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Journal articles on the topic 'Multi parameter optimisation'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Nikita, Rawat, Thakur Padmanabh, and Jadli Utkarsh. "Solar PV parameter estimation using multi-objective optimisation." Bulletin of Electrical Engineering and Informatics 8, no. 4 (2019): 1198–205. https://doi.org/10.11591/eei.v8i4.1312.

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The estimation of the electrical model parameters of solar PV, such as light-induced current, diode dark saturation current, thermal voltage, series resistance, and shunt resistance, is indispensable to predict the actual electrical performance of solar photovoltaic (PV) under changing environmental conditions. Therefore, this paper first considers the various methods of parameter estimation of solar PV to highlight their shortfalls. Thereafter, a new parameter estimation method, based on multi-objective optimisation, namely, Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is proposed. F

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McDonnell, Michael D. T., Daniel Arnaldo, Etienne Pelletier, et al. "Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining." Journal of Intelligent Manufacturing 32, no. 5 (2021): 1471–83. http://dx.doi.org/10.1007/s10845-020-01717-4.

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AbstractInteractions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships between these parameters. An alternative approach such as parameter optimisation, often requires a systematic and hence time-consuming experimental exploration over the available parame

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Mala-Jetmarova, Helena, Andrew Barton, and Adil Bagirov. "Sensitivity of algorithm parameters and objective function scaling in multi-objective optimisation of water distribution systems." Journal of Hydroinformatics 17, no. 6 (2015): 891–916. http://dx.doi.org/10.2166/hydro.2015.062.

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This paper presents an extensive analysis of the sensitivity of multi-objective algorithm parameters and objective function scaling tested on a large number of parameter setting combinations for a water distribution system optimisation problem. The optimisation model comprises two operational objectives minimised concurrently, the pump energy costs and deviations of constituent concentrations as a water quality measure. This optimisation model is applied to a regional non-drinking water distribution system, and solved using the optimisation software GANetXL incorporating the NSGA-II linked wit

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Krauße, T., J. Cullmann, P. Saile, and G. H. Schmitz. "Robust multi-objective calibration strategies – possibilities for improving flood forecasting." Hydrology and Earth System Sciences 16, no. 10 (2012): 3579–606. http://dx.doi.org/10.5194/hess-16-3579-2012.

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Abstract. Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently, automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situa

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Krauße, T., J. Cullmann, P. Saile, and G. H. Schmitz. "Robust multi-objective calibration strategies – chances for improving flood forecasting." Hydrology and Earth System Sciences Discussions 8, no. 2 (2011): 3693–741. http://dx.doi.org/10.5194/hessd-8-3693-2011.

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Abstract. Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situat

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Seibert, J. "Multi-criteria calibration of a conceptual runoff model using a genetic algorithm." Hydrology and Earth System Sciences 4, no. 2 (2000): 215–24. http://dx.doi.org/10.5194/hess-4-215-2000.

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Abstract. Abstract: Calibration of a model against more than one output variable is important for reliable simulations of internal processes. In this study, a genetic algorithm combined with local optimisation was proposed for automatic single- and multi-criteria calibration of the HBV model, a conceptual runoff model. The model and the optimisation algorithm were applied in two catchments with different geology where, in addition to observed runoff, time series of groundwater level data were available. For a theoretical, error-free test case with synthetic data, the optimisation algorithm was

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Słowik, Agnieszka, Chaitanya Mangla, Mateja Jamnik, Sean B. Holden, and Lawrence C. Paulson. "Bayesian Optimisation for Premise Selection in Automated Theorem Proving (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13919–20. http://dx.doi.org/10.1609/aaai.v34i10.7232.

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Modern theorem provers utilise a wide array of heuristics to control the search space explosion, thereby requiring optimisation of a large set of parameters. An exhaustive search in this multi-dimensional parameter space is intractable in most cases, yet the performance of the provers is highly dependent on the parameter assignment. In this work, we introduce a principled probabilistic framework for heuristic optimisation in theorem provers. We present results using a heuristic for premise selection and the Archive of Formal Proofs (AFP) as a case study.

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He, Yicheng, Kai Yang, Xiaoqing Wang, Haisong Huang, and Jiadui Chen. "Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning." Applied Sciences 12, no. 19 (2022): 9625. http://dx.doi.org/10.3390/app12199625.

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In a small sample welding test space, and to achieve online prediction and self-optimisation of process parameters for the resistance welding joint quality of power lithium battery packs, this paper proposes a welding quality prediction model. The model combines a chaos game optimisation algorithm (CGO) with the multi-output least-squares support vector regression machine (MLSSVR), and a multi-objective process parameter optimisation method based on a particle swarm algorithm. First, the MLSSVR model was constructed, and a hyperparameter optimisation strategy based on CGO was designed. Next, t

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Dekker, R., and R. P. Plasmeijer. "Multi-parameter maintenance optimisation via the marginal cost approach." Journal of the Operational Research Society 52, no. 2 (2001): 188–97. http://dx.doi.org/10.1057/palgrave.jors.2601072.

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Liu, J. S., and L. Hollaway. "Multi-Factor Optimisation of Large Reflector Antenna Structures." International Journal of Space Structures 11, no. 3 (1996): 307–20. http://dx.doi.org/10.1177/026635119601100303.

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A novel multi-parameter overall situation optimisation method has been developed for use on antenna reflector structures. Various structural performances arc included as objective functions. The design variables involve geometric and size variables of structures. Various working environments and loading cases which affect antenna performances could be combined in the optimisation mathematical model. An important aspect to the work is the establishment of evaluation criteria to optimise the design of a system. Such an optimisation procedure would satisfy extremely high design requirements. An 8

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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 (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 pro

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Kubit, Andrzej, Tomasz Trzepieciński, Rafał Kluz, Krzysztof Ochałek, and Ján Slota. "Multi-Criteria Optimisation of Friction Stir Welding Parameters for EN AW-2024-T3 Aluminium Alloy Joints." Materials 15, no. 15 (2022): 5428. http://dx.doi.org/10.3390/ma15155428.

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The aim of this research was the selection of friction stir welding (FSW) parameters for joining stiffening elements (Z-stringers) to a thin-walled structure (skin) made of 1 mm-thick EN AW-2024 T3 aluminium alloy sheets. Overlapping sheets were friction stir welded with variable values of welding speed, pin length (plunge depth), and tool rotational speed. The experimental research was carried out based on a three-factor three-level full factorial Design of Experiments plan (DoE). The load capacity of the welded joints was determined in uniaxial tensile/pure shear tests. Based on the results

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Cui, Yan, Wei Lu, and Jun Teng. "Updating of structural multi-scale monitoring model based on multi-objective optimisation." Advances in Structural Engineering 22, no. 5 (2018): 1073–88. http://dx.doi.org/10.1177/1369433218805235.

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Structural safety assessments are implemented based on measured data, but the limited number of sensors restricts the comprehensive acquisition of response information in large complex structures. A concurrent multi-scale model utilises global and local simulation characteristics to expand the insufficient measured data. Thus, good global and local simulation capability is necessary for structural health monitoring-oriented multi-scale model, and the updating of this monitoring model needs to consider the multi-type responses that are obtained from different structural scales. However, the exi

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Xie, Xing, Zhenlin Li, Baoshan Zhu, and Hong Wang. "Suppression of secondary flows in a centrifugal impeller by optimisation design." Engineering Computations 37, no. 9 (2020): 3023–44. http://dx.doi.org/10.1108/ec-09-2019-0411.

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Purpose The purpose of this study is to suppress secondary flows and improve aerodynamic performance of a centrifugal impeller. Design/methodology/approach A multi-objective optimisation design system was described. The optimization design system was composed of a three-dimensional (3D) inverse design, multi-objective optimisation and computational fluid dynamics (CFD) analysis. First, the control parameter ΔCp for the secondary flows was derived and selected as the optimisation objective. Then, aimed at minimising ΔCp, a 3D inverse design for impellers with different blade loading distributio

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Dawson, Rach, Carolyn O’Dwyer, Edward Irwin, et al. "Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer." Sensors 23, no. 8 (2023): 4007. http://dx.doi.org/10.3390/s23084007.

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Machine learning (ML) is an effective tool to interrogate complex systems to find optimal parameters more efficiently than through manual methods. This efficiency is particularly important for systems with complex dynamics between multiple parameters and a subsequent high number of parameter configurations, where an exhaustive optimisation search would be impractical. Here we present a number of automated machine learning strategies utilised for optimisation of a single-beam caesium (Cs) spin exchange relaxation free (SERF) optically pumped magnetometer (OPM). The sensitivity of the OPM (T/Hz)

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See, Chee Howe, Kieran J. MacKenzie, Oscar M. Dunens, and Andrew T. Harris. "Multi-parameter optimisation of carbon nanotube synthesis in fluidised-beds." Chemical Engineering Science 64, no. 16 (2009): 3614–21. http://dx.doi.org/10.1016/j.ces.2009.05.003.

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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 (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

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Bukhsh, Zaharah Allah, Irina Stipanovic, Sandra Skaric Palic, and Giel Klanker. "Robustness of the Multi-Attribute Utility Model for Bridge Maintenance Planning." Baltic Journal of Road and Bridge Engineering 13, no. 4 (2018): 404–15. http://dx.doi.org/10.7250/bjrbe.2018-13.425.

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Optimisation of maintenance planning is an essential part of bridge management. With the purpose to support maintenance planning, a multi- objective decision-making model is introduced in this paper. The model is based on multi-attribute utility theory, which is used for the optimisation process when multiple performance goals have to be taken into account. In the model, there are several parameters, which are freely chosen by the decision maker. The model is applied to the inventory of 22 bridges, where four Key Performance Indicators were determined for four performance aspects: reliability,

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Susperregui, Ana, Juan Manuel Herrero, Miren Itsaso Martinez, Gerardo Tapia-Otaegui, and Xavier Blasco. "Multi-Objective Optimisation-Based Tuning of Two Second-Order Sliding-Mode Controller Variants for DFIGs Connected to Non-Ideal Grid Voltage." Energies 12, no. 19 (2019): 3782. http://dx.doi.org/10.3390/en12193782.

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In this paper, a posteriori multi-objective optimisation (MOO) is applied to tune the parameters of a second-order sliding-mode control (2-SMC) scheme commanding the grid-side converter (GSC) of a doubly-fed induction generator (DFIG) subject to unbalanced and harmonically distorted grid voltage. Two variants (i.e., design concepts) of the same 2-SMC algorithm are assessed, which only differ in the format of their switching functions and which contain six and four parameters to be adjusted, respectively. A single set of parameters which stays valid for nine different operating regimes of the D

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Inkaew, Rewadee, and Pongchanun Luangpaiboon. "Multi-Response Surface Optimisation with Different Priorities for Ramp Process Parameter Design." Advanced Materials Research 548 (July 2012): 744–48. http://dx.doi.org/10.4028/www.scientific.net/amr.548.744.

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This study proposes a multi-response surface optimisation with different priorities (MRSOP) problem for determining the proper choices of a process parameter design (PPD) decision problem in a noisy environment of a ramp process in plastic injection molding. The proposed model attempts to minimise process responses of the flow mark defect and mold deposit. Firstly, Taguchi design and analysis are applied to screen out controllable design parameters significantly affecting the quality performances and the regression is then used to determine the form of the relationship between the response and

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Dionísio, R. P., G. Parca, C. Reis, and A. L. Teixeira. "Operational parameter optimisation of MZI-SOA using multi-objective genetic algorithms." Electronics Letters 47, no. 9 (2011): 561. http://dx.doi.org/10.1049/el.2011.0128.

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Keskin, Ridvan, and Ibrahim Aliskan. "Multi-Objective Optimisation-based Robust H∞ Controller Design Approach for a Multi-Level DC-DC Voltage Regulator." Elektronika ir Elektrotechnika 29, no. 1 (2023): 4–14. http://dx.doi.org/10.5755/j02.eie.32887.

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In case an analytical approach to the selection of any weighting function is not possible, the selection process is usually a random and time-consuming process. In robust H∞ control theory, the selection of scalar, time, or frequency-dependent weighting functions is the main issue to shape the amplitude-frequency characteristic curve of the feedback controller. Therefore, we propose a robust H∞ control approach which utilises the multi-objective grey wolf optimisation algorithm (MOGWO) to obtain the optimal performance weighting functions in the presence of right half-plane zeros and limited b

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Mercieca, Thomas, and Joseph G. Vella. "Multi-Dimensional Indexes in DBMSs." Journal of Cases on Information Technology 21, no. 3 (2019): 40–50. http://dx.doi.org/10.4018/jcit.2019070103.

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Multi-dimensional data is present across multimedia, data mining and other data-driven applications. The R-Tree is a popular index structure that DBMSs are implementing as core for efficient retrieval of such data. The gap between the best and worst-case performance is very wide in an R-tree. Thus, building quality R-trees quickly is desirable. Variations differ in how node overflow are approached during the building process. This article studies the R-Tree technique that the open-source PostgreSQL DBMS uses. Focus is on a specific parameter controlling node overflows as an optimisation target

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Jiang, Yongzhi, Pingbo Wu, Jing Zeng, Yingsheng Zhang, Yunchang Zhang, and Shuai Wang. "Multi-parameter and multi-objective optimisation of articulated monorail vehicle system dynamics using genetic algorithm." Vehicle System Dynamics 58, no. 1 (2019): 74–91. http://dx.doi.org/10.1080/00423114.2019.1566557.

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Doll, U., I. Röhle, and M. Dues. "Unsteady Multi-Parameter Flow Diagnostics By Filtered Rayleigh Scattering: System Design By Multi-Objective Optimisation." Proceedings of the International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics 20 (July 11, 2022): 1–18. http://dx.doi.org/10.55037/lxlaser.20th.23.

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The measurement of the time-resolved three-component (3C) velocity field together with scalar flow quantities such as temperature or pressure by laser-optical diagnostics is a challenging task. Current approaches typically employ combinations of different methods relying on tracer particles or molecules, which requires elaborate calibration procedures of the tracer's photo-physical properties and extensive instrumentation. In contrast to this, the tracer-free filtered Rayleigh scattering (FRS) technique has been proven to obtain combined time-averaged velocity and scalar fields and might offer

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al-Rifaie, Mohammad Majid. "Penguins Huddling Optimisation." International Journal of Agent Technologies and Systems 6, no. 2 (2014): 1–29. http://dx.doi.org/10.4018/ijats.2014040101.

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In our everyday life, people deal with many optimisation problems, some of which trivial and some more complex. These problems have been frequently addressed using multi-agent, population-based approaches. One of the main sources of inspiration for techniques applicable to complex search space and optimisation problems is nature. This paper proposes a new metaheuristic – Penguin Huddling Optimisation or PHO – whose inspiration is beckoned from the huddling behaviour of emperor penguins in Antarctica. The simplicity of the algorithm, which is the implementation of one such paradigm for continuo

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Panchu K., Padmanabhan, M. Rajmohan, R. Sundar, and R. Baskaran. "Multi-objective Optimisation of Multi-robot Task Allocation with Precedence Constraints." Defence Science Journal 68, no. 2 (2018): 175. http://dx.doi.org/10.14429/dsj.68.11187.

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Efficacy of the multi-robot systems depends on proper sequencing and optimal allocation of robots to the tasks. Focuses on deciding the optimal allocation of set-of-robots to a set-of-tasks with precedence constraints considering multiple objectives. Taguchi’s design of experiments based parameter tuned genetic algorithm (GA) is developed for generalised task allocation of single-task robots to multi-robot tasks. The developed methodology is tested for 16 scenarios by varying the number of robots and number of tasks. The scenarios were tested in a simulated environment with a maximum of 20 rob

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Paggiola, Giulia, Andrew J. Hunt, Con R. McElroy, James Sherwood, and James H. Clark. "Biocatalysis in bio-derived solvents: an improved approach for medium optimisation." Green Chem. 16, no. 4 (2014): 2107–10. http://dx.doi.org/10.1039/c3gc42526f.

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Chen, Zihua, Chuanli Wang, Huawei Jin, Jingzhao Li, Shunxiang Zhang, and Qichun Ouyang. "Hierarchical-fuzzy allocation and multi-parameter adjustment prediction for industrial loading optimisation." Connection Science 34, no. 1 (2022): 687–708. http://dx.doi.org/10.1080/09540091.2022.2031887.

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Damavandi, Esmaeil, Amin Kolahdooz, Yousef Shokoohi, Seyyed Ali Latifi Rostami, and Sayed Mohamadbagher Tabatabaei. "Multi-objective parameter optimisation to improve machining performance on deep drilling process." International Journal of Machining and Machinability of Materials 23, no. 5/6 (2021): 500. http://dx.doi.org/10.1504/ijmmm.2021.121196.

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Saporito, A., A. R. Day, T. G. Karayiannis, and F. Parand. "Multi-parameter building thermal analysis using the lattice method for global optimisation." Energy and Buildings 33, no. 3 (2001): 267–74. http://dx.doi.org/10.1016/s0378-7788(00)00091-8.

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Srinivasan, K., V. Balamurugan, and S. Jayanti. "Shape Optimisation of Curved Interconnecting Ducts." Defence Science Journal 65, no. 4 (2015): 300. http://dx.doi.org/10.14429/dsj.65.8353.

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<p class="AbstractText">Practical ducting layout in process plants needs to satisfy a number of on-site constraints. The search for an optimal flow path around the obstructions is a multi-parameter problem and is computationally prohibitively expensive. In this study, authors proposed a rapid and efficient methodology for the optimal linkage of arbitrarily oriented fluid flow ducts using a single-parameter quadratic/cubic Bézier curves in two/three dimensions to describe the centreline of the curved duct. A smooth interconnecting duct can then be generated by extruding the duct face alon

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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 (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 coeffi

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Paszek, S. "Use of Pareto optimisation for tuning power system stabilizers." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 1 (2012): 125–31. http://dx.doi.org/10.2478/v10175-012-0018-5.

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Use of Pareto optimisation for tuning power system stabilizers The paper presents a method for determining sets of Pareto optimal solutions (compromise sets) - parameter values of PSS3B system stabilizers working in a multi-machine power system - when optimising different multidimensional criteria. These criteria are determined for concrete disturbances when taking into account transient waveforms of the instantaneous power, angular speed and terminal voltage of generators in one, chosen generating unit or in all units of the system analysed. The application of multi-criteria methods allows ta

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Yue, Caitong, Tai shan Lou, Jie Wang, Jing Liang, and Guang Li. "Parameter optimisation of sliding window algorithm based on ensemble multi-objective evolutionary computation." International Journal of Bio-Inspired Computation 19, no. 4 (2022): 228. http://dx.doi.org/10.1504/ijbic.2022.10049009.

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Li, Guang, Jie Wang, Jing Liang, Caitong Yue, and Tai shan Lou. "Parameter optimisation of sliding window algorithm based on ensemble multi-objective evolutionary computation." International Journal of Bio-Inspired Computation 19, no. 4 (2022): 228. http://dx.doi.org/10.1504/ijbic.2022.124328.

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Zhang, Guohai, and Huibin Sun. "Multi-objective machining parameter optimisation for residual stress based on quantum cat swarm." International Journal of Service and Computing Oriented Manufacturing 3, no. 1 (2017): 54. http://dx.doi.org/10.1504/ijscom.2017.087962.

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Sun, Huibin, and Guohai Zhang. "Multi-objective machining parameter optimisation for residual stress based on quantum cat swarm." International Journal of Service and Computing Oriented Manufacturing 3, no. 1 (2017): 54. http://dx.doi.org/10.1504/ijscom.2017.10008918.

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Lin, Guohan, and Qin Wan. "Heterogeneous multi-subswarm particle swarm optimisation for numerical and parameter estimation of PMSM." International Journal of Wireless and Mobile Computing 13, no. 1 (2017): 51. http://dx.doi.org/10.1504/ijwmc.2017.087347.

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Wan, Qin, and Guohan Lin. "Heterogeneous multi-subswarm particle swarm optimisation for numerical and parameter estimation of PMSM." International Journal of Wireless and Mobile Computing 13, no. 1 (2017): 51. http://dx.doi.org/10.1504/ijwmc.2017.10008215.

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Gras, J. P., N. Sivasithamparam, M. Karstunen, and J. Dijkstra. "Strategy for consistent model parameter calibration for soft soils using multi-objective optimisation." Computers and Geotechnics 90 (October 2017): 164–75. http://dx.doi.org/10.1016/j.compgeo.2017.06.006.

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ZHAO, Yongpeng, Yongcang LI, Changxi MA, Ke WANG, and Xuecai XU. "Optimised LSTM Neural Network for Traffic Speed Prediction with Multi-Source Data Fusion." Promet - Traffic&Transportation 36, no. 4 (2024): 765–78. http://dx.doi.org/10.7307/ptt.v36i4.592.

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Predicting traffic speed accurately and in real-time is crucial for the development of smart transportation systems. Given the nonlinear and stochastic nature of vehicle data, integrating diverse spatio-temporal data sources with the Improved Particle Swarm Optimisation (IPSO) offers a promising approach to optimise the Long Short-Term Memory Neural Network (LSTM). Firstly, we enhance the optimisation capabilities of PSO by implementing nonlinear inertial weight and adaptive variation. Secondly, addressing the challenge of selecting the LSTM hyperparameters, the PSO algorithm effectively ident

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Le, Son Tay, Tuan Ngoc Nguyen, Dac-Khuong Bui, Quang Phuc Ha, and Tuan Duc Ngo. "Modelling and Multi-Objective Optimisation of Finger Joints: Improving Flexural Performance and Minimising Wood Waste." Buildings 13, no. 5 (2023): 1186. http://dx.doi.org/10.3390/buildings13051186.

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The wood industry faces the dual requirements of improving the quality of timber products and minimising waste during the manufacturing process. The finger joint, which is an end-to-end joining method for timber boards, is one of the most important aspects of engineering wood products. This study presents a numerical and optimisation investigation of the effects of finger-joint design parameters on the flexural behaviour of finger-jointed timber beams. A numerical model based on advanced three-dimensional finite element analysis was developed to model the behaviour of finger-jointed beams. Usi

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Jun, Tan Kai, Mohammad Yazdi Harmin, and Fairuz I. Romli. "Aeroelastic Tailoring of Composite Wing Design Using Bee Colony Optimisation." Applied Mechanics and Materials 629 (October 2014): 182–88. http://dx.doi.org/10.4028/www.scientific.net/amm.629.182.

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Bee Colony Optimisation (BCO) method is used to optimise the fibre orientation of a simple rectangular composite wing with respect to maximising flutter/divergence speed. A modified implementation is proposed to provide a suitable version of BCO algorithm for solving the multi-variable optimisation problem. 50 test cases are performed and the statistical investigation is made in order to investigate the effectiveness and robustness of the proposed algorithm. Consideration is also made in terms of the best weightage of the minimum confident parameter. The overall results indicate that the modif

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Takase, Y., L. Vacher, H. Ishino, et al. "Multi-dimensional optimisation of the scanning strategy for the LiteBIRD space mission." Journal of Cosmology and Astroparticle Physics 2024, no. 12 (2024): 036. https://doi.org/10.1088/1475-7516/2024/12/036.

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Abstract Large angular scale surveys in the absence of atmosphere are essential for measuring the primordial B-mode power spectrum of the Cosmic Microwave Background (CMB). Since this proposed measurement is about three to four orders of magnitude fainter than the temperature anisotropies of the CMB, in-flight calibration of the instruments and active suppression of systematic effects are crucial. We investigate the effect of changing the parameters of the scanning strategy on the in-flight calibration effectiveness, the suppression of the systematic effects themselves, and the ability to dist

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Zhao, H., Z. Chen, and J. Chen. "Optimisation analysis of reinforced cable distribution on the airship." Aeronautical Journal 126, no. 1298 (2021): 607–16. http://dx.doi.org/10.1017/aer.2021.84.

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AbstractReinforced cables are usually installed on flexible airship structures to enhance their load-bearing capability. However, reinforced cables also increase the total weight of the airship. In order to find a balance between large loading-bear capability and light weight, a multi-objective optimisation scheme based on the genetic algorithm NSGA-II is put forward for the reinforced cable distribution on the airship. Firstly, different cable distribution schemes are presented according to engineering experience and the optimal one is determined by load analysis. Then, the CAE method and opt

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Chen, Lijia, Kai Wang, Kezhong Liu, et al. "Combinatorial-Testing-Based Multi-Ship Encounter Scenario Generation for Collision Avoidance Algorithm Evaluation." Journal of Marine Science and Engineering 13, no. 2 (2025): 338. https://doi.org/10.3390/jmse13020338.

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Collision avoidance algorithms play a crucial role in ensuring the safety and effectiveness of autonomous ships, which require comprehensive testing in realistic multi-ship encounter scenarios. However, existing scenario generation methods often inadequately represent the spatiotemporal complexity and dynamic risk interactions of real-world encounters, leading to biased evaluations. To bridge this gap, this paper proposes a combinatorial-testing-based scenario generation framework integrated with spatiotemporal complexity optimisation. First, a full-process scenario representation model is dev

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Nie, Lufeng, Xiang Ji, and Heng Liu. "Research on Multi-objective Optimisation Algorithm for Light and Heat Environment in Underground Atrium Buildings." E3S Web of Conferences 455 (2023): 03003. http://dx.doi.org/10.1051/e3sconf/202345503003.

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This paper firstly defines the concepts of underground atrium body shape, climate adaptability and parametric design, analyses the climatic characteristics of cold regions to extract their climatic factors, and responds to the climate with the light and heat environment, extracts the influence factors of the light and heat environment of the underground atrium and the body shape factors of the underground atrium, and constructs the underground atrium body shape design parameter system according to the classification of the body shape factors. Secondly using Ladybug+Honeybee software, a paramet

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Jaouachi, B., M. Ben Hassen, and F. Sakli. "OPTIMISATION OF THE BEHAVIOUR OF SIZED WET SPLICED YARNS." AUTEX Research Journal 9, no. 1 (2009): 1–4. http://dx.doi.org/10.1515/aut-2009-090101.

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Abstract This work presents the contribution of optimisation analysis of the performance of wet pneumatic spliced cotton yarns using two different methods: the superimposed contours method and the function of desirability. Cotton yarn linear density (Yc), length of splice (SL), duration of water joining (Dwj) and duration of air joining (Daj) were optimised in the experimental field of interest. In this study regression equations expressing multi-component splice mechanical performances were elaborated by using the response surface method. In order to validate the results, a desirability funct

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Louw, Jakobus Murray, Willis de Ronde, Stephen Marais, and Thabisa Maweni. "Multi-objective design optimisation of a delta coordinate measurement machine." MATEC Web of Conferences 406 (2024): 04003. https://doi.org/10.1051/matecconf/202440604003.

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A systematic approach for multi-objective optimisation in machine design is presented and further demonstrated through a case study on a delta Coordinate Measurement Machine (CMM). Employing the Non- dominated Sorting Genetic Algorithm II (NSGA-II) [1], the methodology aims to balance competing objectives like measurement accuracy, motion resolution, and machine size. Through an iterative process and simulation- guided parameter refinement, new Pareto optimal solutions are identified at concurrent decision-making steps to reach a final design solution. The results from four concurrent simulati

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