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Статті в журналах з теми "Multiparameter optimization"

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Автор: Grafiati

Опубліковано: 13 вересня 2021

Оновлено: 1 лютого 2022

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1

Hajima, R., and R. Nagai. "Multiparameter optimization of an ERL injector." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 557, no. 1 (February 2006): 103–5. http://dx.doi.org/10.1016/j.nima.2005.10.060.

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2

Daboczi, T., and I. Kollar. "Multiparameter optimization of inverse filtering algorithms." IEEE Transactions on Instrumentation and Measurement 45, no. 2 (April 1996): 417–21. http://dx.doi.org/10.1109/19.492758.

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3

Court, L. E., and R. Speller. "A multiparameter optimization of digital mammography." Physics in Medicine and Biology 40, no. 11 (November 1, 1995): 1841–61. http://dx.doi.org/10.1088/0031-9155/40/11/006.

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4

Elkattan, M., and A. Kamel. "Multiparameter Optimization for Electromagnetic Inversion Problem." Advanced Electromagnetics 6, no. 3 (October 21, 2017): 94. http://dx.doi.org/10.7716/aem.v6i3.438.

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Electromagnetic (EM) methods have been extensively used in geophysical investigations such as mineral and hydrocarbon exploration as well as in geological mapping and structural studies. In this paper, we developed an inversion methodology for Electromagnetic data to determine physical parameters of a set of horizontal layers. We conducted Forward model using transmission line method. In the inversion part, we solved multi parameter optimization problem where, the parameters are conductivity, dielectric constant, and permeability of each layer. The optimization problem was solved by simulated annealing approach. The inversion methodology was tested using a set of models representing common geological formations.

5

Chandrasekar, V., Eugenio Gorgucci, and Gianfranco Scarchilli. "Optimization of Multiparameter Radar Estimates of Rainfall." Journal of Applied Meteorology 32, no. 7 (July 1993): 1288–93. http://dx.doi.org/10.1175/1520-0450(1993)032<1288:oomreo>2.0.co;2.

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6

Cernea, Aurelian. "Minimum principle and controllability for multiparameter discrete inclusions via derived cones." Discrete Dynamics in Nature and Society 2006 (2006): 1–12. http://dx.doi.org/10.1155/ddns/2006/96505.

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We consider a multiparameter discrete inclusion and we prove that the reachable set of a certain variational multiparameter discrete inclusion is a derived cone in the sense of Hestenes to the reachable set of the discrete inclusion. This result allows to obtain sufficient conditions for local controllability along a reference trajectory and a new proof of the minimum principle for an optimization problem given by a multiparameter discrete inclusion with endpoint constraints.

7

Shmitko, E. I., A. A. Rezanov, and A. A. Bedarev. "Multiparameter structure optimization of the cellular silicate concrete." Magazine of Civil Engineering 38, no. 3 (April 2013): 15–23. http://dx.doi.org/10.5862/mce.38.2.

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8

Segall, Matthew. "Advances in multiparameter optimization methods forde novodrug design." Expert Opinion on Drug Discovery 9, no. 7 (May 3, 2014): 803–17. http://dx.doi.org/10.1517/17460441.2014.913565.

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9

Li, Na, Xinchen Huang, Huijie Zhao, Xianfei Qiu, Ruonan Geng, Xiuping Jia, and Daming Wang. "Multiparameter Optimization for Mineral Mapping Using Hyperspectral Imagery." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 4 (April 2018): 1348–57. http://dx.doi.org/10.1109/jstars.2018.2814617.

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10

Nomura, Laurel, Vernon C. Maino, and Holden T. Maecker. "Standardization and optimization of multiparameter intracellular cytokine staining." Cytometry Part A 73A, no. 11 (July 8, 2008): 984–91. http://dx.doi.org/10.1002/cyto.a.20602.

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11

Strasters, Joost K., Sang-Tae Kim, and Morteza G. Khaledi. "Multiparameter optimizations in micellar liquid chromatography using the iterative regression optimization strategy." Journal of Chromatography A 586, no. 2 (November 1991): 221–32. http://dx.doi.org/10.1016/0021-9673(91)85126-z.

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12

NIKITIN, S., and D. PRÄTZEL-WOLTERS. "Multiparameter Polynomial Adaptive Stabilizers." IMA Journal of Mathematical Control and Information 8, no. 4 (1991): 431–39. http://dx.doi.org/10.1093/imamci/8.4.431.

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13

Betounes, David, and Mylan Redfern. "Stochastic integrals for nonprevisible, multiparameter processes." Applied Mathematics & Optimization 28, no. 2 (September 1993): 197–223. http://dx.doi.org/10.1007/bf01182982.

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14

Voskoboinikov, Yu E. "Optimization of wavelet filtering algorithms with multiparameter threshold functions." Optoelectronics, Instrumentation and Data Processing 50, no. 6 (November 2014): 589–97. http://dx.doi.org/10.3103/s8756699014060077.

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15

Raevsky, O. A., V. Yu Grigorev, D. E. Polianczyk, O. E. Raevskaja, and J. C. Dearden. "Contribution assessment of multiparameter optimization descriptors in CNS penetration." SAR and QSAR in Environmental Research 29, no. 10 (October 2, 2018): 785–800. http://dx.doi.org/10.1080/1062936x.2018.1514652.

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16

Bar-Ilan, A., S. Zamir, O. KatzBar-Ilan, B. Meyler, and J. Salzman. "Multiparameter Statistical Design of Experiments for GaN Growth Optimization." physica status solidi (a) 176, no. 1 (November 1999): 313–17. http://dx.doi.org/10.1002/(sici)1521-396x(199911)176:1<313::aid-pssa313>3.0.co;2-5.

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17

Toropov, V. V., A. A. Filatov, and A. A. Polynkin. "Multiparameter structural optimization using FEM and multipoint explicit approximations." Structural Optimization 6, no. 1 (March 1993): 7–14. http://dx.doi.org/10.1007/bf01743169.

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18

Burczyński, T., and T. Adamczyk. "The boundary element formulation for multiparameter structural shape optimization." Applied Mathematical Modelling 9, no. 3 (June 1985): 195–200. http://dx.doi.org/10.1016/0307-904x(85)90007-1.

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19

Bublik, B. N., and B. N. Lyashenko. "Eigenvalue problem in multiparameter optimization problems of theoretical physics." Cybernetics and Systems Analysis 31, no. 3 (May 1995): 445–49. http://dx.doi.org/10.1007/bf02366523.

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20

Lai, Z. H., S. B. Wang, G. Q. Zhang, C. L. Zhang, and J. W. Zhang. "Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance." Shock and Vibration 2020 (March 10, 2020): 1–15. http://dx.doi.org/10.1155/2020/6096024.

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Анотація:

The weak-signal detection technologies based on stochastic resonance (SR) play important roles in the vibration-based health monitoring and fault diagnosis of rolling bearings, especially at their early-fault stage. Aiming at the parameter-fixed vibration signals in practical engineering, it is feasible to diagnose the potential rolling bearing faults through adaptively adjusting the SR system parameters, as well as other generalized parameters such as the amplitude-transformation coefficient and scale-transformation coefficient. However, extant adaptive adjustment methods focus on the system parameters, while the adjustments of other adjustable parameters have not been fully studied, thus limiting the detection performance of the adaptive SR method. In order to further enhance the detection performance of adaptive SR methods and extend their application in rolling bearing fault diagnosis, an adaptive multiparameter-adjusting SR (AMPASR) method for bistable systems based on particle swarm optimization (PSO) algorithm is proposed in this paper. This method can produce optimal SR output through adaptively adjusting multiparameters, thus realizing fault feature extraction and further fault diagnosis. Furthermore, the influence of algorithm parameters on the optimization results is discussed, and the optimization results of the Langevin system and the Duffing system are compared. Finally, we propose a weak-signal detection method based on the AMPASR of the Duffing system and employ three diagnosis examples involving inner ring fault, outer ring fault, and rolling element fault diagnoses to demonstrate its feasibility in rolling bearing fault diagnosis.

21

Winter, Robin, Joren Retel, Frank Noé, Djork-Arné Clevert, and Andreas Steffen. "grünifai: interactive multiparameter optimization of molecules in a continuous vector space." Bioinformatics 36, no. 13 (May 5, 2020): 4093–94. http://dx.doi.org/10.1093/bioinformatics/btaa271.

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Abstract Summary Optimizing small molecules in a drug discovery project is a notoriously difficult task as multiple molecular properties have to be considered and balanced at the same time. In this work, we present our novel interactive in silico compound optimization platform termed grünifai to support the ideation of the next generation of compounds under the constraints of a multiparameter objective. grünifai integrates adjustable in silico models, a continuous representation of the chemical space, a scalable particle swarm optimization algorithm and the possibility to actively steer the compound optimization through providing feedback on generated intermediate structures. Availability and implementation Source code and documentation are freely available under an MIT license and are openly available on GitHub (https://github.com/jrwnter/gruenifai). The backend, including the optimization method and distribution on multiple GPU nodes is written in Python 3. The frontend is written in ReactJS.

22

Gan, Weinan, Yunzhong Cao, Wen Jiang, Liangqiang Li, and Xiaolin Li. "Energy-Saving Design of Building Envelope Based on Multiparameter Optimization." Mathematical Problems in Engineering 2019 (December 10, 2019): 1–11. http://dx.doi.org/10.1155/2019/5261869.

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The contradiction between the indoor environment and building energy consumption has been controversial. The design of building envelope involves many parameters such as window size and exterior wall material. These parameters have significant influence on building energy-saving design and indoor environment. In this paper, nondominant sorting genetic algorithm-II (NSGA-II) is utilized to calculate winter heat consumption, indoor total lighting energy consumption, and thermal comfort. The Pareto method is used to select the compromise solution and effective value of each building parameter. Different from other studies, we add more architectural design variables into the model calculation, which can bring architects more detailed energy-saving design content.

23

Wager, Travis T., Xinjun Hou, Patrick R. Verhoest, and Anabella Villalobos. "Central Nervous System Multiparameter Optimization Desirability: Application in Drug Discovery." ACS Chemical Neuroscience 7, no. 6 (April 4, 2016): 767–75. http://dx.doi.org/10.1021/acschemneuro.6b00029.

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24

Cao, Mingchen, Huiying Zhao, Ruiqing Xie, Lingyu Zhao, Shijie Zhao, and Jinfeng Bai. "Multiparameter optimization design of chemical mechanical polishing for planar optics." International Journal of Advanced Manufacturing Technology 113, no. 7-8 (February 18, 2021): 2153–62. http://dx.doi.org/10.1007/s00170-021-06743-x.

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25

Xia, Guang, Yueqiang Wang, Xiwen Tang, Linfeng Zhao, and Jinfang Hu. "Double-Clutch Power Shift Quality Optimization Based on Optimal Control Theory." Applied Engineering in Agriculture 37, no. 4 (2021): 677–89. http://dx.doi.org/10.13031/aea.14312.

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Highlights A power shift control strategy based on torque and speed transition, which aims to deliver multiple target and multiparameter optimization of power shift control, is proposed in this study. It can effectively solve the shift power cycle. Based on minimum optimal control theory, the optimal control of shift quality during power shifting optimizes clutch terminal oil pressure, which is determined by solving the Rebecca differential matrix equation and shift characteristics based on various stages. By aiming at the multiple target and multiparameter optimization problem of the clutch control in the power shift process, the minimum optimal control principle is applied to the shift quality optimization of the power shift. Based on the minimum optimal control theory, the optimal solution of the terminal oil pressure of the clutch is determined by solving the Rebecca differential matrix equation to improve the shift quality of the power shift process. Abstract . The dual clutch of the combined transmission of a tractor with large horsepower uses a dynamic shifting process, in which only one clutch undergoes slipping friction during the shift. A power shift control strategy based on torque and speed transition, which aims to deliver multiple target and multiparameter optimization of power shift control, is proposed in this study. Based on minimum optimal control theory, the optimal control of shift quality during power shifting optimizes clutch terminal oil pressure, which is determined by solving the Rebecca differential matrix equation and shift characteristics based on various stages. In addition, the power shift simulation model of the double clutch is established. Simulation results show that the power shift control strategy based on single slip friction can effectively avoid power flow cycle, uninterruptible tractor power shift, and adaptive resistance change. The minimum optimization theory can effectively reduce the output torque fluctuation in the dynamic shift process, reduce friction work, and improve the shift impact. Keywords: Double clutch, Heavy-horsepower tractor, Minimum theory, Power shift.

26

Fang, Xiao-Feng, Jiang-She Zhang, and Ying-Qi Li. "Sparse Signal Reconstruction Based on Multiparameter Approximation Function with Smoothedl0Norm." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/416542.

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The smoothedl0norm algorithm is a reconstruction algorithm in compressive sensing based on approximate smoothedl0norm. It introduces a sequence of smoothed functions to approximate thel0norm and approaches the solution using the specific iteration process with the steepest method. In order to choose an appropriate sequence of smoothed function and solve the optimization problem effectively, we employ approximate hyperbolic tangent multiparameter function as the approximation to the big “steep nature” inl0norm. Simultaneously, we propose an algorithm based on minimizing a reweighted approximatel0norm in the null space of the measurement matrix. The unconstrained optimization involved is performed by using a modified quasi-Newton algorithm. The numerical simulation results show that the proposed algorithms yield improved signal reconstruction quality and performance.

27

Wang, Pengjiang, Yang Shen, Xiaodong Ji, Kai Zong, Weixiong Zheng, Dongjie Wang, and Miao Wu. "Multiparameter Control Strategy and Method for Cutting Arm of Roadheader." Shock and Vibration 2021 (July 16, 2021): 1–18. http://dx.doi.org/10.1155/2021/9918988.

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A multiparameter control strategy and method for the cutting arm of a roadheader is proposed through the operation analysis of roadheader. The method can address the problems of low intelligence and low cutting efficiency faced by the roadheader in the cutting process. The control strategy is divided into two parts: the cutting load identification part and the swing speed control part. The former part is designed using a backpropagation neural network that is optimized by an improved particle swarm optimization algorithm. The latter part is optimally designed using a fuzzy PID controller with improved simulated annealing particle swarm optimization. The simulation analysis in SIMULINK showed that the response time was reduced, proving the robustness of the method. In addition, experimental studies verified the good control effect of the method under different cutting states. The proposed method uses multiparameter to intelligently change the swing speed, providing a theoretical and practical basis for the realization of intelligent and unmanned cutting of roadheader.

28

Ståhl, Niclas, Göran Falkman, Alexander Karlsson, Gunnar Mathiason, and Jonas Boström. "Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design." Journal of Chemical Information and Modeling 59, no. 7 (June 19, 2019): 3166–76. http://dx.doi.org/10.1021/acs.jcim.9b00325.

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29

Kerr, Q. P., and M. J. Feldman. "Multiparameter optimization of RSFQ circuits using the method of inscribed hyperspheres." IEEE Transactions on Appiled Superconductivity 5, no. 2 (June 1995): 3337–40. http://dx.doi.org/10.1109/77.403306.

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30

Stoffa, Paul L., and Mrinal K. Sen. "Nonlinear multiparameter optimization using genetic algorithms: Inversion of plane‐wave seismograms." GEOPHYSICS 56, no. 11 (November 1991): 1794–810. http://dx.doi.org/10.1190/1.1442992.

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Seismic waveform inversion is one of many geophysical problems which can be identified as a nonlinear multiparameter optimization problem. Methods based on local linearization fail if the starting model is too far from the true model. We have investigated the applicability of “Genetic Algorithms” (GA) to the inversion of plane‐wave seismograms. Like simulated annealing, genetic algorithms use a random walk in model space and a transition probability rule to help guide their search. However, unlike a single simulated annealing run, the genetic algorithms search from a randomly chosen population of models (strings) and work with a binary coding of the model parameter set. Unlike a pure random search, such as in a “Monte Carlo” method, the search used in genetic algorithms is not directionless. Genetic algorithms essentially consist of three operations, selection, crossover, and mutation, which involve random number generation, string copies, and some partial string exchanges. The choice of the initial population, the probabilities of crossover and mutation are crucial for the practical implementation of the algorithm. We investigated the effects of these parameters in the inversion of plane‐wave seismograms in which a normalized crosscorrelation function was used as the objective or fitness function (E). We also introduce the concept of “update” probability to control the influence of past generations. The combination of a low value of mutation probability (∼0.01), a moderate value of the crossover probability (∼0.6) and a high value of update probability (∼0.9) are found to be optimal for the convergence of the algorithm. Further, we show that concepts from simulated annealing can be used effectively for the stretching of the fitness function which helps in the convergence of the algorithm. Thus, we propose to use exp (E/T) rather than E as the fitness function, where T (analogous to temperature in simulated annealing) is a properly chosen parameter which can change slowly with each generation. Also, by repeating the GA optimization procedure several times with different randomly chosen initial model populations, we derive “a very good subset” of models from the entire model space and calculate the a posteriori probability density σ(m) ∝ exp (E(m)/T). The σ(m) ’s are then used to calculate a “mean” model, which is found to be close to the true model.

31

Gontarz, S., R. Patyk, L. Bohdal, and D. Jackiewicz. "Multiparameter Optimization of Mechanical Cutting Process of Grain Oriented Silicon Steel." Acta Physica Polonica A 137, no. 5 (May 2020): 930–32. http://dx.doi.org/10.12693/aphyspola.137.930.

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32

Xu, Yun, Lei Tan, Shuliang Cao, and Wanshi Qu. "Multiparameter and multiobjective optimization design of centrifugal pump based on orthogonal method." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 14 (March 22, 2016): 2569–79. http://dx.doi.org/10.1177/0954406216640303.

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Optimization design of centrifugal pumps involving multiple parameters and objectives is a complicated research topic. The orthogonal method is introduced in the present study to find a high efficiency and low cost way in the optimization process of a centrifugal pump. A orthogonal table designation L16(45) is established, in which 16 individuals of impellers are generated with five design parameters: blade wrap angle, blade angles at impeller inlet and outlet, blade leading edge position, and blade trailing edge lean varying at four levels for each parameter. To realize the multiobjective optimization of both pump efficiency and cavitation performance, an integrated factor considering the weight of two objectives is introduced. On the basis of validated computational fluid dynamics (CFD) technique, the range analysis gives the influence order of five parameters and also determines the value of each parameter. Finally, the optimal centrifugal pump is obtained with remarkable superiority on the efficiency of 3.09% rise and cavitation performance of 1.45 m promotion in comparison with the original pump.

33

Hernandez, F., and M. Izquierdo. "Criteria for Economic Optimization of an Absorption System Using Sources of Low Temperature." Journal of Energy Resources Technology 118, no. 2 (June 1, 1996): 164–66. http://dx.doi.org/10.1115/1.2792709.

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An absorption system, reduced to the equivalent combined system of three-heat-source heat pump, is optimized with economical criteria. The multiparameter thermodynamic function η combines the COP and rate of pumping, and determines that optimum. Values of μ = 0, μ = 1, and μ > 1 define three different cases of application for different markets.

34

Fan, Honggang, Jinsong Zhang, Wei Zhang, and Bing Liu. "Multiparameter and Multiobjective Optimization Design Based on Orthogonal Method for Mixed Flow Fan." Energies 13, no. 11 (June 2, 2020): 2819. http://dx.doi.org/10.3390/en13112819.

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Optimization design of an impeller is critical for the energy performance of a fan. This paper takes the promotion of fan efficiency and pressure rise as the optimization objectives to carry out multiparameter and multiobjective optimization research. Firstly, an experimental test bench is built to measure the energy performance of the original fan and verify the accuracy of the numerical method. Then, the hub outlet angle of impeller β1, the impeller outlet angle increment Δβ1, the wrap angle φ, the hub outlet angle of diffuser β2, and the diffuser outlet angle increment Δβ2 are set as the optimal parameters to conduct orthogonal optimal design. The results show that the efficiency of the optimal fan increases by 11.71%, and the pressure rise increases by 50.15%. The pressure and velocity distributions in an optimal fan are uniform, the internal flow separation is weakened, and the influence of tip leakage flow is reduced, which makes for the improvement of energy performance for the fan.

35

Pennington, Lewis D., Brian M. Aquila, Younggi Choi, Roman A. Valiulin, and Ingo Muegge. "Positional Analogue Scanning: An Effective Strategy for Multiparameter Optimization in Drug Design." Journal of Medicinal Chemistry 63, no. 17 (April 24, 2020): 8956–76. http://dx.doi.org/10.1021/acs.jmedchem.9b02092.

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36

Polajnar, D., L. Lukic, and V. Solaja. "An Interactive Simulation Model for Multiparameter Optimization of Cutting Processes in FMS." IFAC Proceedings Volumes 23, no. 3 (September 1990): 197–202. http://dx.doi.org/10.1016/s1474-6670(17)52557-4.

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37

Raevsky, Oleg A. "CNS Multiparameter Optimization Approach: Is it in Accordance with Occam’s Razor Principle?" Molecular Informatics 35, no. 3-4 (January 12, 2016): 94–98. http://dx.doi.org/10.1002/minf.201500109.

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38

Burke, E. J., L. A. Bastidas, and W. J. Shuttleworth. "Exploring the potential for multipatch soil-moisture retrievals using multiparameter optimization techniques." IEEE Transactions on Geoscience and Remote Sensing 40, no. 5 (May 2002): 1114–20. http://dx.doi.org/10.1109/tgrs.2002.1010898.

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39

Schürger, Klaus. "A multiparameter almost superadditive limit theorem and its application to combinatorial optimization." Stochastic Processes and their Applications 26 (1987): 212. http://dx.doi.org/10.1016/0304-4149(87)90127-x.

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40

Plander, M., G. Brockhoff, S. Barlage, S. Schwarz, G. Rothe, and R. Knuechel. "Optimization of three- and four-color multiparameter DNA analysis in lymphoma specimens." Cytometry 54A, no. 1 (June 16, 2003): 66–74. http://dx.doi.org/10.1002/cyto.a.10051.

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41

Pennington, Lewis D., and Ingo Muegge. "Holistic drug design for multiparameter optimization in modern small molecule drug discovery." Bioorganic & Medicinal Chemistry Letters 41 (June 2021): 128003. http://dx.doi.org/10.1016/j.bmcl.2021.128003.

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42

Spasos, M., R. Nilavalan, K. Tsiakmakis, N. Charalampidis, and S. W. Cheung. "Optimization of a 12.5 GHz Microstrip Antenna Array Using Taguchi's Method." International Journal of Antennas and Propagation 2011 (2011): 1–9. http://dx.doi.org/10.1155/2011/458569.

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This paper presents Taguchi's optimization method implemented in the design of a single feed (without any matching network) microstrip antenna array operating around 12.5 GHz. The proposed optimization method is statistical and is widely used for quality assurance in many fields such as mechanical and chemical production, consumer electronics, services; however it has been underused in the field of electromagnetics. It allows optimization of multiparameter, multitarget complex designs in a very short time in conjunction with advanced simulation tools. The proposed antenna has been fully evaluated under Taghuchi's and PSO's optimization methods, and the experimental results show total Gain of 15 dB, and good matching withS11 better than 20 dB, in the frequency range 12.3 to 12.8 GHz.

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Dong, Qin, and Mo. "Low-Cost Multi-Objective Optimization of Multiparameter Antenna Structures Based on the l1 Optimization BPNN Surrogate Model." Electronics 8, no. 8 (July 26, 2019): 839. http://dx.doi.org/10.3390/electronics8080839.

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The development of modern wireless communication systems not only requires the antenna to be lightweight, low cost, easy to manufacture and easy to integrate but also imposes requirements on the miniaturization, wideband, and multiband design of the antenna. Therefore, designing an antenna that quickly and effectively meets multiple performance requirements is of great significance. To solve the problem of the large computational cost of traditional multi-objective antenna design methods, this paper proposes a backpropagation neural network surrogate model based on l1 optimization (l1-BPNN). The l1 optimization method tends to punish larger weight values and select smaller weight values so as to preserve a small amount of important weights and reset relatively unimportant weights to zero. By using l1 optimization method, the network mapping structure can be automatically adjusted to achieve the most suitable and compact structure of the surrogate model. Furthermore, for multi-parameter antenna design problems, a fast multi-objective optimization framework is constructed using the proposed l1-BPNN as a surrogate model. The framework is illustrated using a miniaturized multiband antenna design case, and a comparison with previously published methods, as well as numerical validation, is also provided.

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Walda, Jan, Benjamin Schwarz, and Dirk Gajewski. "A competitive comparison of multiparameter stacking operators." GEOPHYSICS 82, no. 4 (July 1, 2017): V275—V283. http://dx.doi.org/10.1190/geo2016-0432.1.

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The classic common-midpoint (CMP) stack, which sums along offsets, suffers in challenging environments in which the acquisition is sparse. In the past, several multiparameter stacking techniques were introduced that incorporate many neighboring CMPs during summation. This increases data redundancy and reduces noise. Multiparameter methods that can be parameterized by the same wavefront attributes are multifocusing (MF), the common-reflection-surface (CRS), implicit CRS, and nonhyperbolic CRS (nCRS). The CRS-type operators use a velocity-shift mechanism to account for heterogeneity by changing the slope of the asymptote. On the other hand, MF uses a different mechanism: a shift of reference time while preserving the slope of the asymptote. We have formulated MF such that it uses the same mechanism as the CRS-type operators and compare them on a marine data set. In turn, we investigate the behavior of time-shifted versions of the CRS-type approximations. To provide a fair comparison, we use a global optimization technique, differential evolution, which allows to accurately estimate a solution without an initial guess solution. Our results indicate that the velocity-shift mechanism performs, in general, better than the one incorporating a time shift. The double-square-root operators are also less sensitive to the choice of aperture. They perform better in the case of diffractions than conventional hyperbolic CRS, and this fact is in good agreement with previous works. In our work the nCRS is of almost the same computational cost as that of conventional hyperbolic CRS, but it generally leads to a superior fit; therefore, we recommend its use in the future.

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Yang, Xiaoliu, Zetao Li, Qingfang Zhang, Qinmu Wu, and Linli Yang. "A Nonlinear Adaptive Observer-Based Differential Evolution Algorithm to Multiparameter Fault Diagnosis." Mathematical Problems in Engineering 2020 (May 7, 2020): 1–12. http://dx.doi.org/10.1155/2020/4531075.

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In this paper, a novel adaptive diagnosis scheme is proposed for multiparametric faults of nonlinear systems by using the model and intelligent optimization-based approaches. The key idea of the proposed method is to analyze the correlation of the output signals between the real system and the fault identification system instead of residual. A new adaptive scheme is built based on an adaptive observer and differential evolution algorithm. Meanwhile, the conditions of detectability and identifiability of faults are analyzed. The isolation and estimation of the multiparametric fault are formulated as the solution of an optimization problem that is solved by using a differential evolutionary algorithm (DE). The fitness function of DE is constructed by the correlation coefficient equations in which the faulty components are contained. The application on a coupled three water tank model attests the feasibility and validity of the suggested approach. Simulation and experimental results show that the developed method is applicable to diagnose either single or multiparameter faults on-line.

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Lv, Xueying, Yitian Wang, Junyi Deng, Guanyu Zhang, and Liu Zhang. "Improved Particle Swarm Optimization Algorithm Based on Last-Eliminated Principle and Enhanced Information Sharing." Computational Intelligence and Neuroscience 2018 (December 5, 2018): 1–17. http://dx.doi.org/10.1155/2018/5025672.

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In this study, an improved eliminate particle swarm optimization (IEPSO) is proposed on the basis of the last-eliminated principle to solve optimization problems in engineering design. During optimization, the IEPSO enhances information communication among populations and maintains population diversity to overcome the limitations of classical optimization algorithms in solving multiparameter, strong coupling, and nonlinear engineering optimization problems. These limitations include advanced convergence and the tendency to easily fall into local optimization. The parameters involved in the imported “local-global information sharing” term are analyzed, and the principle of parameter selection for performance is determined. The performances of the IEPSO and classical optimization algorithms are then tested by using multiple sets of classical functions to verify the global search performance of the IEPSO. The simulation test results and those of the improved classical optimization algorithms are compared and analyzed to verify the advanced performance of the IEPSO algorithm.

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Pennington, Lewis D., and Demetri T. Moustakas. "The Necessary Nitrogen Atom: A Versatile High-Impact Design Element for Multiparameter Optimization." Journal of Medicinal Chemistry 60, no. 9 (February 8, 2017): 3552–79. http://dx.doi.org/10.1021/acs.jmedchem.6b01807.

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Todorović, G., V. Milanović, Z. Ikonić, and D. Indjin. "Multiparameter optimization of optical nonlinearities in semiconductor quantum wells by supersymmetric quantum mechanics." Physics Letters A 279, no. 3-4 (January 2001): 268–74. http://dx.doi.org/10.1016/s0375-9601(00)00843-4.

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Yelk, Joseph, Maxim Sukharev, and Tamar Seideman. "Optimal design of nanoplasmonic materials using genetic algorithms as a multiparameter optimization tool." Journal of Chemical Physics 129, no. 6 (August 14, 2008): 064706. http://dx.doi.org/10.1063/1.2961011.

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Craven, Gregory B., Dominic P. Affron, Teresa Kösel, Tsz Lam M. Wong, Zoë H. Jukes, Chun‐Ting Liu, Rhodri M. L. Morgan, Alan Armstrong, and David J. Mann. "Multiparameter Kinetic Analysis for Covalent Fragment Optimization by Using Quantitative Irreversible Tethering (qIT)." ChemBioChem 21, no. 23 (August 7, 2020): 3417–22. http://dx.doi.org/10.1002/cbic.202000457.

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