Journal articles: 'Dynamics of optimization'

1

Boettcher, Stefan, and Allon G. Percus. "Optimization with Extremal Dynamics." Physical Review Letters 86, no. 23 (June 4, 2001): 5211–14. http://dx.doi.org/10.1103/physrevlett.86.5211.

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Bennett, J. A., and G. J. Park. "Automotive Occupant Dynamics Optimization." Shock and Vibration 2, no. 6 (1995): 471–79. http://dx.doi.org/10.1155/1995/682694.

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One of the more difficult optimal design tasks occurs when the data describing the system to be optimized is either highly nonlinear or noisy or both. This situation arises when trying to design restraint systems for automotive crashworthiness using the traditional lumped parameter analysis methods. The nonlinearities in the response can come from either abrupt changes in the occupants interaction with the interior or from relatively minor fluctuation in the response due to the interactions of two restraint systems such as belts and airbags. In addition the calculated response measures are usually highly nonlinear functions of the accelerations. Two approaches using an approximate problem formulation strategy are proposed. One approach uses a first-order approximation based on finite difference derivatives with a nonlocal step size. The second and more effective approach uses a second-order curve fitting strategy. Successful example problems of up to 16 design variables are demonstrated. A conservative design strategy using a derivative-based constraint padding is also discussed. The approach proves effective because analytical expressions are available for the second-order terms.

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Tamura, Kenichi, and Keiichiro Yasuda. "Spiral Dynamics Inspired Optimization." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 8 (October 20, 2011): 1116–22. http://dx.doi.org/10.20965/jaciii.2011.p1116.

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We recently proposed a new multipoint search method for 2-dimensional continuous optimization problems based on an analogy of spiral phenomena called 2-dimensional spiral optimization. Focused spiral phenomena, which appear frequently in nature, are approximated to logarithmic spirals. Two-dimensional spiral optimization used a feature of logarithmic spirals. In this paper, we proposen-dimensional spiral optimization by extending the 2-dimensional one. The n-dimensional spiral model is constructed based on rotation matrices defined inn-dimensional space. Simulation results for different benchmark problems show the effectiveness of our proposal compared to other metaheuristics.

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Gay-Balmaz, François, Darryl D. Holm, and Tudor S. Ratiu. "Geometric dynamics of optimization." Communications in Mathematical Sciences 11, no. 1 (2013): 163–231. http://dx.doi.org/10.4310/cms.2013.v11.n1.a6.

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Barettin, Daniele, and Paolo Sibani. "Optimization by record dynamics." Computer Physics Communications 185, no. 3 (March 2014): 730–35. http://dx.doi.org/10.1016/j.cpc.2013.10.030.

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Boettcher, Stefan, and Allon G. Percus. "Optimization with extremal dynamics." Complexity 8, no. 2 (November 2002): 57–62. http://dx.doi.org/10.1002/cplx.10072.

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Schuster, Peter, and Karl Sigmund. "Dynamics of Evolutionary Optimization." Berichte der Bunsengesellschaft für physikalische Chemie 89, no. 6 (June 1985): 668–82. http://dx.doi.org/10.1002/bbpc.19850890620.

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Zhang, Jing, Xiaokai Zhu, Te Chen, and Guowei Dou. "Optimal Dynamics Control in Trajectory Tracking of Industrial Robots Based on Adaptive Gaussian Pseudo-Spectral Algorithm." Algorithms 18, no. 1 (January 3, 2025): 18. https://doi.org/10.3390/a18010018.

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A pseudo-spectral control algorithm based on adaptive Gauss collocation point reconstruction is proposed to efficiently solve the optimal dynamics control problem of industrial robots. A mathematical model for the kinematic relationship and dynamic optimization control of industrial robots has been established. On the basis of deriving the Legendre–Gauss collocation formula, a two-stage adaptive Gauss collocation strategy for industrial robot dynamics control variables was designed to improve the dynamics optimization control effect of industrial robot by improving the solution efficiency of constrained optimization problems. The results show that compared with the control variable parameterization method and the traditional Gaussian pseudo-spectral method, the proposed dynamic optimal control method based on an adaptive Gaussian point reconstruction algorithm can effectively improve the solving time and efficiency of constrained optimization problems, thereby further enhancing the dynamic optimization control and trajectory tracking effect of industrial robots.

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Lurie, K. A. "MATERIAL OPTIMIZATION AND DYNAMIC MATERIALS." Cybernetics and Physics, Volume 10, 2021, Number 2 (October 1, 2021): 84–87. http://dx.doi.org/10.35470/2226-4116-2021-10-2-84-87.

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The paper is about the connection between material optimization in dynamics and a novel concept of dynamic materials (DM) defined as inseparable union of a framework and the fluxes of mass, momentum, and energy existing in time dependent material formations. An example of a spatial-temporal material geometry is discussed as illustration of a DM capable of accumulating wave energy. Finding the optimal material layouts in dynamics demonstrates conceptual difference from a similar procedure in statics. In the first case, the original constituents are distributed in space-time, whereas in the second - in space alone. The habitual understanding of a material as an isolated framework has come from statics, but a transition to dynamics brings in a new component - the fluxes of mass, momentum, and energy. Based on Noether theorem, these fluxes connect the framework with the environment into inseparable entity termed dynamic material (DM). The key role of DM is that they support controls that may purposefully change the material properties in both space and time, which is the main goal of optimization.

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Jiang, Shuai, Yuanpeng Lin, Jianan Liu, Linjing Xiao, and Shuaishuai Zhang. "Dynamics Optimization Research and Dynamics Accuracy and Reliability Analysis of a Multi-Link Mechanism with Clearances." Machines 10, no. 8 (August 16, 2022): 698. http://dx.doi.org/10.3390/machines10080698.

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With the development of high-speed and lightweight mechanisms, and the continuous improvement of manufacturing accuracy requirements in industrial production, clearance joints have increasingly become one of the key factors affecting dynamics performance. Poor clearance will seriously compromise stability, accuracy, and dynamics performance. Based on a genetic algorithm, an efficient modeling methodology for the dynamics optimization of a planar complex multi-link mechanism containing multiple clearance joints is put forward. The model comprises a 2-degree of freedom (DOF) nine-bar mechanism that can be used as the main transmission mechanism of a hybrid drive multi-link press, which is taken as the research object. The optimization objective is to minimize the maximum acceleration of the slider and minimize the difference between the actual central trajectory and the ideal trajectory. By optimizing the quality parameters of key components, an optimal solution for the design parameters is obtained, and the effects of the different optimizations of the objective functions on dynamics response are compared and analyzed. At the same time, a new modeling and calculation methodology of the dynamics accuracy and reliability of a complex multi-link mechanism in terms of multiple clearances is proposed, and the effect of optimization on dynamics accuracy and the reliability of the mechanism is analyzed. Based on the optimization results obtained by taking the minimum difference between the actual center trajectory and the ideal trajectory as an optimization objective, the nonlinear characteristics before and after optimization are analyzed through a phase diagram and Poincaré map. A test platform was built to study the dynamics of the mechanism with clearances. Research not only provides a basis for the dynamics optimization of a multi-link mechanism containing clearances but also provides reference significance for the reliability analysis of a multi-link mechanism containing clearances.

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Wiskott, Laurenz, and Terrence Sejnowski. "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization." Neural Computation 10, no. 3 (April 1, 1998): 671–716. http://dx.doi.org/10.1162/089976698300017700.

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Computational models of neural map formation can be considered on at least three different levels of abstraction: detailed models including neural activity dynamics, weight dynamics that abstract from the neural activity dynamics by an adiabatic approximation, and constrained optimization from which equations governing weight dynamics can be derived. Constrained optimization uses an objective function, from which a weight growth rule can be derived as a gradient flow, and some constraints, from which normalization rules are derived. In this article, we present an example of how an optimization problem can be derived from detailed nonlinear neural dynamics. A systematic investigation reveals how different weight dynamics introduced previously can be derived from two types of objective function terms and two types of constraints. This includes dynamic link matching as a special case of neural map formation. We focus in particular on the role of coordinate transformations to derive different weight dynamics from the same optimization problem. Several examples illustrate how the constrained optimization framework can help in understanding, generating, and comparing different models of neural map formation. The techniques used in this analysis may also be useful in investigating other types of neural dynamics.

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Poitras, Gérard Jacques, Gabriel Cormier, and Armel Stanislas Nabolle. "Peloton Dynamics Optimization: Algorithm for Discrete Structural Optimization." Journal of Structural Engineering 147, no. 10 (October 2021): 04021164. http://dx.doi.org/10.1061/(asce)st.1943-541x.0003113.

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Liu, Yongguang, Saisai Tong, and Xiaohui Gao. "Optimization of swash plate multistage compressor based on dynamics." Journal of Physics: Conference Series 2741, no. 1 (April 1, 2024): 012038. http://dx.doi.org/10.1088/1742-6596/2741/1/012038.

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Abstract The swash plate compressor can provide high-pressure gas through multistage compression using its different cylinders, which is an ideal structure for high-pressure compressor miniaturization. The swash plate multistage compressor (SPMC) with variable sections cylinder and inter-stage cooler is proposed and its complex structure results in complex multi-body dynamics and thermodynamics. To research the dynamic characteristics of SPMC, the dynamic model of SPMC is established, which includes the thermodynamics of compression and inter-stage cooler and the dynamics of all the check valves and pistons. The optimization objective function considering the dynamic characteristics and efficiency is proposed. The constraint conditions and sensitivity of the optimized parameters are studied and the final parameters are achieved through an optimization algorithm. Based on the above work, it can be found that the optimization way has high convergence and accuracy, and the comprehensive performance of SPMC is improved significantly.

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Batay, Sagidolla, Bagdaulet Kamalov, Dinmukhamed Zhangaskanov, Yong Zhao, Dongming Wei, Tongming Zhou, and Xiaohui Su. "Adjoint-Based High-Fidelity Concurrent Aerodynamic Design Optimization of Wind Turbine." Fluids 8, no. 3 (February 28, 2023): 85. http://dx.doi.org/10.3390/fluids8030085.

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To evaluate novel turbine designs, the wind energy sector extensively depends on computational fluid dynamics (CFD). To use CFD in the design optimization process, where lower-fidelity approaches such as blade element momentum (BEM) are more popular, new tools to increase the accuracy must be developed as the latest wind turbines are larger and the aerodynamics and structural dynamics become more complex. In the present study, a new concurrent aerodynamic shape optimization approach towards multidisciplinary design optimization (MDO) that uses a Reynolds-averaged Navier–Stokes solver in conjunction with a numerical optimization methodology is introduced. A multidisciplinary design optimization tool called DAFoam is used for the NREL phase VI turbine as a baseline geometry. Aerodynamic design optimizations in terms of five different schemes, namely, cross-sectional shape, pitch angle, twist, chord length, and dihedral optimization are conducted. Pointwise, a commercial mesh generator is used to create the numerical meshes. As the adjoint approach is strongly reliant on the mesh quality, up to 17.8 million mesh cells were employed during the mesh convergence and result validation processes, whereas 2.65 million mesh cells were used throughout the design optimization due to the computational cost. The Sparse Nonlinear OPTimizer (SNOPT) is used for the optimization process in the adjoint solver. The torque in the tangential direction is the optimization’s merit function and excellent results are achieved, which shows the promising prospect of applying this approach for transient MDO. This work represents the first attempt to implement DAFoam for wind turbine aerodynamic design optimization.

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Slavković, Radomir. "Rigid body dynamics in optimization of the machine tool vibroisolation." Tehnicki vjesnik - Technical Gazette 22, no. 1 (2015): 87–94. http://dx.doi.org/10.17559/tv-20130918225023.

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Tang, Quan, and Peng Wang. "Quantum Dynamics Framework with Quantum Tunneling Effect for Numerical Optimization." Entropy 27, no. 2 (February 1, 2025): 150. https://doi.org/10.3390/e27020150.

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In recent years, optimization algorithms have developed rapidly, especially those which introduce quantum ideas, which perform excellently. Inspired by quantum thought, this paper proposes a quantum dynamics framework (QDF) which converts optimization problems into the problem of the constrained ground state of the quantum system and analyzes optimization algorithms by simulating the dynamic evolution process of physical optimization systems in the ground state. Potential energy equivalence and Taylor expansion are performed on the objective function to obtain the basic iterative operations of optimization algorithms. Furthermore, a quantum dynamics framework based on the quantum tunneling effect (QDF-TE) is proposed which adopts dynamic multiple group collaborative sampling to improve the quantum tunneling effect of the QDF, thereby increasing the population diversity and improving algorithm performance. The probability distribution of solutions can be visually observed through the evolution of the wave function, which also indicates that the QDF-TE can strengthen the tunneling effect. The QDF-TE was evaluated on the CEC 2017 test suite and shown to be competitive with other heuristic optimization algorithms. The experimental results reveal the effectiveness of introducing a quantum mechanism to analyze and improve optimization algorithms.

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Youlun, Xiong, and Ding Han. "Optimization of manipulator dynamics in non extreme design case." Robotica 8, no. 2 (April 1990): 131–36. http://dx.doi.org/10.1017/s0263574700007700.

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SummaryThis paper is concerned with the dynamic merit of robot manipulators and method for the optimization of manipulator dynamics in the case of an extremely bad design, which can be put into three categories: 1. Acceleration performance: minimize 2. Velocity performance: minimize 3. Comprehensive performance: minimize , and are the dynamics indices of API, VPI and CD PI under various conditions respectively.The exchange algorithm is presented and a procedure is developed to solve this kind of Minimax problem. An example is discussed to illustrate the application of the optimization method. The large uniform magnitude of a joint relative driving torque provided by this dynamics optimization procedure will be made into a large work space and large torques.

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Angelova, J., and A. Dishliev. "Optimization problems in population dynamics." Applicable Analysis 69, no. 3-4 (July 1998): 207–21. http://dx.doi.org/10.1080/00036819808840657.

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de Souza, Robert, Song Zice, and Liu Chaoyang. "Supply chain dynamics and optimization." Integrated Manufacturing Systems 11, no. 5 (September 2000): 348–64. http://dx.doi.org/10.1108/09576060010335627.

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Ovchinnikov, Igor V., and Kang L. Wang. "Stochastic dynamics and combinatorial optimization." Modern Physics Letters B 31, no. 31 (November 6, 2017): 1750285. http://dx.doi.org/10.1142/s0217984917502852.

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Natural dynamics is often dominated by sudden nonlinear processes such as neuroavalanches, gamma-ray bursts, solar flares, etc., that exhibit scale-free statistics much in the spirit of the logarithmic Ritcher scale for earthquake magnitudes. On phase diagrams, stochastic dynamical systems (DSs) exhibiting this type of dynamics belong to the finite-width phase (N-phase for brevity) that precedes ordinary chaotic behavior and that is known under such names as noise-induced chaos, self-organized criticality, dynamical complexity, etc. Within the recently proposed supersymmetric theory of stochastic dynamics, the N-phase can be roughly interpreted as the noise-induced “overlap” between integrable and chaotic deterministic dynamics. As a result, the N-phase dynamics inherits the properties of the both. Here, we analyze this unique set of properties and conclude that the N-phase DSs must naturally be the most efficient optimizers: on one hand, N-phase DSs have integrable flows with well-defined attractors that can be associated with candidate solutions and, on the other hand, the noise-induced attractor-to-attractor dynamics in the N-phase is effectively chaotic or aperiodic so that a DS must avoid revisiting solutions/attractors thus accelerating the search for the best solution. Based on this understanding, we propose a method for stochastic dynamical optimization using the N-phase DSs. This method can be viewed as a hybrid of the simulated and chaotic annealing methods. Our proposition can result in a new generation of hardware devices for efficient solution of various search and/or combinatorial optimization problems.

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Carmichael, D. G. "Structural optimization and system dynamics." Structural Optimization 2, no. 2 (June 1990): 105–8. http://dx.doi.org/10.1007/bf01745457.

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ZAVADSKY, SERGEY V., DMITRI A. OVSYANNIKOV, and SHENG-LUEN CHUNG. "PARAMETRIC OPTIMIZATION METHODS FOR THE TOKAMAK PLASMA CONTROL PROBLEM." International Journal of Modern Physics A 24, no. 05 (February 20, 2009): 1040–47. http://dx.doi.org/10.1142/s0217751x09044486.

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Mathematical models of the structural parametric optimization of plasma dynamics are discussed. Optimization approach to plasma dynamic is based on the consideration of trajectory ensemble. This ensemble describes transient process in tokamak subject to the initial data and external disturbances. In the framework of this approach the optimization of dynamics of the trajectory ensemble in ITER tokamak is given. The trajectories of this ensemble are perturbed at the initial point set and the set of external disturbances.

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Gagnol, Vincent, Belhassen C. Bouzgarrou, Pascal Ray, and Christian Barra. "Stability-Based Spindle Design Optimization." Journal of Manufacturing Science and Engineering 129, no. 2 (November 8, 2006): 407–15. http://dx.doi.org/10.1115/1.2673400.

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Prediction of stable cutting regions is a critical requirement for high-speed milling operations. These predictions are generally made using frequency-response measurements of the tool-holder-spindle set obtained from a nonrotating spindle. However, significant changes in system dynamics occur during high-speed rotation. In this paper, a dynamic high-speed spindle-bearing system model is elaborated on the basis of rotor dynamics prediction and readjusted on the basis of experimental modal identification. The dependency of dynamic behavior on speed range is then investigated and determined with accuracy. Dedicated experiments are carried out in order to confirm model results. They show that dynamic effects due to high rotational speed and elastic deformations, such as gyroscopic coupling and spin softening, have a significant influence on spindle behavior. By integrating the modeled speed-dependent spindle transfer function in the chatter vibration stability approach of Altintas and Budak (1995, CIRPS Ann, 44(1), pp. 357–362), a new dynamic stability lobe diagram is predicted. Significant changes are observed in the stability limits constructed using the proposed approach and allow accurate prediction of cutting conditions to be established. Finally, optimization studies are performed on spindle design parameters in order to obtain a chatter vibration-free cutting operation at the desired speed and depth of cut for a given cutter.

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Deng, Zhaoxue, Xinxin Wei, Xingquan Li, Shuen Zhao, and Sunke Zhu. "Design and multi-objective optimization of magnetorheological damper considering vehicle riding comfort and operation stability." Journal of Intelligent Material Systems and Structures 33, no. 9 (October 9, 2021): 1215–28. http://dx.doi.org/10.1177/1045389x211048223.

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Mostly, magnetorheological (MR) dampers were optimized based on individual performance, without considering the influence of structure parameters change on vehicle performance. Therefore, a multi-objective optimization scheme of MR damper based on vehicle dynamics model was proposed. The finite element method was used to analyze magnetic flux density distribution in tapered damping channel under different structure parameters. Furthermore, the damping force expression of the tapered flow mode MR damper was derived, and the damping force was introduced into the vehicle dynamics model. In order to improve the ride comfort and operation stability of the vehicle, a collaborative optimization platform combining magnetic circuit finite element analysis and vehicle dynamics model was established. Based on this platform, the optimal design variables were determined by comfort and stability sensitivity analysis. The time domain optimization objective and frequency domain optimization objective are proposed simultaneously to overcome the lack of time domain optimization objective. The results show that compared with the time domain optimization and the initial design, the suspension dynamic deflection, tire dynamic load and vehicle body vertical acceleration are decreased after the time-frequency optimization. At the same time, in the frequency domain, the amplitude of vibration acceleration in each working condition is significantly reduced.

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Dastyar, Haniyeh, Daniel Rippel, and Michael Freitag. "Optimization of Supplier Development under Market Dynamics." Mathematical Problems in Engineering 2020 (March 20, 2020): 1–18. http://dx.doi.org/10.1155/2020/2912380.

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Over the last decades, supplier development has become an increasingly important concept to remain competitive in today’s markets. Therefore, manufacturers invest resources in their suppliers to increase their abilities and, ultimately, to reduce their product prices. Thereby, most approaches found in the literature focus on long-term supplier development programs. Nevertheless, today’s volatile and dynamic markets require flexible approaches to deal with this complexity. We apply Model Predictive Control to optimize the number of supplier development projects in order to achieve flexibility while maintaining a certain level of security for all parties. Thereby, the article focusses on a multimanufacturer scenario, where two manufacturers aim to develop the same supplier. These manufacturers can establish different levels of horizontal collaboration. While previous results already show the benefits of applying this approach to a static scenario, this article extends this formulation by introducing market dynamics in the numerical simulations as well as into the optimization approach. Thus, the article proposes to derive regression models using real-world data. The article evaluates the effects of real-world market dynamics on two use cases: an automotive use case and a use case from the mobile phone sector. The results show that assuming market dynamics during the optimization leads to increased or at least close-to-equal revenues across the involved partners. The average increase ranges from approximately 1% to 5% depending on the type and magnitude of the dynamics. Thereby, the results differ depending on the selected collaboration scheme. While a full-cooperative collaboration scheme benefits the least from regarding dynamics in the optimization, it results in the highest overall revenue across all partners.

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Yin, Zongjun, Ru Wang, Xuegang Ma, and Rong Su. "Fractional-Order PIλDµ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles." World Electric Vehicle Journal 15, no. 5 (April 26, 2024): 184. http://dx.doi.org/10.3390/wevj15050184.

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The suspension system is a crucial part of an electric vehicle, which directly affects its handling performance, driving comfort, and driving safety. The dynamics of the 8-DoF full-vehicle suspension with seat active control are established based on rigid-body dynamics, and the time-domain stochastic excitation model of four tires is constructed by the filtered white noise method. The suspension dynamics model and road surface model are constructed on the Matlab/Simulink simulation software platform, and the simulation study of the dynamic characteristics of active suspension based on the fractional-order PIλDµ control strategy is carried out. The three performance indicators of acceleration, suspension dynamic deflection, and tire dynamic displacement are selected to construct the fitness function of the genetic algorithm, and the structural parameters of the fractional-order PIlDm controller are optimized using the genetic algorithm. The control effect of the optimized fractional-order PIlDm controller based on the genetic algorithm is analyzed by comparing the integer-order PID control suspension and passive suspension. The simulation results show that for optimized fractional-order PID control suspension, compared with passive suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions reaches over 25%, the average optimization of suspension dynamic deflection exceeds 30%, and the average optimization of tire dynamic displacement is 5%. However, compared to the integer-order PID control suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions decreased by 5%, the average optimization of suspension dynamic deflection increased by 3%, and the average optimization of tire dynamic displacement increased by 2%.

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Merkle, Daniel, and Martin Middendorf. "Modeling the Dynamics of Ant Colony Optimization." Evolutionary Computation 10, no. 3 (September 2002): 235–62. http://dx.doi.org/10.1162/106365602760234090.

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The dynamics of Ant Colony Optimization (ACO) algorithms is studied using a deterministic model that assumes an average expected behavior of the algorithms. The ACO optimization metaheuristic is an iterative approach, where in every iteration, artificial ants construct solutions randomly but guided by pheromone information stemming from former ants that found good solutions. The behavior of ACO algorithms and the ACO model are analyzed for certain types of permutation problems. It is shown analytically that the decisions of an ant are influenced in an intriguing way by the use of the pheromone information and the properties of the pheromone matrix. This explains why ACO algorithms can show a complex dynamic behavior even when there is only one ant per iteration and no competition occurs. The ACO model is used to describe the algorithm behavior as a combination of situations with different degrees of competition between the ants. This helps to better understand the dynamics of the algorithm when there are several ants per iteration as is always the case when using ACO algorithms for optimization. Simulations are done to compare the behavior of the ACO model with the ACO algorithm. Results show that the deterministic model describes essential features of the dynamics of ACO algorithms quite accurately, while other aspects of the algorithms behavior cannot be found in the model.

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Li, Chuan Jun, and Zhan Fu Zhao. "Based on Dynamic Simulation Optimization of CNC Cutting Parameter Database System is the Key Technology Research." Advanced Materials Research 915-916 (April 2014): 26–30. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.26.

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Put forward a kind of based on B/S three layer architecture model of cutting parameter database system, the establishment of a complete effective cutting data model used to describe the dynamics simulation optimization of the parameters in the software and the other elements in the process of cutting.At the same time, the establishment of a database contact dynamics simulation optimization software, realized the database support for the typical dynamic simulation optimization software, the development of the corresponding database management system, and completed the cutting data transfer between different data structure.

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Shin, Eungsoo, and D. A. Streit. "Harmonic Synthesis Design Methodology for Dynamic Spring Balancing." Journal of Dynamic Systems, Measurement, and Control 118, no. 4 (December 1, 1996): 733–40. http://dx.doi.org/10.1115/1.2802350.

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A new spring balancing technique, called a two-phase optimization method, is presented. Phase 1 uses harmonic synthesis to provide a system configuration which achieves an approximation to a desired dynamic system response. Phase 2 uses results of harmonic synthesis as initial conditions for dynamic system optimization. Optimization techniques compensate for nonlinearities in machine dynamics. Example applications to robot manipulators and to walking machine legs are presented and discussed.

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Chen, Xiu Long, Shuai Shuai Jia, Chong Jie Du, and Shi Bo Shen. "Elastic Dynamics Optimization Design of Novel High-Speed Spatial Parallel Robot." Advanced Materials Research 415-417 (December 2011): 39–42. http://dx.doi.org/10.4028/www.scientific.net/amr.415-417.39.

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In order to improve performance of 4-UPS-RPS high-speed spatial parallel robot, the elastic dynamics optimization design was executed. The influence law of the mass of moving platform and the diameter of driving limbs on the dynamic performance, which includes kinematics output response and maximum dynamic stress of driving limbs, were discussed. On these grounds, the mass of moving platform and the diameter of driving limbs were optimized. The simulation results show obvious improvement of the dynamics performance for 4-UPS-RPS parallel robot, therefore the theoretical basis are provided.

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Yunpeng, Du, Li Zhaoyong, Wang Ziqiang, Zhao Zhiyong, Zhang Jing, Du Meijie, Zhang Yan, and Zhang Liqiong. "Optimization design of cartridge adapter based on launch dynamics." Journal of Physics: Conference Series 2478, no. 12 (June 1, 2023): 122077. http://dx.doi.org/10.1088/1742-6596/2478/12/122077.

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Abstract To study the influence of the cartridge adapter on the initial disturbance of the rocket shell launch, the dynamic simulation model of the interaction between the cartridge and the storage and distribution box during the rocket shell launch is established. With the aim of reducing the initial disturbance, the structure optimization and design parameters of the cartridge adapter are obtained fro m the dynamics simulation optimization analysis of rocket shell launch. The optimization results suggest that the number and layout of the cartridge adapter can greatly reduce the initial disturbance to launch and the optimization of cartridge adapter can greatly reduce the initial disturbance to rocket shell. The optimization method and results can provide reference for the design of rocket shell.

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Guerrero-Peña, Elaine, Aluízio Araújo, and Cícero Garrozi. "Dynamic Multi-Objective Evolutionary Algorithms: An Overview." Learning and Nonlinear Models 21, no. 2 (December 31, 2023): 55–82. http://dx.doi.org/10.21528/lnlm-vol21-no2-art5.

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Evolutionary algorithms have been widely explored and applied in optimization problems. The introduction of multi-objective evolutionary algorithms (MOEAs) has facilitated the adaptation and creation of new methods to handle more complex and realistic optimizations, such as dynamic multi-objective optimization problems (DMOPs). A dynamic MOEA (DMOEA) can be constructed by changing the MOEA structure and variation operators used to solve DMOPs. Furthermore, DMOEAs can implement change-detection strategies and mechanisms to handle the dynamics of the environment. DMOEAs are often designed to solve unconstrained DMOPs. However, several studies have been conducted to solve dynamic constrained MOPs and more recently, to solve dynamic many-objective optimization problems. There are many real-world DMOPs; however, few DMOEAs have been evaluated with respect to those problems. Evaluation of algorithm performance is an essential aspect of using DMOEAs. Several measures used to validate MOEAs have been adapted to assess DMOEAs and quantitatively compare them. This paper presents a broad review of DMOEAs. The information is summarized and organized according to current research branches. Furthermore, a more general classification scheme for DMOEAs is proposed, based on the method of incorporating diversity to solve DMOPs.

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OVSYANNIKOV, ALEXANDER D., DMITRI A. OVSYANNIKOV, MIKHAIL YU. BALABANOV, and SHENG-LUEN CHUNG. "ON THE BEAM DYNAMICS OPTIMIZATION PROBLEM." International Journal of Modern Physics A 24, no. 05 (February 20, 2009): 941–51. http://dx.doi.org/10.1142/s0217751x09044401.

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The problem of dynamics optimization for charged particle beams in RFQ accelerators is discussed in this article. The problems of the optimization of particle capture into the acceleration mode are analyzed. Accelerators with low injection energies are considered.

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Bryer, Giles, and Christopher Eccles. "Structural Optimization for Vehicle Dynamics Loadcases." SAE International Journal of Passenger Cars - Electronic and Electrical Systems 4, no. 1 (April 12, 2011): 24–29. http://dx.doi.org/10.4271/2011-01-0058.

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Natig Abbasov, Nazim Hasanov, Natig Abbasov, Nazim Hasanov. "OPTİMİZATİON AND DYNAMİCS OF DİSTİLLATİON COLUMN." ETM - Equipment, Technologies, Materials 11, no. 03 (May 23, 2022): 20–30. http://dx.doi.org/10.36962/etm11032022-20.

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Ever increasing global energy demand necessitates the need to enhance energy generation and efficient utilization. As energy prices continue to rise, energy conservation is the prime concern for every industry. Distillation is probably the most studied unit operation in terms of optimization and control. Since distillation columns are major energy-intensive units in chemical plants and given the recent increase in fuel cost, the plant engineers are obliged to find better operating strategies and control systems. One of the significant sections that can be worked upon is finding the optimum feed tray locations of distillation columns. The energy consumptions during distillation can have a strong impact on overall profitability. Hence, the optimization of a column is aimed towards greater production and an increase in quality. Since a column is multivariable system, disturbance in any parameter can affect the overall performance as well as production loss. This paper shows how an acetone manufacturing process can be conveniently simulated first to get temperature, pressure and composition profile followed by application of optimization techniques to enhance performance. It was performed by the means of using a simulator Aspen Plus®. Keywords: distillation Simulation, column optimization, dynamic modeling, energy consumptions, energy-intensive units.

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Chen, C. N., C. I. Chou, C. R. Hwang, J. Kang, T. K. Lee, and S. P. Li. "Monte Carlo dynamics in global optimization." Physical Review E 60, no. 2 (August 1, 1999): 2388–93. http://dx.doi.org/10.1103/physreve.60.2388.

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Boker, Steven M. "Selection, Optimization, Compensation, and Equilibrium Dynamics." GeroPsych 26, no. 1 (January 2013): 61–73. http://dx.doi.org/10.1024/1662-9647/a000081.

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One of the major theoretic frameworks through which human development is studied is a process-oriented model involving selection, optimization, and compensation. These three processes each provide accounts for methods by which gains are maximized and losses minimized throughout the lifespan, in particular during later life. These processes can be cast within the framework of dynamical systems theory and then modeled using differential equations. The current article will review basic tenets of selection, optimization, and compensation while introducing language and concepts from dynamical systems. Four categories of interindividual differences and intraindividual variability in dynamics are then described and discussed in the context of selection, optimization, and compensation.

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Ostov, Yu Ya, and A. P. Ivanov. "Method of optimization in flight dynamics." Automation and Remote Control 75, no. 2 (February 2014): 294–301. http://dx.doi.org/10.1134/s000511791402009x.

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YAMAMOTO, Takeshi. "Computational Fluid Dynamics and Design Optimization." Journal of the Society of Mechanical Engineers 105, no. 999 (2002): 63. http://dx.doi.org/10.1299/jsmemag.105.999_63.

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TSIOUTSIAS, DIMITRIS I., and ERIC MJOLSNESS. "OPTIMIZATION DYNAMICS FOR PARTITIONED NEURAL NETWORKS." International Journal of Neural Systems 05, no. 04 (December 1994): 275–86. http://dx.doi.org/10.1142/s0129065794000281.

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Given a relaxation-based neural network and a desired partition of the neurons in the network into modules with relatively slow communication between modules, we investigate relaxation dynamics for the resulting partitioned neural network. In particular, we show how the slow inter-module communication channels can be modeled by means of certain transformations of the original objective function which introduce new state variables for the inter-module communication links. We report on a parallel implementation of the resulting relaxation dynamics, for a two-dimensional image segmentation network, using a network of workstations. Experiments demonstrate a functional and efficient parallelization of this neural network algorithm. We also discuss implications for analog hardware implementations of relaxation networks.

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Dinh, Quang Vinh, Gilbert Rogé, Cyrille Sevin, and Bruno Stoufflet. "Shape optimization in computational fluid dynamics." Revue Européenne des Éléments Finis 5, no. 5-6 (January 1996): 569–94. http://dx.doi.org/10.1080/12506559.1996.10511238.

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Zhou, Guanglu, Qun Lin, Ryan Loxton, and Sheng-Jie Li. "Dynamics, Control, and Optimization with Applications." Abstract and Applied Analysis 2013 (2013): 1. http://dx.doi.org/10.1155/2013/352512.

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Masad, J. A. "Optimization methods with structural dynamics applications." Computers & Structures 62, no. 3 (February 1997): 521–25. http://dx.doi.org/10.1016/s0045-7949(96)00221-0.

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Tudose, Lucian, Cristina Stǎnescu, and András Sóbester. "Improving machine dynamics via geometry optimization." Structural and Multidisciplinary Optimization 44, no. 4 (March 31, 2011): 547–58. http://dx.doi.org/10.1007/s00158-011-0641-z.

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Kotina, E. D. "Discrete optimization problem in beam dynamics." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 558, no. 1 (March 2006): 292–94. http://dx.doi.org/10.1016/j.nima.2005.11.025.

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El-Sayed, M., T. Sun, and J. Berry. "Shape optimization with computational fluid dynamics." Advances in Engineering Software 36, no. 9 (September 2005): 607–13. http://dx.doi.org/10.1016/j.advengsoft.2005.03.009.

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Wang, Di-Bao, Fei-Bin Hsiao, Cheng-Hsin Chuang, and Yung-Chun Lee. "Algorithm optimization in molecular dynamics simulation." Computer Physics Communications 177, no. 7 (October 2007): 551–59. http://dx.doi.org/10.1016/j.cpc.2007.05.009.

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Lurie, Konstantin A. "On Material Optimization in Continuum Dynamics." Journal of Optimization Theory and Applications 167, no. 1 (November 18, 2014): 147–60. http://dx.doi.org/10.1007/s10957-014-0678-4.

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Marafona, João D. M., Gonçalo N. Carneiro, Pedro M. T. Marques, Ramiro C. Martins, Carlos C. António, and Jorge H. O. Seabra. "Gear design optimization: Stiffness versus dynamics." Mechanism and Machine Theory 191 (January 2024): 105503. http://dx.doi.org/10.1016/j.mechmachtheory.2023.105503.

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Stelzer, M., and O. von Stryk. "Efficient forward dynamics simulation and optimization of human body dynamics." ZAMM 86, no. 10 (October 24, 2006): 828–40. http://dx.doi.org/10.1002/zamm.200610290.

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Journal articles on the topic 'Dynamics of optimization'

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