065399.

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20

Odstrcil, M., J. Mlynar, T. Odstrcil, B. Alper, and A. Murari. "Modern numerical methods for plasma tomography optimisation." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 686 (September 2012): 156–61. http://dx.doi.org/10.1016/j.nima.2012.05.063.

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21

Xiang, qin. "An improved firefly algorithm for numerical optimisation." International Journal of Computing Science and Mathematics 6, no. 2 (2015): 201. http://dx.doi.org/10.1504/ijcsm.2015.069466.

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22

Guo, Bao-Yu, Ke-Jun Dong, and Ai-Bing Yu. "Optimisation of sputnik distributor using numerical method." International Journal of Computational Fluid Dynamics 23, no. 2 (2009): 119–35. http://dx.doi.org/10.1080/10618560902740618.

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23

G., W., and Philippe G. Ciarlet. "Introduction to Numerical Linear Algebra and Optimisation." Mathematics of Computation 55, no. 191 (1990): 395. http://dx.doi.org/10.2307/2008817.

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24

Dai, Chaohua, Jiajun Ouyang, Weirong Chen, Yunfang Zhu, and Lei Ma. "Human group optimiser for global numerical optimisation." International Journal of Bio-Inspired Computation 6, no. 5 (2014): 333. http://dx.doi.org/10.1504/ijbic.2014.065586.

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25

Naar, Raphaëlle, and François Bay. "Numerical optimisation for induction heat treatment processes." Applied Mathematical Modelling 37, no. 4 (2013): 2074–85. http://dx.doi.org/10.1016/j.apm.2012.04.058.

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26

Guha, A. "Optimisation of aero gas turbine engines." Aeronautical Journal 105, no. 1049 (2001): 345–58. http://dx.doi.org/10.1017/s0001924000012264.

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Abstract A systematic methodology for the thermodynamic optimisation of civil bypass engines (turbofan or advanced propulsors) is presented, which would be useful for designing air-breathing engines based on “clean-sheet analysis”. The process starts with establishing an optimum specific thrust for the engine based on an economic analysis (installation constraints, noise regulations etc. also need to be considered). The task of the optimisation process is then to find the combination of optimum values of fan pressure ratio, overall pressure ratio, bypass ratio and turbine entry temperature con

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27

Shariatmadar, Keivan, and Mark Versteyhe. "Numerical Linear Programming under Non-Probabilistic Uncertainty Models — Interval and Fuzzy Sets." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. 03 (2020): 469–95. http://dx.doi.org/10.1142/s0218488520500191.

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This paper considers a linear optimisation problem under uncertainty with at least one element modelled as a non-probabilistic uncertainty. The uncertainty is expressed in the coefficient matrices of constraints and/or coefficients of goal function. Previous work converts such problems to classical (linear) optimisation problems and eliminates uncertainty by converting the linear programming under uncertainty problem to a decision problem using imprecise probability and imprecise decision theory. Our aim here is to generalise this approach numerically and present three methods to calculate the

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28

Haas, Titus, Sascha Weikert, and Konrad Wegener. "MPCC-Based Set Point Optimisation for Machine Tools." International Journal of Automation Technology 13, no. 3 (2019): 407–18. http://dx.doi.org/10.20965/ijat.2019.p0407.

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Numerical control code is typically used for manufacturing a workpiece using machine tools. Most state-of-the-art approaches decouple the set point optimisation into two steps: the geometry and the feed rate optimisation that does not necessarily result in time-optimal set points for the desired geometry. Given the originally programmed geometry through the numerical control code, dynamic constraints of the machine tool, and maximum permissible contour error for the optimisation, a model predictive contouring control based set point optimisation approach is developed to generate time-optimal s

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29

Ali, Karim, Tim Stallard, and Pablo Ouro. "Evaluating wind-farm power generation using a new direct integration of axisymmetric turbine wake." Journal of Physics: Conference Series 2767, no. 9 (2024): 092015. http://dx.doi.org/10.1088/1742-6596/2767/9/092015.

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Abstract Engineering wake models enable fast calculation of wind-farm power generation including wake-turbine interactions, which is critical for annual energy yield calculations and for farm optimisation either at the design phase or during operation. The operating point of a turbine is determined based on its rotor-averaged wind speed, which is impacted by upstream wakes. The rotor-averaged deficit is typically calculated numerically by using a set of averaging points across the rotor disk, for which a Gaussian shape function is commonly evaluated. This numerical approach can have uncertaint

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30

Godi, N. Y., L. B. Zhengwuvi, and M. O. Petinrin. "Forced convection heat transfer in micro heat sinks with square and circular configuration." Nigerian Journal of Technological Development 20, no. 2 (2023): 35–42. http://dx.doi.org/10.4314/njtd.v20i2.1272.

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This paper reports the results of three-dimensional numerical optimisation of microchannel heat exchanger with square and circular cooling channels. The objective of the optimisation is to maximise the global thermal conductance or minimise global thermal resistance. Response surface optimisation methodology (RSM) is used in the numerical optimisation. A high-density heat flux (2.5×106W/m2) is imposed at the bottom surface of the unit cell microchannel and numerical simulation carried out using ANSYS Fluent commercial software package.The elemental volume and axial length N=10mm of the microch

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31

Düğenci, Muharrem, and Mehmet Emin Aydin. "A honeybees-inspired heuristic algorithm for numerical optimisation." Neural Computing and Applications 32, no. 16 (2019): 12311–25. http://dx.doi.org/10.1007/s00521-019-04533-x.

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Abstract Swarm intelligence is all about developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributors so that a complementary collective effort can be achieved to offer a useful solution. The main points in organising the harmony remain as managing the diversification and intensification actions appropriately, where the efficiency of collective behaviours depends on blending these two actions appropriately. In this paper, a hybrid bee algorithm is presented, which harmonis

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32

Porsev, V. I., A. I. Sivkov, and E. P. Voroshilin. "Estimating angular velocities of a ballistic object using radial acceleration." Journal of «Almaz – Antey» Air and Space Defence Corporation, no. 2 (June 30, 2016): 30–35. http://dx.doi.org/10.38013/2542-0542-2016-2-30-35.

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We developed an algorithm for estimating the total velocity vector of a ballistic object based on its radial acceleration, without employing numerical optimisation methods. We supply a comparison between the efficiency of the algorithm functioning and an existing optimisation method.

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33

Khurana, M., and H. Winarto. "Development and validation of an efficient direct numerical optimisation approach for aerofoil shape design." Aeronautical Journal 114, no. 1160 (2010): 611–28. http://dx.doi.org/10.1017/s0001924000004097.

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Abstract Intelligent shape optimisation architecture is developed, validated and applied in the design of high-altitude long endurance aerofoil (HALE). The direct numeric optimisation (DNO) approach integrating a geometrical shape parameterisation model coupled to a validated flow solver and a population based search algorithm are applied in the design process. The merit of the DNO methodology is measured by computational time efficiency and feasibility of the optimal solution. Gradient based optimisers are not suitable for multi-modal solution topologies. Thus, a novel particle swarm optimise

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34

Sun, Liang, and Bing Wang. "Robust Optimisation Approach for Vehicle Routing Problems with Uncertainty." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/901583.

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We formulated a solution procedure for vehicle routing problems with uncertainty (VRPU for short) with regard to future demand and transportation cost. Unlike E-SDROA (expectation semideviation robust optimisation approach) for solving the proposed problem, the formulation focuses on robust optimisation considering situations possibly related to bidding and capital budgets. Besides, numerical experiments showed significant increments in the robustness of the solutions without much loss in solution quality. The differences and similarities of the robust optimisation model and existing robust op

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35

Banks, Robert J., Dan E. Browne, and P. A. Warburton. "Rapid quantum approaches for combinatorial optimisation inspired by optimal state-transfer." Quantum 8 (February 13, 2024): 1253. http://dx.doi.org/10.22331/q-2024-02-13-1253.

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We propose a new design heuristic to tackle combinatorial optimisation problems, inspired by Hamiltonians for optimal state-transfer. The result is a rapid approximate optimisation algorithm. We provide numerical evidence of the success of this new design heuristic. We find this approach results in a better approximation ratio than the Quantum Approximate Optimisation Algorithm at lowest depth for the majority of problem instances considered, while utilising comparable resources. This opens the door to investigating new approaches for tackling combinatorial optimisation problems, distinct from

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36

Ramakrishna, Varanasi, Shinadam Rama Krishna, and PVS Ganesh Kumar. "Experimental and Numerical Investigation of Engine Foundation for Vibration Reduction." Defence Science Journal 68, no. 6 (2018): 604. http://dx.doi.org/10.14429/dsj.68.12705.

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<p>The purpose of this study is to minimise frequency response of engine foundation using topology optimisation. The study involves vibration response estimation of an existing marine engine foundation, validation of estimations with measurements and estimation of reduction in vibration response after optimisation. Initially, solid model of baseline model is generated using dimensions of the existing foundation measured by a laser line probe coordinate measuring machine. Harmonic analysis is used to find the vibration response of the foundation. These results are experimentally validated

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37

Merklein, Marion, Maren Johannes, M. Biasutti, and Michael Lechner. "Numerical Optimisation of a Shear Specimen Geometry According to ASTM." Key Engineering Materials 549 (April 2013): 317–24. http://dx.doi.org/10.4028/www.scientific.net/kem.549.317.

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Complex material models used for the numerical representation of forming processes need in addition to tension and compression tests also shear tests to completely analyse the behaviour of the material under different loading conditions. There are two concepts of shear test one uses symmetrical specimens with two shear zones (according to Miyauchi) the other one specimens with a single shear zone. In both cases, a homogeneous distribution of the strain in the shear zone is essential for the validity of the shear test. Therefore, the length and width of the shear zone in a single shear specimen

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38

Kijanski, Wojciech, and Franz-Joseph Barthold. "Two-scale shape optimisation based on numerical homogenisation techniques and variational sensitivity analysis." Computational Mechanics 67, no. 4 (2021): 1021–40. http://dx.doi.org/10.1007/s00466-020-01955-6.

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AbstractThis contribution presents a theoretical and computational framework for two-scale shape optimisation of nonlinear elastic structures. Particularly, minimum compliance optimisation problems with composite (matrix-inclusion) microstructures subjected to static loads and volume-type design constraints are focused. A homogenisation-based FE$$^2$$ 2 scheme is extended by an enhanced formulation of variational (shape) sensitivity analysis based on Noll’s intrinsic, frame-free formulation of continuum mechanics. The obtained overall two-scale sensitivity information couples shape variations

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39

Ait Ouchaoui, A., M. Nassraoui, and B. Radi. "Numerical investigation of the effect of topology optimisation methods parameters in the topology quality, the strength, and the computational cost." Archives of Materials Science and Engineering 123, no. 2 (2023): 55–71. http://dx.doi.org/10.5604/01.3001.0054.2492.

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The literature abounds with many distinct topology optimisation methods, many of which share common parameter configurations. This study demonstrates that alternative parameter configurations may produce better results than common parameters. Additionally, we try to answer two fundamental questions: identifying the most effective topology optimisation method and determining the optimal parameter selection within this optimisation method. In order to respond to these questions, we conducted a comparative and objective analysis of topology optimisation methods.This paper evaluates four prominent

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40

Lógó, J., and E. Pintér. "Numerical Methods in Probabilistic Topology Optimisation: A Review." Computational Technology Reviews 5 (September 4, 2012): 79–108. http://dx.doi.org/10.4203/ctr.5.3.

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41

Colloud, Floren. "Advanced modelling of human movements using numerical optimisation." Movement & Sport Sciences 90, no. 4 (2015): 1. http://dx.doi.org/10.3917/sm.090.0001.

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42

Colloud, Floren. "Advanced modelling of human movements using numerical optimisation." Movement & Sport Sciences - Science & Motricité, no. 90 (2015): 1–6. http://dx.doi.org/10.1051/sm/2016001.

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43

Roshanaei, M. M., C. Lucas, and A. R. Mehrabian. "Adaptive beamforming using a novel numerical optimisation algorithm." IET Microwaves, Antennas & Propagation 3, no. 5 (2009): 765. http://dx.doi.org/10.1049/iet-map.2008.0188.

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44

Hiltcher, Yann, Michèle Guingand, and Jean Pierre de Vaujany. "Numerical simulation and optimisation of worm gear cutting." Mechanism and Machine Theory 41, no. 9 (2006): 1090–110. http://dx.doi.org/10.1016/j.mechmachtheory.2005.10.016.

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45

Xue, Gang, Wenbin Song, Simon J. Cox, and Andy Keane. "Numerical Optimisation as Grid Services for Engineering Design." Journal of Grid Computing 2, no. 3 (2004): 223–38. http://dx.doi.org/10.1007/s10723-004-6742-9.

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46

Boudinar, M. Ben, W. T. Hanbury, and S. Avlonitis. "Numerical simulation and optimisation of spiral-wound modules." Desalination 86, no. 3 (1992): 273–90. http://dx.doi.org/10.1016/0011-9164(92)80038-b.

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47

Draa, Amer, Samira Bouzoubia, and Imene Boukhalfa. "A sinusoidal differential evolution algorithm for numerical optimisation." Applied Soft Computing 27 (February 2015): 99–126. http://dx.doi.org/10.1016/j.asoc.2014.11.003.

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48

Herty, Michael, and Veronika Schleper. "Time discretizations for numerical optimisation of hyperbolic problems." Applied Mathematics and Computation 218, no. 1 (2011): 183–94. http://dx.doi.org/10.1016/j.amc.2011.05.116.

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49

Fischer, M., and X. Jiang. "Numerical optimisation for model evaluation in combustion kinetics." Applied Energy 156 (October 2015): 793–803. http://dx.doi.org/10.1016/j.apenergy.2015.05.002.

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50

Colomer, Maria Prat, Luke Mortimer, Irénée Frérot, Máté Farkas, and Antonio Acín. "Three numerical approaches to find mutually unbiased bases using Bell inequalities." Quantum 6 (August 17, 2022): 778. http://dx.doi.org/10.22331/q-2022-08-17-778.

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Mutually unbiased bases correspond to highly useful pairs of measurements in quantum information theory. In the smallest composite dimension, six, it is known that between three and seven mutually unbiased bases exist, with a decades-old conjecture, known as Zauner's conjecture, stating that there exist at most three. Here we tackle Zauner's conjecture numerically through the construction of Bell inequalities for every pair of integers n,d&#x2265;2 that can be maximally violated in dimension d if and only if n MUBs exist in that dimension. Hence we turn Zauner's conjecture into an optimisa

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