Journal articles: 'Nonlinear optimisation'

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Wang, Jiakun Liu &. Xu-Jia. "Light Refraction is Nonlinear Optimisation." Journal of Mathematical Study 54, no. 2 (June 2021): 142–63. http://dx.doi.org/10.4208/jms.v54n2.21.02.

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Diouf, Serigne, Mamadou M. Diop, and Alassane Sy. "NUMERICAL IMAGING USING NONLINEAR OPTIMISATION." International Journal of Numerical Methods and Applications 20, no. 1 (January 10, 2021): 1–16. http://dx.doi.org/10.17654/nm020010001.

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Dobersek, D., and D. Goricanec. "Optimisation of tree path pipe network with nonlinear optimisation method." Applied Thermal Engineering 29, no. 8-9 (June 2009): 1584–91. http://dx.doi.org/10.1016/j.applthermaleng.2008.07.017.

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Safari, Sina, and Julián Londoño Monsalve. "Benchmarking Optimisation Methods for Model Selection and Parameter Estimation of Nonlinear Systems." Vibration 4, no. 3 (August 5, 2021): 648–65. http://dx.doi.org/10.3390/vibration4030036.

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Characterisation and quantification of nonlinearities in the engineering structures include selecting and fitting a good mathematical model to a set of experimental vibration data with significant nonlinear features. These tasks involve solving an optimisation problem where it is difficult to choose a priori the best optimisation technique. This paper presents a systematic comparison of ten optimisation methods used to select the best nonlinear model and estimate its parameters through nonlinear system identification. The model selection framework fits the structure’s equation of motions using time-domain dynamic response data and takes into account couplings due to the presence of the nonlinearities. Three benchmark problems are used to evaluate the performance of two families of optimisation methods: (i) deterministic local searches and (ii) global optimisation metaheuristics. Furthermore, hybrid local–global optimisation methods are examined. All benchmark problems include a free play nonlinearity commonly found in mechanical structures. Multiple performance criteria are considered based on computational efficiency and robustness, that is, finding the best nonlinear model. Results show that hybrid methods, that is, the multi-start strategy with local gradient-based Levenberg–Marquardt method and the particle swarm with Levenberg–Marquardt method, lead to a successful selection of nonlinear models and an accurate estimation of their parameters within acceptable computational times.

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Liu, Hanmin, Xuesong Yan, and Qinghua Wu. "An Improved Pigeon-Inspired Optimisation Algorithm and Its Application in Parameter Inversion." Symmetry 11, no. 10 (October 15, 2019): 1291. http://dx.doi.org/10.3390/sym11101291.

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Pre-stack amplitude variation with offset (AVO) elastic parameter inversion is a nonlinear, multi-solution optimisation problem. The techniques that combine intelligent optimisation algorithms and AVO inversion provide an effective identification method for oil and gas exploration. However, these techniques also have shortcomings in solving nonlinear geophysical inversion problems. The evolutionary optimisation algorithms have recognised disadvantages, such as the tendency of convergence to a local optimum resulting in poor local optimisation performance when dealing with multimodal search problems, decreasing diversity and leading to the prematurity of the population as the number of evolutionary iterations increases. The pre-stack AVO elastic parameter inversion is nonlinear with slow convergence, while the pigeon-inspired optimisation (PIO) algorithm has the advantage of fast convergence and better optimisation characteristics. In this study, based on the characteristics of the pre-stack AVO elastic parameter inversion problem, an improved PIO algorithm (IPIO) is proposed by introducing the particle swarm optimisation (PSO) algorithm, an inverse factor, and a Gaussian factor into the PIO algorithm. The experimental comparisons indicate that the proposed IPIO algorithm can achieve better inversion results.

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Özcan, Dinçer, Ümit Sönmez, and Levent Güvenç. "Optimisation of the Nonlinear Suspension Characteristics of a Light Commercial Vehicle." International Journal of Vehicular Technology 2013 (February 18, 2013): 1–16. http://dx.doi.org/10.1155/2013/562424.

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The optimum functional characteristics of suspension components, namely, linear/nonlinear spring and nonlinear damper characteristic functions are determined using simple lumped parameter models. A quarter car model is used to represent the front independent suspension, and a half car model is used to represent the rear solid axle suspension of a light commercial vehicle. The functional shapes of the suspension characteristics used in the optimisation process are based on typical shapes supplied by a car manufacturer. The complexity of a nonlinear function optimisation problem is reduced by scaling it up or down from the aforementioned shape in the optimisation process. The nonlinear optimised suspension characteristics are first obtained using lower complexity lumped parameter models. Then, the performance of the optimised suspension units are verified using the higher fidelity and more realistic Carmaker model. An interactive software module is developed to ease the nonlinear suspension optimisation process using the Matlab Graphical User Interface tool.

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Beran, Václav, and Petr Dlask. "Nonlinear optimisation and rational cash flow." Engineering, Construction and Architectural Management 14, no. 3 (May 8, 2007): 277–92. http://dx.doi.org/10.1108/09699980710744917.

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Ławryńczuk, Maciej. "A Family of Model Predictive Control Algorithms With Artificial Neural Networks." International Journal of Applied Mathematics and Computer Science 17, no. 2 (June 1, 2007): 217–32. http://dx.doi.org/10.2478/v10006-007-0020-5.

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A Family of Model Predictive Control Algorithms With Artificial Neural NetworksThis paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine local linearisation and a nonlinear free trajectory. Single-point and multi-point linearisation methods are discussed. The MPC-NPL structure is far more reliable and less computationally demanding in comparison with the MPC-NO one because it solves a quadratic programming problem, which can be done efficiently within a foreseeable time frame. At the same time, closed-loop performance of both algorithm classes is similar. Finally, a hybrid MPC algorithm with Nonlinear Prediction, Linearisation and Nonlinear optimisation (MPC-NPL-NO) is discussed.

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Skworcow, P., D. Paluszczyszyn, and B. Ulanicki. "Pump schedules optimisation with pressure aspects in complex large-scale water distribution systems." Drinking Water Engineering and Science Discussions 7, no. 1 (February 10, 2014): 121–49. http://dx.doi.org/10.5194/dwesd-7-121-2014.

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Abstract. This paper considers optimisation of pump and valve schedules in complex large-scale water distribution networks (WDN), taking into account pressure aspects such as minimum service pressure and pressure-dependent leakage. An optimisation model is automatically generated in GAMS language from a hydraulic model in EPANET format and from additional files describing operational constraints, electricity tariffs and pump station configurations. The paper describes in details how each hydraulic component is modelled. To reduce the size of the optimisation problem the full hydraulic model is simplified using module reduction algorithm, while retaining the nonlinear characteristics of the model. Subsequently, a nonlinear programming solver CONOPT is used to solve the optimisation model, which is in the form of Nonlinear Programming with Discontinuous Derivatives (DNLP). The results produced by CONOPT are processed further by heuristic algorithms to generate integer solution. The proposed approached was tested on a large-scale WDN model provided in EPANET format. The considered WDN included complex structures and interactions between pump stations. Solving of several scenarios considering different horizons, time steps, operational constraints, demand levels and topological changes demonstrated ability of the approach to automatically generate and solve optimisation problems for variety of requirements.

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Ha, Manh Hung, Viet Hung Truong, and Minh Phuong Nguyen. "Rao algorithm-based method for optimisation of steel frames using nonlinear inelastic analysis." Ministry of Science and Technology, Vietnam 63, no. 8 (August 30, 2021): 35–39. http://dx.doi.org/10.31276/vjst.63(8).35-39.

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Metaheuristic optimisation algorithms have been increasingly used because of their superior ability to find globally optimal solutions. Theoretically, these algorithms are very powerful and can be applied in all types of optimisation problems. However, in reality, their performance depends on the characteristics of each optimisation problem class. In addition, the complexity of an algorithm is an important factor affecting its application in specific design problems. In this paper, the Rao optimisation algorithm, one of the newest metaheuristic algorithms, is presented for the optimisation algorithm of steel frame structures. The advantage of the Rao algorithm is that it is quite simple with few parameters, so engineers can easily apply it in daily design tasks. A nonlinear inelastic analysis is used to consider the nonlinear behaviors of the steel structure. A two-story space steel frame is studied. The results show that Rao-1 is more stable and converged more quickly but easily trapped in local solutions than Rao-2 and Rao-4. Rao-4 is more efficient in global search but its stability is less than other algorithms. Keywords:nonlinear inelastic analysis, optimisation, Rao algorithm, steel frame.

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Ortigosa, R., J. Martínez-Frutos, D. Ruiz, A. Donoso, and J. C. Bellido. "Density-based topology optimisation considering nonlinear electromechanics." Structural and Multidisciplinary Optimization 64, no. 1 (April 10, 2021): 257–80. http://dx.doi.org/10.1007/s00158-021-02886-3.

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Liu, Jiakun. "A class of nonlinear optimisation and applications." Methods and Applications of Analysis 28, no. 1 (2021): 31–52. http://dx.doi.org/10.4310/maa.2021.v28.n1.a3.

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Sansaturio, M. Eugenia, Andrea Milani, and Luisa Cattaneo. "Nonlinear optimisation and the asteroid identification problem." Symposium - International Astronomical Union 172 (1996): 193–98. http://dx.doi.org/10.1017/s0074180900127342.

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Differential correction procedure allows us to improve orbits for which new observations are available; however, it only works provided the original orbit is within the convergence domain of the pseudo-Newton method. Given the strong nonlinearity of the problem, this only occurs when the residuals of the new observations with respect to the old orbit are quite small.

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Harrison, Peter G. "Linearisation: An optimisation for nonlinear functional programs." Science of Computer Programming 10, no. 3 (June 1988): 281–318. http://dx.doi.org/10.1016/0167-6423(88)90052-4.

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Luo, X. G., Z. C. Zhang, C. K. Kwong, and J. F. Tang. "Share-of-Surplus Product Line Optimisation with Price Levels." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/389749.

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Kraus and Yano (2003) established the share-of-surplus product line optimisation model and developed a heuristic procedure for this nonlinear mixed-integer optimisation model. In their model, price of a product is defined as a continuous decision variable. However, because product line optimisation is a planning process in the early stage of product development, pricing decisions usually are not very precise. In this research, a nonlinear integer programming share-of-surplus product line optimization model that allows the selection of candidate price levels for products is established. The model is further transformed into an equivalent linear mixed-integer optimisation model by applying linearisation techniques. Experimental results in different market scenarios show that the computation time of the transformed model is much less than that of the original model.

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Skworcow, P., D. Paluszczyszyn, and B. Ulanicki. "Pump schedules optimisation with pressure aspects in complex large-scale water distribution systems." Drinking Water Engineering and Science 7, no. 1 (June 16, 2014): 53–62. http://dx.doi.org/10.5194/dwes-7-53-2014.

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Abstract. This paper considers optimisation of pump and valve schedules in complex large-scale water distribution networks (WDN), taking into account pressure aspects such as minimum service pressure and pressure-dependent leakage. An optimisation model is automatically generated in the GAMS language from a hydraulic model in the EPANET format and from additional files describing operational constraints, electricity tariffs and pump station configurations. The paper describes in details how each hydraulic component is modelled. To reduce the size of the optimisation problem the full hydraulic model is simplified using module reduction algorithm, while retaining the nonlinear characteristics of the model. Subsequently, a nonlinear programming solver CONOPT is used to solve the optimisation model, which is in the form of Nonlinear Programming with Discontinuous Derivatives (DNLP). The results produced by CONOPT are processed further by heuristic algorithms to generate integer solution. The proposed approached was tested on a large-scale WDN model provided in the EPANET format. The considered WDN included complex structures and interactions between pump stations. Solving of several scenarios considering different horizons, time steps, operational constraints, demand levels and topological changes demonstrated ability of the approach to automatically generate and solve optimisation problems for a variety of requirements.

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Langenbucher, Achim, Nóra Szentmáry, Alan Cayless, Jascha Wendelstein, and Peter Hoffmann. "Strategies for formula constant optimisation for intraocular lens power calculation." PLOS ONE 17, no. 5 (May 5, 2022): e0267352. http://dx.doi.org/10.1371/journal.pone.0267352.

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Background To investigate modern nonlinear iterative strategies for formula constant optimisation and show the application and results from a large dataset using a set of disclosed theoretical-optical lens power calculation concepts. Methods Nonlinear iterative optimisation algorithms were implemented for optimising the root mean squared (SoSPE), the mean absolute (SoAPE), the mean (MPE), the standard deviation (SDPE), the median (MEDPE), as well as the 90% confidence interval (CLPE) of the prediction error (PE), defined as the difference between postoperative achieved and formula predicted spherical equivalent power of refraction. Optimisation was performed using the Levenberg-Marquardt algorithm (SoSPE and SoAPE) or the interior point method (MPE, SDPE, MEDPE, CLPE) for the SRKT, Hoffer Q, Holladay 1, Haigis, and Castrop formulae. The results were based on a dataset of measurements made on 888 eyes after implantation of an aspherical hydrophobic monofocal intraocular lens (Vivinex, Hoya). Results For all formulae and all optimisation metrics, the iterative algorithms showed a fast and stable convergence after a couple of iterations. The results prove that with optimisation for SoSPE, SoAPE, MPE, SDPE, MEDPE, and CLPE the root mean squared PE, mean absolute PE, mean PE, standard deviation of PE, median PE, and confidence interval of PE could be minimised in all situations. The results in terms of cumulative distribution function are quite coherent with optimisation for SoSPE, SoAPE, MPE and MEDPE, whereas with optimisation for SDPE and CLPE the standard deviation and confidence interval of the PE distribution could only be minimised at the cost of a systematic offset in mean and median PE. Conclusion Nonlinear iterative techniques are capable of minimising any statistical metrics (e.g. root mean squared or mean absolute error) of any target parameter (e.g. PE). These optimisation strategies are an important step towards optimising for the target parameters which are used for evaluating the performance of lens power calculation formulae.

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Van Rooyen, M., M. Sears, and S. Zlobec. "Constraint qualifications in input optimisation." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 30, no. 3 (January 1989): 326–42. http://dx.doi.org/10.1017/s0334270000006275.

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AbstractWe introduce assumptions in input optimisation that simplify the necessary conditions for an optimal input. These assumptions, in the context of nonlinear programming, give rise to conceptually new kinds of constraint qualifications.

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Mutra, R. R., and J. Srinivas. "An Integrated Bearing Parameter Identification Approach Using A Nonlinear Optimisation Scheme." International Journal of Automotive and Mechanical Engineering 16, no. 1 (March 16, 2019): 6245–62. http://dx.doi.org/10.15282/ijame.16.1.2019.12.0474.

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In system identification process, efficient global optimisation schemes play a vital role. In the present work, an effective optimisation-based identification methodology of bearing stiffness and damping coefficients using the bearing response data is proposed. The flexible rotor is initially analysed by finite element model with nonlinear bearing forces. The dynamic equations of rotor are solved to obtain the bearing responses in frequency domain at different operating speeds. The equivalent speed dependent parameters of stiffness and damping are then obtained by solving an error-based optimal formulation via modified particle-swarm optimisation scheme. The robustness of the approach is tested with added input noise. The approach is illustrated with the simulation results of rotor supported on full floating ring bearings and using an experimental data obtained from a rotor supported on two similar journal bearings. The error in identification is not exceeding five percent. The modified particle swarm optimisation is converging faster, and it is taking less computational time. The results are shown in the forms of graphs and tables.

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Mohamodhosen, Bilquis Bibi Safoorah, Frederic Gillon, Mounaim Tounzi, and Loïc Chevallier. "Topology optimisation using nonlinear behaviour of ferromagnetic materials." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 6 (November 5, 2018): 2211–23. http://dx.doi.org/10.1108/compel-12-2017-0522.

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Purpose The purpose of this paper is to propose a methodology to seek the optimal topology of electromagnetic devices using the density method while taking into account the non-linear behaviour of ferromagnetic materials. The tools and methods used are detailed and applied to a three-dimensional (3D) electromagnet for analysis and validation. Resulting topologies with and without the non-linear behaviour are investigated. Design/methodology/approach The polynomial mapping is used with the density method for material distribution in the optimisation domain. To consider the non-linear behaviour of the materials, an analytical approximation based on the Marrocco equation is used and combined with the polynomial mapping to solve the problem. Furthermore, to prevent the occurrence of intermediate materials, a weighted sum of objectives is used in the optimisation problem to eliminate these undesired materials. Findings Taking into account the non-linear materials behaviour and 3D model during topology optimisation (TO) is important, as it produces more physically feasible and coherent results. Moreover, the use of a weighted sum of objectives to eliminate intermediate materials increases the number of evaluations to reach the final solution, but it is efficient. Practical implications Considering non-linear materials behaviour yields results closer to reality, and physical feasibility of structures is more obvious in absence of intermediate materials. Originality/value This work tackles an obstacle of TO in electromagnetism which is often overlooked in literature, that is, non-linear behaviour of ferromagnetic materials by proposing a methodology.

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Anastasiadis, A. D., and G. D. Magoulas. "Particle Swarms and Nonextensive Statistics for Nonlinear Optimisation." Open Cybernetics & Systemics Journal 2, no. 1 (June 13, 2008): 173–79. http://dx.doi.org/10.2174/1874110x00802010173.

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Bucur, Dorin, and Paola Trebeschi. "Shape optimisation problems governed by nonlinear state equations." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 128, no. 5 (1998): 945–63. http://dx.doi.org/10.1017/s0308210500030006.

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The purpose of this paper is to give a compactness-continuity result for the solution of a nonlinear Dirichlet problem in terms of its domain variation. The topology in the family of domains is given by the Hausdorff metric and continuity is obtained under capacity conditions. A generalisation of Sverak's result in iV-dimensions is deduced as a particular case.

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Halldorsson, U., H. Unbehauen, and M. Fikar. "Nonlinear predictive control with multirate optimisation step lengths." IEE Proceedings - Control Theory and Applications 152, no. 3 (May 1, 2005): 273–84. http://dx.doi.org/10.1049/ip-cta:20041310.

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Baranowski, Jerzy, and Waldemar Bauer. "Trust region based parametric optimisation for nonlinear systems." Automatyka/Automatics 16, no. 1 (2012): 15. http://dx.doi.org/10.7494/automat.2012.16.1.15.

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Counsell, John, Obadah Zaher, Joseph Brindley, and Gavin Murphy. "Robust nonlinear HVAC systems control with evolutionary optimisation." Engineering Computations 30, no. 8 (November 11, 2013): 1147–69. http://dx.doi.org/10.1108/ec-04-2012-0079.

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Hickey, D., Keith Worden, Jan R. Wright, and J. E. Cooper. "Force Appropriation for Nonlinear Systems (FANS) Using Optimisation Methods." Applied Mechanics and Materials 5-6 (October 2006): 265–76. http://dx.doi.org/10.4028/www.scientific.net/amm.5-6.265.

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Normal mode force appropriation is a method of physically exciting and measuring the undamped natural frequencies and normal mode shapes of a structure. Traditionally used in the aerospace industry for ground vibration testing, it is capable of accurate normal mode estimates. The method attempts to determine multi-point force vectors that will induce single mode behaviour, thus allowing each mode to be viewed in isolation. However it fails to tackle changing dynamic response with forcing level of excitation in nonlinear systems. The method of Force Appropriation for Nonlinear Systems or FANS, produces a special appropriated force vector resulting in nonlinear response. The structure responds dominantly in the target linear mode shape permitting the direct nonlinear characteristics of that mode to be identified in the absence of cross coupling effects.

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Sun, Haifeng, Jing Du, Chuan Jin, Jinhua Feng, Jian Wang, Song Hu, and Junbo Liu. "Global Source Optimisation Based on Adaptive Nonlinear Particle Swarm Optimisation Algorithm for Inverse Lithography." IEEE Photonics Journal 13, no. 4 (August 2021): 1–7. http://dx.doi.org/10.1109/jphot.2021.3102229.

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Sánchez, Cristian, Lionel Bloch, Jordan Holweger, Christophe Ballif, and Nicolas Wyrsch. "Optimised Heat Pump Management for Increasing Photovoltaic Penetration into the Electricity Grid." Energies 12, no. 8 (April 25, 2019): 1571. http://dx.doi.org/10.3390/en12081571.

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Advanced control of heat pumps with thermal storage and photovoltaics has recently been promoted as a promising solution to help decarbonise the residential sector. Heat pumps and thermal storage offer a valuable flexibilisation mean to integrate stochastic renewable energy sources into the electricity grid. Heat pump energy conversion is nonlinear, leading to a challenging nonlinear optimisation problem. However, issues like global optimum uncertainty and the time-consuming methods of current nonlinear programming solvers draw researchers to linearise heat pump models that are then implemented in faster and globally convergent linear programming solvers. Nevertheless, these linearisations generate some inaccuracies, especially in the calculation of the heat pump’s coefficient of performance ( C O P ). In order to solve all of these issues, this paper presents a heuristic control algorithm (HCA) to provide a fast, accurate and near-optimal solution to the original nonlinear optimisation problem for a single-family house with a photovoltaic system, using real consumption data from a typical Swiss house. Results highlight that the HCA solves this optimisation problem up to 1000 times faster, yielding an operation that is up to 49% cheaper and self-consumption rates that are 5% greater than other nonlinear solvers. Comparing the performance of the HCA and the linear solver intlinprog, it is shown that the HCA provides more accurate heat pump control with an increase of up to 9% in system Operating Expense OPEX and a decrease of 8% in self-consumption values.

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Zhu, J. Z., C. S. Chang, W. Yan, and G. Y. Xu. "Reactive power optimisation using an analytic hierarchical process and a nonlinear optimisation neural network approach." IEE Proceedings - Generation, Transmission and Distribution 145, no. 1 (1998): 89. http://dx.doi.org/10.1049/ip-gtd:19981715.

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Maciejewski, Igor. "A Method for Shaping the Dynamic Characteristics of Vibration Isolation Systems." Solid State Phenomena 198 (March 2013): 615–20. http://dx.doi.org/10.4028/www.scientific.net/ssp.198.615.

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In this paper a method for shaping the dynamic characteristics of vibration isolation systems is presented. The multi-criteria optimisation procedure is utilized in order to find the Pareto-optimal system configuration with simultaneous minimization of conflicted optimisation criteria: the isolated body acceleration and suspension travel. As an example of the proposed method, the seat with a pneuma-hydraulic suspension is investigated and its vibro-isolation properties are shaped by an appropriate selection of the system characteristics. The basic findings of the paper consist in the optimisation procedure that takes into account the nonlinear dynamic behaviour of vibration isolation systems.

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Chang, Ching-Chun. "Automation of reversible steganographic coding with nonlinear discrete optimisation." Connection Science 34, no. 1 (June 14, 2022): 1719–35. http://dx.doi.org/10.1080/09540091.2022.2078792.

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Mees, A. I. "CONTROL AND OPTIMISATION (The Linear Treatment of Nonlinear Problems)." Bulletin of the London Mathematical Society 19, no. 3 (May 1987): 295–96. http://dx.doi.org/10.1112/blms/19.3.295.

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Nahas, Nabil, and Mustapha Nourelfath. "Nonlinear threshold accepting meta-heuristic for combinatorial optimisation problems." International Journal of Metaheuristics 3, no. 4 (2014): 265. http://dx.doi.org/10.1504/ijmheur.2014.068904.

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Szukiewicz, Barbara, Ulrich Eckern, and Karol I. Wysokiński. "Optimisation of a three-terminal nonlinear heat nano-engine." New Journal of Physics 18, no. 2 (February 23, 2016): 023050. http://dx.doi.org/10.1088/1367-2630/18/2/023050.

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Khorshid, E., and A. Seireg. "Discrete nonlinear optimisation by constraint decomposition and designer interaction." International Journal of Computer Applications in Technology 12, no. 2/3/4/5 (1999): 233. http://dx.doi.org/10.1504/ijcat.1999.000208.

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Ma, Borong, Zhixin Ma, Dagan Nie, and Xianbo Li. "IFOA: an improved forest algorithm for continuous nonlinear optimisation." International Journal of Computational Science and Engineering 19, no. 3 (2019): 343. http://dx.doi.org/10.1504/ijcse.2019.10022719.

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Jenssen, Matthew, and Jozef Skokan. "Exact Ramsey numbers of odd cycles via nonlinear optimisation." Advances in Mathematics 376 (January 2021): 107444. http://dx.doi.org/10.1016/j.aim.2020.107444.

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Ong, S. L., and G. S. Lee. "Program DSSRSSR for the solution of nonlinear optimisation models." Advances in Engineering Software (1978) 8, no. 3 (July 1986): 134–40. http://dx.doi.org/10.1016/0141-1195(86)90032-x.

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Kotsialos, Apostolos. "Nonlinear optimisation using directional step lengths based on RPROP." Optimization Letters 8, no. 4 (July 2, 2013): 1401–15. http://dx.doi.org/10.1007/s11590-013-0668-8.

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De Domenico, Dario, and Iman Hajirasouliha. "Multi-level performance-based design optimisation of steel frames with nonlinear viscous dampers." Bulletin of Earthquake Engineering 19, no. 12 (June 21, 2021): 5015–49. http://dx.doi.org/10.1007/s10518-021-01152-7.

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AbstractThis paper presents a practical multi-level performance-based optimisation method of nonlinear viscous dampers (NVDs) for seismic retrofit of existing substandard steel frames. A Maxwell model is adopted to simulate the behaviour of the combined damper-supporting brace system, with a fractional power-law force–velocity relationship for the NVDs, while a distributed-plasticity fibre-based section approach is used to model the beam-column members thus incorporating the nonlinearity of the parent steel frame in the design process. The optimum height-wise distribution of the damping coefficients of NVDs satisfying given performance requirements is identified via a uniform damage distribution (UDD) design philosophy. The efficiency of the proposed multi-level performance-based design optimisation is illustrated through nonlinear time-history analysis of 3-, 7- and 12-storey steel frames under both artificial and natural spectrum-compatible earthquakes. Sensitivity analysis is performed to investigate the effects of initial height-wise damping distribution, convergence factor and uncertainty in design ground-motion prediction on the optimisation strategy. The efficiency of the final optimum design solution is also investigated by using drift-based, velocity-based, and energy-based UDD approaches to identify the most efficient performance index parameter for optimisation purposes. It is found that regardless of the selected performance parameter, the optimum damping distribution identified by the proposed methodology leads to frames exhibiting lower maximum inter-storey drift, local damage (maximum plastic rotation) and global damage index compared to an equal-cost uniform damping distribution. However, using drift-based UDD approach generally results in a better seismic performance. It is shown that the proposed UDD optimisation method can be efficiently used to satisfy multiple performance objectives at different intensity levels of the earthquake excitation, in line with performance-based design recommendations of current seismic codes. The proposed method is easy to implement for practical design purposes and represents a simple yet efficient tool for optimum seismic retrofit of steel frames with NVDs.

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Ławryńczuk, Maciej, and Robert Nebeluk. "Computationally Efficient Nonlinear Model Predictive Control Using the L1 Cost-Function." Sensors 21, no. 17 (August 30, 2021): 5835. http://dx.doi.org/10.3390/s21175835.

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Model Predictive Control (MPC) algorithms typically use the classical L2 cost function, which minimises squared differences of predicted control errors. Such an approach has good numerical properties, but the L1 norm that measures absolute values of the control errors gives better control quality. If a nonlinear model is used for prediction, the L1 norm leads to a difficult, nonlinear, possibly non-differentiable cost function. A computationally efficient alternative is discussed in this work. The solution used consists of two concepts: (a) a neural approximator is used in place of the non-differentiable absolute value function; (b) an advanced trajectory linearisation is performed on-line. As a result, an easy-to-solve quadratic optimisation task is obtained in place of the nonlinear one. Advantages of the presented solution are discussed for a simulated neutralisation benchmark. It is shown that the obtained trajectories are very similar, practically the same, as those possible in the reference scheme with nonlinear optimisation. Furthermore, the L1 norm even gives better performance than the classical L2 one in terms of the classical control performance indicator that measures squared control errors.

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Ławryńczuk, Maciej. "Nonlinear State–Space Predictive Control with On–Line Linearisation and State Estimation." International Journal of Applied Mathematics and Computer Science 25, no. 4 (December 1, 2015): 833–47. http://dx.doi.org/10.1515/amcs-2015-0060.

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Abstract This paper describes computationally efficient model predictive control (MPC) algorithms for nonlinear dynamic systems represented by discrete-time state-space models. Two approaches are detailed: in the first one the model is successively linearised on-line and used for prediction, while in the second one a linear approximation of the future process trajectory is directly found on-line. In both the cases, as a result of linearisation, the future control policy is calculated by means of quadratic optimisation. For state estimation, the extended Kalman filter is used. The discussed MPC algorithms, although disturbance state observers are not used, are able to compensate for deterministic constant-type external and internal disturbances. In order to illustrate implementation steps and compare the efficiency of the algorithms, a polymerisation reactor benchmark system is considered. In particular, the described MPC algorithms with on-line linearisation are compared with a truly nonlinear MPC approach with nonlinear optimisation repeated at each sampling instant.

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Pérez-Aracil, Jorge, Carlos Camacho-Gómez, Emiliano Pereira, Vahid Vaziri, Sumeet S. Aphale, and Sancho Salcedo-Sanz. "Eliminating Stick-Slip Vibrations in Drill-Strings with a Dual-Loop Control Strategy Optimised by the CRO-SL Algorithm." Mathematics 9, no. 13 (June 29, 2021): 1526. http://dx.doi.org/10.3390/math9131526.

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Friction-induced stick-slip vibrations are one of the major causes for down-hole drill-string failures. Consequently, several nonlinear models and control approaches have been proposed to solve this problem. This work proposes a dual-loop control strategy. The inner loop damps the vibration of the system, eliminating the limit cycle due to nonlinear friction. The outer loop achieves the desired velocity with a fast time response. The optimal tuning of the control parameters is carried out with a multi-method ensemble meta-heuristic, the Coral Reefs Optimisation algorithm with Substrate Layer (CRO-SL). It is an evolutionary-type algorithm that combines different search strategies within a single population, obtaining a robust, high-performance algorithm to tackle hard optimisation problems. An application example based on a real nonlinear dynamics model of a drill-string illustrates that the controller optimised by the CRO-SL achieves excellent performance in terms of stick-slip vibrations cancellation, fast time response, robustness to system parameter uncertainties and chattering phenomenon prevention.

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Kijanski, Wojciech, and Franz-Joseph Barthold. "Two-scale shape optimisation based on numerical homogenisation techniques and variational sensitivity analysis." Computational Mechanics 67, no. 4 (March 6, 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 across micro- and macroscopic scales. A numerical example demonstrates the capabilities of the proposed variational sensitivity analysis and the (shape) optimisation framework. The investigations involve a mesh morphing scheme for the design parametrisation at both macro- and microscopic scales.

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Etaati, Bahareh, Amin Abdollahi Dehkordi, Ali Sadollah, Mohammed El-Abd, and Mehdi Neshat. "A Comparative State-of-the-Art Constrained Metaheuristics Framework for TRUSS Optimisation on Shape and Sizing." Mathematical Problems in Engineering 2022 (March 26, 2022): 1–13. http://dx.doi.org/10.1155/2022/6078986.

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In order to develop the dynamic effectiveness of the structures such as trusses, the application of optimisation methods plays a significant role in improving the shape and size of elements. However, conjoining two heterogeneous variables, nodal coordinates and cross-sectional elements, makes a challenging optimisation problem that is nonlinear, multimodal, large-scale with dynamic constraints. To handle these challenges, evolutionary and swarm optimisation algorithms can be robust and practical tools and show great potential to solve such complex problems. This paper proposed a comparative truss optimisation framework to solve two large-scale structures, including 314-bar and 260-bar trusses. The proposed framework consists of twelve state-of-the-art bio-inspired algorithms. The experimental results show that the marine predators algorithm (MPA) performed best compared with other algorithms in terms of convergence speed and the quality of the proposed designs of the trusses.

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Ashok, Alaknanda, Sandeep Singh, and Tarun Kumar Rawat. "Nonlinear system identification using butterfly optimisation algorithm and Hammerstein model." International Journal of Modelling, Identification and Control 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijmic.2022.10051352.

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Čižmešija, Mirjana, Zrinka Lukač, and Tomislav Novoselec. "Nonlinear optimisation approach to proposing novel Croatian Industrial Confidence Indicator." Croatian Review of Economic, Business and Social Statistics 5, no. 2 (December 1, 2019): 17–26. http://dx.doi.org/10.2478/crebss-2019-0008.

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AbstractCroatian Industrial Confidence Indicator (ICI) is one of the measures of mangers’ sentiment about the economic situation in the Croatian manufacturing industry. Since 2005, the ICI has been calculated in accordance with the harmonised European Commission methodology as a simple average of three variables: order books, stocks of finished products and production expectation. It was empirically confirmed that the ICI could predict the direction of change in industrial production more than one month ahead. With the aim of raising the ICI forecasting power, this paper proposes a novel ICI with a different weighting scheme. The empirical analysis is based on monthly data for three standard ICI subcomponents and industrial production expressed as year-on-year growth rates. The data set covers the period from May 2008 to February 2019. Data sources were the European Commission and Eurostat. The newly defined ICI was constructed by using the nonlinear optimisation approach. The weights were determined by minimizing the root mean square error (RMSE) in a simple regression model and by maximizing the correlation coefficient between the ICI and industrial production for various time lags. The results reveal that the newly defined ICI performs better in adapting and following the industrial production growth rate as well as that the dominant component in the ICI is the production expectation.

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Asadi, Houshyar, Shady Mohamed, Delpak Rahim Zadeh, and Saeid Nahavandi. "Optimisation of nonlinear motion cueing algorithm based on genetic algorithm." Vehicle System Dynamics 53, no. 4 (February 2, 2015): 526–45. http://dx.doi.org/10.1080/00423114.2014.1003948.

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Maurya, M. R., V. Venkatasubramanian, S. Subramaniam, and S. J. Bornheimer. "Mixed-integer nonlinear optimisation approach to coarse-graining biochemical networks." IET Systems Biology 3, no. 1 (January 1, 2009): 24–39. http://dx.doi.org/10.1049/iet-syb:20080098.

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Jana, Mrinal, and Geetanjali Panda. "LR-optimal solution of nonlinear optimisation problem with varying parameters." International Journal of Operational Research 33, no. 4 (2018): 431. http://dx.doi.org/10.1504/ijor.2018.096485.

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Journal articles on the topic 'Nonlinear optimisation'

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