Статті в журналах: "Optimisation"

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Jesudasan, Rejish, Ardeshir Hanifi, and Raffaello Mariani. "Investigating Planar and Nonplanar Wing Planform Optimisation for Ground Effect Aircraft." Aerospace 10, no. 11 (November 17, 2023): 969. http://dx.doi.org/10.3390/aerospace10110969.

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Wing-in-Ground (WIG) effect aircraft are gaining attention for their potential in reducing environmental impact. However, optimising wing planforms based solely on aerodynamics might improve performance while compromising static height stability of WIG aircraft. This study investigates the effects of planar and nonplanar wing planform optimisation for regional transport ground effect aircraft. Three distinct multiobjective wing planform optimisations are explored: planar wing optimisation, nonplanar wing optimisation, and nonplanar wingtip optimisation. These optimisations assess the impact on both aerodynamic efficiency and static height stability characteristics of a wing planform in ground effect, at three different flying altitudes. In extreme ground effect, the Pareto set includes wings with negative spanwise camber, enhancing both cushion sensation and aerodynamic efficiency by effectively utilizing ground effect, thus proving advantageous over planar wing configurations.

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Valetov, Eremey, Giovanni Dal Maso, Peter-Raymond Kettle, Andreas Knecht, and Angela Papa. "Beamline Optimisation for High-Intensity Muon Beams at PSI Using the Heterogeneous Island Model." Particles 7, no. 3 (August 1, 2024): 683–91. http://dx.doi.org/10.3390/particles7030039.

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The High Intensity Muon Beams (HIMB) project at the Paul Scherrer Institute (PSI) will deliver muon beams with unprecedented intensities of up to 1010muons/s for next-generation particle physics and material science experiments. This represents a hundredfold increase over the current state-of-the-art muon intensities, also provided by PSI. We performed beam dynamics optimisations and studies for the design of the HIMB beamlines MUH2 and MUH3 using Graphics Transport, Graphics Turtle, and G4beamline, the latter incorporating PSI’s own measured π+ cross-sections and variance reduction. We initially performed large-scale beamline optimisations using asynchronous Bayesian optimisation with DeepHyper. We are now developing an island-based evolutionary optimisation code glyfada based on the Paradiseo framework, where we implemented Message Passing Interface (MPI) islands with OpenMP parallelisation within each island. Furthermore, we implemented an island model that is also suitable for high-throughput computing (HTC) environments with asynchronous communication via a Redis database. The code interfaces with the codes COSY INFINITY and G4beamline. The code glyfada will provide heterogeneous island model optimisation using evolutionary optimisation and local search methods, as well as part-wise optimisation of the beamline with automatic advancement through stages. We will use the glyfada for a future large-scale optimisation of the HIMB beamlines.

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Wu, Qing, Colin Cole, Maksym Spiryagin, and Tim McSweeney. "Parallel multiobjective optimisations of draft gear designs." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 232, no. 3 (January 31, 2017): 744–58. http://dx.doi.org/10.1177/0954409717690981.

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This paper presents the methodology and results of the parallel multiobjective optimisations of draft gear designs. The methodology used white-box draft gear models, whose parameters were used as the optimisation variables. Two optimisation algorithms were used: genetic algorithm and particle swarm optimisation. All the optimised draft gear designs were constrained by impact tests to ensure that the optimised designs also comply with the current acceptance standards for draft gears. The performance of draft gears was assessed using whole-trip longitudinal train dynamics simulations and coupler fatigue damage calculations. Each simulation covered a round trip (loaded one way, empty on return) over a total of 640 km of track, which involved about 10 h of operational time. Three optimisation objectives were considered: minimal fatigue damage for wagon connection systems of loaded trains, minimal in-train forces for loaded trains, and minimal longitudinal wagon accelerations for empty trains. Two case studies were presented, which optimised two types of draft gears (single-stage and double-stage draft gears) using genetic algorithm and particle swarm optimisation, respectively.

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Vankan, W. J., R. Maas, and S. Grihon. "Efficient optimisation of large aircraft fuselage structures." Aeronautical Journal 118, no. 1199 (January 2014): 31–52. http://dx.doi.org/10.1017/s0001924000008915.

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Abstract This paper presents an innovative optimisation method for aircraft fuselage structural design. Detailed local finite element analyses of panel buckling are further processed such that they can be applied as failure constraints in the global level optimisation. The high computational costs involved with the finite element analyses are limited by advanced use of surrogate modelling methods. This yields high flexibility and efficiency in the local level optimisation procedure and allows for efficient gradient based search methods as well as more costly direct search optimisations like genetic algorithms (GAs). The method is demonstrated on a composite fuselage barrel design case considering common structural sizing variables like thicknesses and stringer dimensions. Optimised barrel designs are obtained where the constraints that are derived from the panel buckling analyses are active. The total computational cost for the complete local and global level optimisation procedures is in the order of days on common-performance hardware.

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Podobedov, Vitaly E. "DETERMINISTIC CHAOS PRODUCED BY LOCAL OPTIMISATION ALGORITHMS." Technological and Economic Development of Economy 13, no. 1 (March 31, 2007): 61–66. http://dx.doi.org/10.3846/13928619.2007.9637777.

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Deterministic chaos situations produced by local optimisation algorithms are considered. A family of test multi‐extremal functions is proposed that allows getting the collections of functions with an arbitrary pre‐defined number of the local optima. Special software is developed for generating test functions from the family, performing multiple local optimisations of different initial points, visualising the chaotic attraction regions. Properties of the regions of attraction are presented. Regularity of the attraction regions is required to be a special characteristic of the local optimisation algorithms.

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Tomašić, Dubravko, Dragan Peraković, and Marinko Jurčević. "Interdependence between Inventory Management and Employees’ Satisfaction." PROMET - Traffic&Transportation 25, no. 3 (June 19, 2013): 245–54. http://dx.doi.org/10.7307/ptt.v25i3.909.

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The study determines the correlation between the application of advanced models and methods of inventory optimisation in the supply chain in relation to the satisfaction of employees who are responsible for managing the inventory optimisation processes. The previous studies confirm that the optimisation of inventory management in the supply chain insures competitive advantages on the market. There is space for further research of impact of the achieved inventory optimisation in the supply chain on the change of the employees’ satisfaction. The paper establishes the interrelation of the interdependence of the achieved inventory optimisations on the satisfaction of the employees and the related synergy effects of acquiring added value of the companies on the market oriented to the satisfaction of the buyers and service users. The research has defined new knowledge in interdependence of inventory management optimisation on the change of indicators of employees’ satisfaction. Based on the performed research an assumption has been created for the design of an application package (so-called XaaS-based services) for the management of interaction processes of inventory optimization in the supply chain, satisfaction of service users and employees.

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Ali Bahr Ennil and Abdulhafid M A Elfaghi. "Multidisciplinary Optimization of Axial Turbine Blade Based on CFD Modelling and FEA Analysis." Journal of Advanced Research in Applied Mechanics 120, no. 1 (July 10, 2024): 40–49. http://dx.doi.org/10.37934/aram.120.1.4049.

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The turbine blade is designed to achieve expansion at high efficiency levels. For improving the turbine efficiency, different aerodynamic design optimisations are performed. On the other hand, the aerodynamic design must be enhanced to match the mechanical design. This research proposes a novel design optimisation method for both aerodynamic and mechanical requirements. A multidisciplinary optimisation approach is used to improve the reliability of the turbine design, which included the use of Computational Fluid Dynamics (CFD) models and Finite Element Analysis (FEA). The primary objective is to guarantee that the aerodynamically optimised blade profile could efficiently withstand mechanical stress. The multidisciplinary optimisation approach is successful in reducing total equivalent pressures from 49.72 MPa to 41.73 MPa while keeping the turbine's overall efficiency at an impressive level of 80.95%. These Results highlight the effectiveness of using a multidisciplinary optimization method to successfully improve the efficiency of a turbine blade profile while simultaneously ensuring its ability to withstand the needed mechanical loads. Using a multidisciplinary optimisation method, the turbine maintains an impressively high efficiency of approximately 83%, with only a marginal reduction of 1.8% compared to the efficiency achieved solely through aerodynamic blade optimisation.

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Yudianto, Aan. "Application of Multi-objective Adjoint-based Aerodynamic Optimisation on Generic Road Vehicle with Rear Spoiler." Automotive Experiences 7, no. 1 (April 27, 2024): 28–47. http://dx.doi.org/10.31603/ae.10577.

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Finding possible solutions where there are multiple conflicting objectives to be simultaneously satisfied is a challenging situation. Multi-objective optimisation of a rear spoiler on a generic road vehicle model is carried out by using adjoint-based optimisation coupled with Computational Fluid Dynamics. The study aims to reduce the vehicle drag and increase vehicle downforce simultaneously by optimising the shape of the spoiler, by allowing the deformation to achieve the most optimised shape assuming no manufacturing constraint. The OpenFOAM software was used for the solver. A strategy for multi-objective optimisation was proposed by assigning appropriate objective function weight, leading to some possible solutions and Pareto front of the proposed design family. Five optimisation solutions of the non-dominated solution Pareto front resulting from the spoiler shape optimisation are presented, explaining the trade-off between conflicting drag and downforce objectives on the vehicle model. The baseline geometry of the simulation is in good agreement with the experimental measurement. The analysis of the shape changes in the proposed optimisation is deeply investigated in terms of the optimised geometry deformation, velocity contour comparison, recirculating region on the base, pressure coefficient comparison and stream-wise velocity component at the slant region of the model. The adjoint-based optimisation method in the presence study can handle multiple objective optimisations and generate possible optimised spoiler shapes to reduce drag and increase downforce. Free deformation of the shape yields in the unique shapes of the spoiler, enabling to manipulate of the base flow at the rear of the vehicle model.

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Jimenez-Garcia, A., M. Biava, G. N. Barakos, K. D. Baverstock, S. Gates, and P. Mullen. "Tiltrotor CFD Part II - aerodynamic optimisation of tiltrotor blades." Aeronautical Journal 121, no. 1239 (May 2017): 611–36. http://dx.doi.org/10.1017/aer.2017.21.

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ABSTRACTThis paper presents aerodynamic optimisation of tiltrotor blades with high-fidelity computational fluid dynamics. The employed optimisation framework is based on a quasi-Newton method, and the required high-fidelity flow gradients were computed using a discrete adjoint solver. Single-point optimisations were first performed to highlight the contrasting requirements of the helicopter and aeroplane flight regimes. It is then shown how a trade-off blade design can be obtained using a multi-point optimisation strategy. The parametrisation of the blade shape allowed the twist and chord distributions to be modified and a swept tip to be introduced. The work shows how these main blade shape parameters influence the optimal performance of the tiltrotor in helicopter and aeroplane modes, and how an optimised blade shape can increase the overall tiltrotor performance. Moreover, in all the presented cases, the accuracy of the adjoint gradients resulted in a small number of flow evaluations for finding the optimal solution, thus indicating gradient-based optimisation as a viable tool for modern tiltrotor design.

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Paler, Alexandru, and Robert Basmadjian. "Energy Cost of Quantum Circuit Optimisation: Predicting That Optimising Shor’s Algorithm Circuit Uses 1 GWh." ACM Transactions on Quantum Computing 3, no. 1 (March 31, 2022): 1–14. http://dx.doi.org/10.1145/3490172.

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Quantum circuits are difficult to simulate, and their automated optimisation is complex as well. Significant optimisations have been achieved manually (pen and paper) and not by software. This is the first in-depth study on the cost of compiling and optimising large-scale quantum circuits with state-of-the-art quantum software. We propose a hierarchy of cost metrics covering the quantum software stack and use energy as the long-term cost of operating hardware. We are going to quantify optimisation costs by estimating the energy consumed by a CPU doing the quantum circuit optimisation. We use QUANTIFY, a tool based on Google Cirq, to optimise bucket brigade QRAM and multiplication circuits having between 32 and 8,192 qubits. Although our classical optimisation methods have polynomial complexity, we observe that their energy cost grows extremely fast with the number of qubits. We profile the methods and software and provide evidence that there are high constant costs associated to the operations performed during optimisation. The costs are the result of dynamically typed programming languages and the generic data structures used in the background. We conclude that state-of-the-art quantum software frameworks have to massively improve their scalability to be practical for large circuits.

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Cvetkovski, Goga, and Lidija Petkovska. "Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches." Power Electronics and Drives 9, no. 1 (January 1, 2024): 34–49. http://dx.doi.org/10.2478/pead-2024-0003.

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Abstract Optimisation, or optimal design, has become a fundamental aspect of engineering across various domains, including power devices, power systems, and industrial systems. Engineers and academics have been actively involved in optimising these systems to achieve better performance, efficiency, and cost-effectiveness. Optimising electrical machines, including permanent magnet motors, is a complex task. It often involves solving intricate problems with various parameters and constraints. Engineers use different optimisation methods to tackle these challenges. Depending on the specific requirements and goals of a design project, engineers may employ either single-objective or multi-objective optimisation approaches. Single-objective optimisation focuses on optimising a single objective, while multi-objective optimisation considers multiple conflicting objectives. In optimisation, objective functions are mathematical representations of what needs to be optimised. In this case, optimising the efficiency of the motor, reducing cogging torque, and minimising the total weight of active materials are defined as possible objective functions. Genetic algorithms are nature based algorithms that are commonly used in engineering to find optimal solutions to complex problems, including those with multiple objectives. In this paper, after conducting optimisations using different objective functions and methods, a comparative analysis of the results is performed. This helps in understanding the trade-offs and benefits of different design choices. Finite element analysis (FEA) is a computational method used to analyse the physical properties and behaviours of complex structures and systems. In this case, FEA is used to validate and analyse selected optimisation solutions to ensure they meet the desired characteristics and parameters. Overall, this work demonstrates the interdisciplinary nature of engineering, where mathematics, computer science (for optimisation algorithms), and physics (for FEA) converge to improve the performance and efficiency of electrical machines. It also underscores the importance of considering multiple objectives in design processes to find optimal solutions that strike a balance between competing goals.

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Liew, Jaime, Riccardo Riva, Mikkel Friis-Møller, and Tuhfe Göçmen. "Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling." Journal of Physics: Conference Series 2767, no. 9 (June 1, 2024): 092039. http://dx.doi.org/10.1088/1742-6596/2767/9/092039.

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Abstract In the field of wind farm control, wake steering has shown the potential to increase the power output of a wind farm by deflecting wakes away from downstream turbines. However, in some wake steering scenarios, the fatigue damage experienced by the turbines can increase, particularly when the wakes partially overlap a downstream rotor. It is for this reason that fatigue load constraints should be introduced into the control optimisation process. Unfortunately, wind turbine loads are notoriously difficult to predict, requiring expensive aeroelastic simulations. In this study, we present a wind farm control optimisation with load constraints using surrogate models to estimate the fatigue damage of each turbine in a wind farm designed for maximum energy production. We use the state-of-the-art aeroelastic wind farm simulator, HAWC2Farm, to produce a comprehensive data set of fatigue loads, which is then used to train surrogate models for rapid execution during an optimisation loop. The inputs of the surrogate model are chosen using the most significant modes from a proper orthogonal decomposition. Artificial neural networks are used for the surrogate models, and the wind farm control optimisation is carried out using OpenMDAO. Finally, a wind farm control optimisation with load constraints using wake steering is performed. The presented methodology for surrogate modelling and control optimisation is significant to produce accurate set point optimisations for wind farms while recognising the implications to turbine fatigue loads.

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Grundmann, Jens, Niels Schütze, and Franz Lennartz. "Sustainable management of a coupled groundwater–agriculture hydrosystem using multi-criteria simulation based optimisation." Water Science and Technology 67, no. 3 (February 1, 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 neural network (ANN) which emulates a numerical density-dependent groundwater flow model. The impact of agriculture is represented by stochastic crop water production functions (SCWPF). Simulation-based optimisation techniques together with the SCWPF and ANN deliver optimal groundwater abstraction and cropping patterns. To address contradicting objectives, e.g. profit-oriented agriculture vs. sustainable abstraction scenarios, we performed multi-objective optimisations using a multi-criteria optimisation algorithm.

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Mounce, S. R., W. Shepherd, S. Ostojin, M. Abdel-Aal, A. N. A. Schellart, J. D. Shucksmith, and S. J. Tait. "Optimisation of a fuzzy logic-based local real-time control system for mitigation of sewer flooding using genetic algorithms." Journal of Hydroinformatics 22, no. 2 (December 24, 2019): 281–95. http://dx.doi.org/10.2166/hydro.2019.058.

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Abstract Urban flooding damages properties, causes economic losses and can seriously threaten public health. An innovative, fuzzy logic (FL)-based, local autonomous real-time control (RTC) approach for mitigating this hazard utilising the existing spare capacity in urban drainage networks has been developed. The default parameters for the control algorithm, which uses water level-based data, were derived based on domain expert knowledge and optimised by linking the control algorithm programmatically to a hydrodynamic sewer network model. This paper describes a novel genetic algorithm (GA) optimisation of the FL membership functions (MFs) for the developed control algorithm. In order to provide the GA with strong training and test scenarios, the compiled rainfall time series based on recorded rainfall and incorporating multiple events were used in the optimisation. Both decimal and integer GA optimisations were carried out. The integer optimisation was shown to perform better on unseen events than the decimal version with considerably reduced computational run time. The optimised FL MFs result in an average 25% decrease in the flood volume compared to those selected by experts for unseen rainfall events. This distributed, autonomous control using GA optimisation offers significant benefits over traditional RTC approaches for flood risk management.

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McGuinness, Justin, and Gareth Thomas. "Optimisation of wave-power arrays without prescribed geometry over incident wave angle." International Marine Energy Journal 4, no. 1 (March 25, 2021): 1–10. http://dx.doi.org/10.36688/imej.4.1-10.

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This paper describes the optimisation of arrays of wave energy converters (WECs) of point absorber type. The WECs are spherical in shape and operate in heave only. Previous work is extended to an optimisation of array layouts without a prescribed geometry. The objective function is chosen as the mean of the array interaction factor over a prescribed range of incident wave angles. This formulation forces the array to perform optimally over a specified range of wave angle, without direct concern for wavelength variations. Both constrained and unconstrained WEC motions are considered, with constrained optimisations limiting device displacements to two or three times the incident wave amplitude. The increased freedom in this more general optimisation results in a 70% to 140% increase in objective function values compared to the analogous linear array optimisations. As in previous studies of this nature, unconstrained arrays tend to contain closely spaced WECs and larger displacement amplitudes, whereas constrained optimal arrays are more widely spaced. It is shown that the prescribed range of incident wave angle has a great effect on the optimal array layout, with better performance achieved for smaller ranges of wave angle due to better tuning of the array members. A previously identified trade-off in linear arrays, between performance stability to different incident wave parameters, is shown not to apply to general array layouts.

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Rashid, A. N. M. Bazlur, and Tonmoy Choudhury. "Cooperative Co-Evolution and MapReduce." International Journal of Information Technology Project Management 12, no. 1 (January 2021): 29–62. http://dx.doi.org/10.4018/ijitpm.2021010102.

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Real-word large-scale optimisation problems often result in local optima due to their large search space and complex objective function. Hence, traditional evolutionary algorithms (EAs) are not suitable for these problems. Distributed EA, such as a cooperative co-evolutionary algorithm (CCEA), can solve these problems efficiently. It can decompose a large-scale problem into smaller sub-problems and evolve them independently. Further, the CCEA population diversity avoids local optima. Besides, MapReduce, an open-source platform, provides a ready-to-use distributed, scalable, and fault-tolerant infrastructure to parallelise the developed algorithm using the map and reduce features. The CCEA can be distributed and executed in parallel using the MapReduce model to solve large-scale optimisations in less computing time. The effectiveness of CCEA, together with the MapReduce, has been proven in the literature for large-scale optimisations. This article presents the cooperative co-evolution, MapReduce model, and associated techniques suitable for large-scale optimisation problems.

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McIntyre, Melissa, Puthenparampil Wilson, Peter Gorayski, and Eva Bezak. "A Systematic Review of LET-Guided Treatment Plan Optimisation in Proton Therapy: Identifying the Current State and Future Needs." Cancers 15, no. 17 (August 25, 2023): 4268. http://dx.doi.org/10.3390/cancers15174268.

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The well-known clinical benefits of proton therapy are achieved through higher target-conformality and normal tissue sparing than conventional radiotherapy. However, there is an increased sensitivity to uncertainties in patient motion/setup, proton range and radiobiological effect. Although recent efforts have mitigated some uncertainties, radiobiological effect remains unresolved due to a lack of clinical data for relevant endpoints. Therefore, RBE optimisations may be currently unsuitable for clinical treatment planning. LET optimisation is a novel method that substitutes RBE with LET, shifting LET hotspots outside critical structures. This review outlines the current status of LET optimisation in proton therapy, highlighting knowledge gaps and possible future research. Following the PRISMA 2020 guidelines, a search of the MEDLINE® and Scopus databases was performed in July 2023, identifying 70 relevant articles. Generally, LET optimisation methods achieved their treatment objectives; however, clinical benefit is patient-dependent. Inconsistencies in the reported data suggest further testing is required to identify therapeutically favourable methods. We discuss the methods which are suitable for near-future clinical deployment, with fast computation times and compatibility with existing treatment protocols. Although there is some clinical evidence of a correlation between high LET and adverse effects, further developments are needed to inform future patient selection protocols for widespread application of LET optimisation in proton therapy.

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Hamzah, Amir Izuddin, Norshah Aizat Shuaib, Azwan Iskandar Azmi, Nur’ain Wahidah Ya Omar, and Mohd Haidiezul Jamal Ab Hadi. "EFFECT OF BUILD PARAMETERS ON PROCESS PERFORMANCES AND MECHANICAL PROPERTIES OF PRINTED PRODUCT IN FUSED DEPOSITION MODELLING METHOD." International Journal of Modern Manufacturing Technologies 14, no. 2 (December 20, 2022): 77–82. http://dx.doi.org/10.54684/ijmmt.2022.14.2.77.

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Fused deposition modelling performances are dominated by selection of process parameters. Multi objective optimisation is essential in ensuring excellent product mechanical properties, surface quality and resource efficiency. This paper presents a preliminary work based on Taguchi orthogonal array design of experiment, considering build orientation, printing angle and layer thickness as the input factors. The build orientation has a significant influence on tensile strength while the layer thickness on energy consumption and printing time. Adverse effects on the responses can be observed during the attempts. However, two factors optimisations were still achievable. Optimal settings should be suited based on final application and economical constraints. This study has established a groundwork of further studies in optimisation of quality of the method.

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Eisenring, Claudia, Sophy E. Oliver, Samar Khatiwala, and Gregory F. de Souza. "Influence of GEOTRACES data distribution and misfit function choice on objective parameter retrieval in a marine zinc cycle model." Biogeosciences 19, no. 21 (November 7, 2022): 5079–106. http://dx.doi.org/10.5194/bg-19-5079-2022.

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Abstract. Biogeochemical model behaviour for micronutrients is typically hard to constrain because of the sparsity of observational data, the difficulty of determining parameters in situ, and uncertainties in observations and models. Here, we assess the influence of data distribution, model uncertainty, and the misfit function on objective parameter optimisation in a model of the oceanic cycle of zinc (Zn), an essential micronutrient for marine phytoplankton with a long whole-ocean residence time. We aim to investigate whether observational constraints are sufficient for reconstruction of biogeochemical model behaviour, given that the Zn data coverage provided by the GEOTRACES Intermediate Data Product 2017 is sparse. Furthermore, we aim to assess how optimisation results are affected by the choice of the misfit function and by confounding factors such as analytical uncertainty in the data or biases in the model related to either seasonal variability or the larger-scale circulation. The model framework applied herein combines a marine Zn cycling model with a state-of-the-art estimation of distribution algorithm (Covariance Matrix Adaption Evolution Strategy, CMA-ES) to optimise the model towards synthetic data in an ensemble of 26 optimisations. Provided with a target field that can be perfectly reproduced by the model, optimisation retrieves parameter values perfectly regardless of data coverage. As differences between the model and the system underlying the target field increase, the choice of the misfit function can greatly impact optimisation results, while limitation of data coverage is in most cases of subordinate significance. In cases where optimisation to full or limited data coverage produces relatively distinct model behaviours, we find that applying a misfit metric that compensates for differences in data coverage between ocean basins considerably improves agreement between optimisation results obtained with the two data situations.

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Maximov, Jordan, Galya Duncheva, Angel Anchev, Vladimir Dunchev, Vladimir Todorov та Yaroslav Argirov. "Influence of an Ageing Heat Treatment on the Mechanical Characteristics of Iron-Aluminium Bronzes with β-Transformation Obtained via Centrifugal Casting: Modelling and Optimisation". Metals 13, № 12 (24 листопада 2023): 1930. http://dx.doi.org/10.3390/met13121930.

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Aluminium bronzes possess a unique combination of high strength and wear and corrosion resistance in aggressive environments; thus, these alloys find wide application in marine, shipbuilding, aviation, railway, offshore platform applications and other fields. Iron-aluminium bronzes (IABs) are the cheapest and most widely used. When the aluminium content is above 9.4 wt%, IAB is biphasic (i.e., it undergoes β-transformation) and can be subjected to all heat-treatment types, depending on the desired operating behaviour of the bronze component. This article presents correlations (mathematical models) between the primary mechanical characteristics (yield limit, tensile strength, elongation, hardness and impact toughness) and the ageing temperature and time of quench at 920 °C in water of Cu-11Al-6Fe bronze, obtained using the centrifugal casting method. The microstructure evolution was evaluated depending on the ageing temperature and time changes. Overall, the research was conducted in three successive inter-related stages: a one-factor-at-a-time study, planned experiment, and optimisations. Four optimisation tasks, which have the greatest importance for practice, were formulated and solved. The defined multiobjective optimisation tasks were solved by searching for the Pareto-optimal solution approach. The decisions were made through a nondominated sorting genetic algorithm (NSGA-II) using QstatLab. The optimisation results were verified experimentally. Additional samples were made for this purpose, quenched at 920 °C in water and subjected to subsequent ageing with the optimal values of the governing factors (ageing temperature and time) for the corresponding optimisation task. The comparison of the results for the mechanical characteristics with the theoretical optimisation results presents a good agreement.

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Bullen, R. D., and N. L. McKenzie. "Aerodynamic cleanliness in bats." Australian Journal of Zoology 56, no. 5 (2008): 281. http://dx.doi.org/10.1071/zo08059.

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In this paper we examine morphometric attributes of the airframes of 24 species of bat from Western Australia. In particular, we consider anatomical features of the ear, head, body and tail related to lift and drag optimisation as well as airflow separation control. We provide an assessment of the relative cleanliness of the species and a range of lift and drag coefficient values for use in metabolic power output modelling. The species assessed have aerodynamic cleanliness optimisations that are appropriate to the range of Reynolds’ numbers in which bats fly. Head/body relative cleanliness was consistent with, and functionally appropriate to, aspects of species foraging niche such as foraging strategy. Cleanliness of face and fineness ratio of head and body were found to be related to minimum foraging drag. Blending of the wing and body, the presence of a wing/body fillet and the texture of the pelage were found to be important. The aerodynamic optimisation of ears and tail membrane were found to correlate with foraging strategy. The interceptors had optimisations for minimum drag generation consistent with their higher foraging flight speed. Rather than being optimised for minimum drag, the air-superiority bats’ tails and ears were consistent with their highly agile but slower-foraging flight speeds. Surface bats were characterised by the absence of optimisations for low drag. The frugivore plus the nectarivore and the carnivore studied appear to be discrete optimisations.

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Sasse, Jana, Malte Schön, and Christian Hopmann. "Static Mixers Producible by Additive Manufacturing: Novel Rapid Automatic Optimisation and Practical Evaluation." Polymers 14, no. 21 (November 1, 2022): 4646. http://dx.doi.org/10.3390/polym14214646.

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In the extrusion of plastics, the thermal and material homogeneity of the plastic melt at the die entry are of high importance for the extrudate quality. While static mixers are widely used to improve the melt homogeneity, previous attempts at optimisation for reduced pressure loss and improved mixing had to be performed by hand and human experience, limiting the degrees of freedom and efficiency. A new automatic optimisation method based on the open source software OpenFOAM was developed. Using immersed boundary methods, new target functions in the pre-existing routine adjointShapeOptimizationFoam and an additional algorithm checking the suitability for additive manufacturing and fixing the geometry during run-time is presented. The new algorithm is used to optimise an existing static mixer based on an X-type geometry with integrated oil channels, maximising the heat exchange between oil and melt. Based on the results of these simulative optimisations, the best candidates were manufactured using selective laser melting and experimental trials were run. Experimental validation shows that with our optimisation algorithm, a pressure loss reduction of 10% could be achieved. The core melt temperature was reduced by 6 ∘C, improving the thermal homogenisation as well. While the main advantage of this method is the rapid optimisation taking the operating point into account, the trials also showed positive results in off-design operating points. This allows the low-cost design and manufacture of individualised static mixers.

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Bellotti, Renato, Romana Boiger, and Andreas Adelmann. "Fast, Efficient and Flexible Particle Accelerator Optimisation Using Densely Connected and Invertible Neural Networks." Information 12, no. 9 (August 28, 2021): 351. http://dx.doi.org/10.3390/info12090351.

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Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. However, finding optimised operation points for these complex machines is a challenging task due to the large number of parameters involved and the underlying non-linear dynamics. Here, we introduce two families of data-driven surrogate models, based on deep and invertible neural networks, that can replace the expensive physics computer models. These models are employed in multi-objective optimisations to find Pareto optimal operation points for two fundamentally different types of particle accelerators. Our approach reduces the time-to-solution for a multi-objective accelerator optimisation up to a factor of 640 and the computational cost up to 98%. The framework established here should pave the way for future online and real-time multi-objective optimisation of particle accelerators.

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Woods, Freya E. R., Cerys A. Jenkins, Rhys A. Jenkins, Susan Chandler, Dean A. Harris, and Peter R. Dunstan. "Optimised Pre-Processing of Raman Spectra for Colorectal Cancer Detection Using High-Performance Computing." Applied Spectroscopy 76, no. 4 (March 29, 2022): 496–507. http://dx.doi.org/10.1177/00037028221088320.

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Spectral pre-processing is an essential step in data analysis for biomedical diagnostic applications of Raman spectroscopy, allowing the removal of undesirable spectral contributions that could mask biological information used for diagnosis. However, due to the specificity of pre-processing for a given sample type and the vast number of potential pre-processing combinations, optimisation of pre-processing via a manual “trial and error” format is often time intensive with no guarantee that the chosen method is optimal for the sample type. Here we present the use of high-performance computing (HPC) to trial over 2.4 million pre-processing permutations to demonstrate the optimisation on the pre-processing of human serum Raman spectra for colorectal cancer detection. The effect of varying pre-processing order, using extended multiplicative scatter correction, spectral smoothing, baseline correction, binning and normalization was considered. Permutations were assessed on their ability to detect patients with disease using a random forest (RF) algorithm trained with 102 patients (510 spectra) and independently tested with a set of 439 patients (1317 spectra) in a primary care patient cohort. Optimising via HPC enables improved performance in diagnostic abilities, with sensitivity increasing by 14.6%, specificity increasing by 6.9%, positive predictive value increasing by 3.4%, and negative predictive value increasing by 2.4% when compared to a standard pre-processing optimisation. Ultimate values of these metrics are very important for diagnostic adoption, and once diagnostics demonstrate good accuracy these types of optimisations can make a significant difference to roll-out of a test and demonstrating advantages over existing tests. We also provide tips/recommendations for pre-processing optimisation without the use of HPC. From the HPC permutations, recommendations for appropriate parameter constraints for conducting a more basic pre-processing optimisation are also detailed, thus helping model development for researchers not having access to HPC.

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Langenbucher, Achim, Nóra Szentmáry, Alan Cayless, Jascha Wendelstein, and Peter Hoffmann. "Formula constant optimisation techniques including variation of keratometer or corneal refractive index and consideration for classical and modern IOL formulae." PLOS ONE 18, no. 2 (February 24, 2023): e0282213. http://dx.doi.org/10.1371/journal.pone.0282213.

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Background To investigate whether variation of the keratometer/corneal refractive index nK/nC improves the performance (prediction error PE) of classical and a modern intraocular lens (IOL) power calculation formula and further, to establish whether any trend error of PE for corneal radius R could be eliminated using formula constant and nK/nC optimisation. Methods Based on 2 large datasets (1: N = 888 Hoya Vivinex aberration-correcting and 2: N = 822 Alcon SA60AT spherical lens) a classical formula constant optimisation has been performed for the Hoffer Q, Holladay 1, Haigis and Castrop formulae, to minimise the root mean squared (rms) PE (situation A). In two further optimisations, the formula constants and the formula specific nK/nC value were optimised to minimise the rms PE (situation B) or rms PE and trend error of PE for R (situation C). Nonlinear iterative optimisation strategy was applied according to Levenberg-Marquardt. Results Optimising for rms PE and trend error (C) mainly improved the performance of the Holladay 1. The Haigis formula also showed a slight improvement compared to (A). The Hoffer Q formula shows no relevant trend error of PE for R. In contrast, the Holladay shows a positive and the Haigis (and the Castrop a slight) negative trend error of PE for R. The trend error could be fully eliminated by optimising formula constants and nK/nC in (B), but this was at the cost of overall performance in the case of the Holladay 1 formula. Conclusion Classical IOL calculation concepts should be critically examined for potential improvement of formula performance by variation of the empirical nK/nC value defined in the formula. With additional degrees of freedom additional optimisation terms such as trend errors might be considered in new intelligent optimisation strategies.

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Moigne, A. Le, and N. Qin. "Aerofoil profile and sweep optimisation for a blended wing-body aircraft using a discrete adjoint method." Aeronautical Journal 110, no. 1111 (September 2006): 589–604. http://dx.doi.org/10.1017/s0001924000001457.

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Abstract Aerodynamic optimisations of a blended wing-body (BWB) aircraft are presented. A discrete adjoint solver is used to calculate efficiently the gradients, which makes it possible to optimise for a large number of design variables. The optimisations employ either a variable-fidelity method that combines low- and high-fidelity models or a direct sequential quadratic programming (SQP) method. Four Euler optimisations of a BWB aircraft are then presented. The optimisation is allowed to change a series of master sections defining the aircraft geometry as well as the sweep angle on the outer wing for two of the optimisations. Substantial improvements are obtained, not only in the Euler mode but also when the optimised geometries are evaluated using Reynolds-averaged Navier-Stokes solutions. Some interesting features of the optimised wing profiles are discussed.

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Chiu, Louis N. S., Daniel Stojanov, Bernard Rolfe, and Wen Yi Yan. "Effect of Optimisation Parameters in Topology Optimisation." Key Engineering Materials 725 (December 2016): 529–34. http://dx.doi.org/10.4028/www.scientific.net/kem.725.529.

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The Bi-directional Evolutionary Structural Optimisation (BESO) method had been used by many authors for the optimisation of structures. This work sets out to investigate the effect of key optimisation parameters utilized in the BESO method, the evolution rate and the filter radius, on the outcome of the optimisation. An understanding of the interaction of these factors in the optimisation process enables a more efficient way to produce optimised components that can fully capitalise on the capabilities of additive manufacturing.

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Deep, Kusum, and Jagdish Chand Bansal. "Mean particle swarm optimisation for function optimisation." International Journal of Computational Intelligence Studies 1, no. 1 (2009): 72. http://dx.doi.org/10.1504/ijcistudies.2009.025339.

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Noshokaty, Said El. "Shipping optimisation systems (SOS): liner optimisation perspective." International Journal of Shipping and Transport Logistics 5, no. 3 (2013): 237. http://dx.doi.org/10.1504/ijstl.2013.054189.

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Ovesná, J., K. Poláková, L. Kučera, and J. Rulcová. "SNP Typing in Cereals: Comparison of SSCP and SNaPShot Markers Using the Barley Mlo Locus as a Model." Czech Journal of Genetics and Plant Breeding 39, No. 4 (November 23, 2011): 109–12. http://dx.doi.org/10.17221/3727-cjgpb.

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We compared the applicability of SSCP and SNaPShot markers in barley Mlo loci as a model of SNP detection in cereals. Whereas the development of SSCP markers required optimisation steps, the ddNTP primer extension (SNaPShot) procedure based on knowledge of target sequence provided expected results without any previous optimisations. We have shown, that SNPs can be easily scored using the ABI PRISM 310 Genetic Analyser.  

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Das, Debasish, Sanjib Jaypuria, Dilip Kumar Pratihar, and Gour Gopal Roy. "Weld optimisation." Science and Technology of Welding and Joining 26, no. 3 (January 22, 2021): 181–95. http://dx.doi.org/10.1080/13621718.2021.1872856.

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Hall, Julie. "Medicines optimisation." Primary Health Care 23, no. 3 (April 2013): 22–24. http://dx.doi.org/10.7748/phc2013.04.23.3.22.e739.

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Makombe, Rachel. "Medicines optimisation." Nursing Standard 30, no. 44 (June 29, 2016): 64–65. http://dx.doi.org/10.7748/ns.30.44.64.s47.

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Raz, Bing. "Medicines optimisation." Nursing Standard 31, no. 39 (May 24, 2017): 64–65. http://dx.doi.org/10.7748/ns.31.39.64.s48.

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35

McKeever, Luke. "Data optimisation." Journal of Direct, Data and Digital Marketing Practice 9, no. 4 (April 2008): 363–70. http://dx.doi.org/10.1057/dddmp.2008.2.

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36

Delahaye, Jean-Paul. "Optimisation impossible." ESAIM: Mathematical Modelling and Numerical Analysis 19, no. 2 (1985): 213–33. http://dx.doi.org/10.1051/m2an/1985190202131.

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Harten, J., and J. Kinsella. "Perioperative Optimisation." Scottish Medical Journal 49, no. 1 (February 2004): 6–9. http://dx.doi.org/10.1177/003693300404900102.

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Alber, M. "Robust optimisation." Physica Medica 31 (November 2015): e24-e25. http://dx.doi.org/10.1016/j.ejmp.2015.10.006.

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Dowsland, Kathryn, and Bill Dowsland. "Combinatorial Optimisation." OR Insight 15, no. 3 (July 2002): 2–3. http://dx.doi.org/10.1057/ori.2002.10.

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Kemp, Simon. "Simple optimisation." Behavioural Processes 69, no. 2 (May 2005): 131–32. http://dx.doi.org/10.1016/j.beproc.2005.02.011.

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Medoh, Chuks Nnamdi, and Arnesh Telukdarie. "Logistics Optimisation." International Journal of Business Analytics 7, no. 1 (January 2020): 54–76. http://dx.doi.org/10.4018/ijban.2020010104.

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Contemporary multinationals exist in a dynamic digital age in which business units direct enormous attention to technological solutions and business challenges, especially logistics. Business units aim for solutions that are relatively effective to implement, in relation to solving business challenges ensuring sustainability. This research seeks to present value add relative to business process optimisation model based on 4IR (Fourth Industrial Revolution) implementations, specific to multinational logistics optimisation. The onset of the 4IR has advanced businesses significantly, specifically to logistics optimisation. This article assumes a business process-centric modelling approach via industry 4.0 implementations to model and predicts the optimum logistics execution time. This is facilitated based on defined scenarios with all potential variables affecting configured sets of logistics business functions. The results address the present gap related to presenting a process-centric and systemic architecture effective to simulate the impact of change on a business.

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Aickelin, Uwe, and Alistair Clark. "Heuristic optimisation." Journal of the Operational Research Society 62, no. 2 (February 2011): 251–52. http://dx.doi.org/10.1057/jors.2010.160.

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Aspinall, David, Lennart Beringer, and Alberto Momigliano. "Optimisation Validation." Electronic Notes in Theoretical Computer Science 176, no. 3 (July 2007): 37–59. http://dx.doi.org/10.1016/j.entcs.2006.06.017.

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Mohamad Saleh, Junita, and Weng Hooi Tan. "Alligator Optimisation algorithm for solving unconstrainted optimisation problems." International Journal of Bio-Inspired Computation 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijbic.2022.10049859.

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Wei, Zhan Hong, Yu Liu, Guo Xin Zhao, and Yu Bao Song. "Social emotional optimisation algorithm for reactive power optimisation." International Journal of Wireless and Mobile Computing 10, no. 4 (2016): 351. http://dx.doi.org/10.1504/ijwmc.2016.078205.

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Lin, Min, Yiwen Zhong, Juan Lin, and Xiaoyu Lin. "Enhanced chicken swarm optimisation for function optimisation problem." International Journal of Wireless and Mobile Computing 15, no. 3 (2018): 258. http://dx.doi.org/10.1504/ijwmc.2018.096009.

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Lin, Xiaoyu, Juan Lin, Min Lin, and Yiwen Zhong. "Enhanced chicken swarm optimisation for function optimisation problem." International Journal of Wireless and Mobile Computing 15, no. 3 (2018): 258. http://dx.doi.org/10.1504/ijwmc.2018.10017126.

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Ramezani, Pezhman, Milad Ahangaran, and Xin She Yang. "Constrained optimisation and robust function optimisation with EIWO." International Journal of Bio-Inspired Computation 5, no. 2 (2013): 84. http://dx.doi.org/10.1504/ijbic.2013.053505.

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Jordehi, A. Rezaee. "Seeker optimisation (human group optimisation) algorithm with chaos." Journal of Experimental & Theoretical Artificial Intelligence 27, no. 6 (April 18, 2015): 753–62. http://dx.doi.org/10.1080/0952813x.2015.1020568.

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Tan, Weng Hooi, and Junita Mohamad Saleh. "Alligator optimisation algorithm for solving unconstrainted optimisation problems." International Journal of Bio-Inspired Computation 21, no. 1 (2023): 11. http://dx.doi.org/10.1504/ijbic.2023.130025.

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