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Journal articles on the topic 'Optimization criterion'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

Bengio, Yoshua. "Gradient-Based Optimization of Hyperparameters." Neural Computation 12, no. 8 (August 1, 2000): 1889–900. http://dx.doi.org/10.1162/089976600300015187.

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Many machine learning algorithms can be formulated as the minimization of a training criterion that involves a hyperparameter. This hyperparameter is usually chosen by trial and error with a model selection criterion. In this article we present a methodology to optimize several hyper-parameters, based on the computation of the gradient of a model selection criterion with respect to the hyperparameters. In the case of a quadratic training criterion, the gradient of the selection criterion with respect to the hyperparameters is efficiently computed by backpropagating through a Cholesky decomposition. In the more general case, we show that the implicit function theorem can be used to derive a formula for the hyper-parameter gradient involving second derivatives of the training criterion.

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2

Li, Shun Guo, and Hui Li. "Optimization Method of Hoisting Points Schemes Using Strain Energy Criterion." Applied Mechanics and Materials 88-89 (August 2011): 583–86. http://dx.doi.org/10.4028/www.scientific.net/amm.88-89.583.

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The optimization method of hoisting point’s schemes using strain energy criterion was studied in this paper. Firstly, the finite element model of complex steel truss hoisting was established and optimization analysis of hoisting point’s schemes for complex steel truss hoisting using strain energy criterion was accomplished. The calculation code which can make finite element analysis and optimization analysis of lifting point’s schemes based on strain energy criterion automatically. Then, lifting point’s schemes of complex steel truss hoisting were analyzed with calculation code mentioned above. The results indicate that, the optimization index using strain energy criterion is just strain energy criterion which is a more comprehensive and unidirectional index. Optimization analysis based on strain energy criterion changes optimization analysis of the lifting points schemes for complex steel truss hoisting from multi-target optimization into single-target optimization. The case study shows that this method is practicable and reliable and have good application prospect in hoisting points schemes optimization analysis with application to complex steel truss hoisting.

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Aneja, Preety. "Optimization and Efficiency Studies of Heat Engines: A Review." Journal of Advanced Research in Mechanical Engineering and Technology 07, no. 03 (October 7, 2020): 37–58. http://dx.doi.org/10.24321/2454.8650.202006.

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This review aims to study the various theoretical and numerical investigations in the optimization of heat engines. The main focus is to discuss the procedures to derive the efficiency of heat engines under different operating regimes (or optimization criteria) for different models of heat engines such as endreversible models, stochastic models, low-dissipation models, quantum models etc. Both maximum power and maximum efficiency operational regimes are desirable but not economical, so to meet the thermo-ecological considerations, some other compromise-based criteria have been proposed such as Ω criterion (ecological criterion) and efficient power criterion. Thus, heat engines can be optimized to work at an efficiency which may not be the maximum (Carnot) efficiency. The optimization efficiency obtained under each criterion shows a striking universal behaviour in the near-equilibrium regime. We also discussed a multi-parameter combined objective function of heat engines. The optimization efficiency derived from the multi-parameter combined objective function includes a variety of optimization efficiencies, such as the efficiency at the maximum power, efficiency at the maximum efficiency-power state, efficiency at the maximum criterion, and Carnot efficiency. Thus, a comparison of optimization of heat engines under different criteria enables to choose the suitable one for the best performance of heat engine under different conditions.

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Yoon, Jong-Min, Youngmyung Lee, Sang-Ok Park, Yong-Ha Han, and Gyung-Jin Park. "Crash optimization considering the head injury criterion." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 11 (October 29, 2018): 2879–90. http://dx.doi.org/10.1177/0954407018809298.

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In the crashworthiness of the vehicle, the head injury criterion is the most significant factor in the injury rate. Crash optimization has been employed to enhance the head injury criterion value. Since the head injury criterion value is calculated from acceleration, a surrogate-model-based crash optimization method is generally used. However, when the number of design variables increases, the cost of analysis increases extremely. Conceptual design such as topology optimization is difficult to apply since it has many design variables. A crash optimization methodology that considers the head injury criterion value is proposed based on the equivalent static loads method. The proposed method calculates the head injury criterion value using the finite difference method, and the channel frequency classes filter during linear static-response structural optimization with the equivalent static loads. Two practical large-scale problems are solved to validate the proposed method. For the headform impact on the upper interior, size optimization is carried out to satisfy the constraint on the head injury criterion value while the mass is minimized. Topology optimization is performed in the case of the hood headform impact. The material distribution of the inner panel in the hood is determined to minimize the head injury criterion value.

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Noghin, V. D. "Linear scalarization in multi-criterion optimization." Scientific and Technical Information Processing 42, no. 6 (December 2015): 463–69. http://dx.doi.org/10.3103/s014768821506009x.

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6

Cacho-Pérez, M. "2D frames optimization. Criterion: maximum stability." Applied Mathematical Modelling 46 (June 2017): 591–601. http://dx.doi.org/10.1016/j.apm.2017.02.002.

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7

Barsky, Eugene. "Conditions Providing Optimum Separation." Physical Separation in Science and Engineering 13, no. 3-4 (January 1, 2004): 153–63. http://dx.doi.org/10.1080/14786470412331328015.

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It is generally accepted in the field of powder technology that the most objective quality criterion for the optimization of separation is the Hancock criterion. In this article, use an analytical procedure to show disadvantages of this criterion. I also apply the same analysis to show the objectivity of the entropy criterion for the optimization of separation processes. A simple objective relationship for the optimization of separation processes of pourable materials is derived on the basis of the entropy criterion.

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8

Amirov, F. G. "Developing Criterion and Optimization of PAL System." Applied Mechanics and Materials 379 (August 2013): 244–49. http://dx.doi.org/10.4028/www.scientific.net/amm.379.244.

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9

Estri, Mutia Nur, Siti Rahmah Nurshiami, Rina Reorita, and Muhammad Okky Ibrohim. "PENENTUAN KRITERIA PENGHENTIAN ITERASI PADA ALGORITMA STROBERI." Jurnal Ilmiah Matematika dan Pendidikan Matematika 10, no. 1 (June 29, 2018): 27. http://dx.doi.org/10.20884/1.jmp.2018.10.1.2834.

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This paper discusses the application of two types of stopping criterion on the strawberry algorithm, which are stopping criteria based on iterative error and Cauchy criterion. Furthermore, the strawberry algorithm program is simulated on the optimization problem with the objective function is quadratic function. The simulation results on optimization problem with the objective function is quadratic function show that strawberry algorithm with stopping criterion based on Cauchy criterion has the best performance, when compared with stopping criterion based on iterative error and without stopping criterion

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10

Kukharchyk, A. G. "TRANSPORT TASK OF OPTIMIZATION OF COSTS WITH MULTIMODAL TRANSPORTATION." Economic innovations 19, no. 2(64) (July 7, 2017): 157–63. http://dx.doi.org/10.31520/ei.2017.19.2(64).157-163.

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In the article, the questions of cost optimization in solving the transport problem using mathematical models are considered. A group of criteria that have the greatest influence in solving the transport problem is determined. The mathematical model of the transport problem allows us to describe a multitude of situations that arise in multimodal transport. The formulation of the goal is optimization - a task more economical, on the other - knowledge of economic and mathematical methods can more effectively solve this problem. The rationale for choosing an optimization criterion is a procedure that cannot be fully formalized, it must be performed taking into account the performance of transport and the interrelationship between them. The common approach to choosing and justifying an optimization criterion is usually based on the following circumstance: as a criterion, only a measure that can be quantified is chosen. Most often, the justification of one indicator is taken as a criterion (characteristic) of the process, less often - a group of criteria, depending on which one speaks of tasks with one criterion or multicriteria. As can be seen from the above, each criterion of optimality has advantages and disadvantages, which most often result from the measure of the synthetic criterion, the difficulty of preparing information in the form an array of coefficients for unknowns in the target function, the narrower or broader scope of its application. The selection and justification of the optimization criterion are performed taking into account all these circumstances in each particular case. In conclusion, it should be noted that all of these criteria have meaning in such tasks, where the volume of traffic is predetermined.

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Li, Q., G. P. Steven, and Y. M. Xie. "On equivalence between stress criterion and stiffness criterion in evolutionary structural optimization." Structural Optimization 18, no. 1 (August 1999): 67–73. http://dx.doi.org/10.1007/bf01210693.

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12

Ayken, Taylan, and Jun-ichi Imura. "Diffusion Based Stopping Criterion for Distributed Optimization." IFAC Proceedings Volumes 47, no. 3 (2014): 10512–17. http://dx.doi.org/10.3182/20140824-6-za-1003.02730.

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13

Solopov, R. V. "Criterion complex optimization in electric-power systems." Russian Electrical Engineering 88, no. 5 (May 2017): 280–84. http://dx.doi.org/10.3103/s1068371217050133.

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14

Seredyński, F., J. Koronacki, and C. Z. Janikow. "Distributed multiprocessor scheduling with decomposed optimization criterion." Future Generation Computer Systems 17, no. 4 (January 2001): 387–96. http://dx.doi.org/10.1016/s0167-739x(99)00119-3.

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15

van Someren, E. P., L. F. A. Wessels, E. Backer, and M. J. T. Reinders. "Multi-criterion optimization for genetic network modeling." Signal Processing 83, no. 4 (April 2003): 763–75. http://dx.doi.org/10.1016/s0165-1684(02)00473-5.

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16

Zawora, Józef, Mieczysław Marciniak, and Lucjan Dąbrowski. "Multi-criterion optimization of the titanium turning." Mechanik, no. 10 (October 2016): 1432–33. http://dx.doi.org/10.17814/mechanik.2016.10.396.

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17

Neumaier, Arnold. "An optimality criterion for global quadratic optimization." Journal of Global Optimization 2, no. 2 (1992): 201–8. http://dx.doi.org/10.1007/bf00122055.

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18

Chen, Qun, Hongye Zhu, Ning Pan, and Zeng-Yuan Guo. "An alternative criterion in heat transfer optimization." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 467, no. 2128 (October 13, 2010): 1012–28. http://dx.doi.org/10.1098/rspa.2010.0293.

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Entropy generation is recognized as a common measurement of the irreversibility in diverse processes, and entropy generation minimization has thus been used as the criterion for optimizing various heat transfer cases. To examine the validity of such entropy-based irreversibility measurement and its use as the optimization criterion in heat transfer, both the conserved and non-conservative quantities during a heat transfer process are analysed. A couple of irreversibility measurements, including the newly defined concept entransy , in heat transfer process are discussed according to different objectives. It is demonstrated that although thermal energy is conserved, the accompanied system entransy and entropy in heat transfer process are non-conserved quantities. When the objective of a heat transfer is for heating or cooling, the irreversibility should be measured by the entransy dissipation, whereas for heat-work conversion, the irreversibility should be described by the entropy generation. Next, in Fourier’s Law derivation using the principle of minimum entropy production, the thermal conductivity turns out to be inversely proportional to the square of temperature. Whereas, by using the minimum entransy dissipation principle, Fourier’s Law with a constant thermal conductivity as expected is derived, suggesting that the entransy dissipation is a preferable irreversibility measurement for heat transfer.

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19

Xu, Xun, Lijun Wu, and Zu’an Lu. "Performance optimization criterion of blast furnace stave." International Journal of Heat and Mass Transfer 105 (February 2017): 102–8. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2016.09.056.

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20

Wang, Nenzi. "Multi-criterion optimization for heel–toe running." Journal of Biomechanics 38, no. 8 (August 2005): 1712–16. http://dx.doi.org/10.1016/j.jbiomech.2005.01.021.

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21

Aladag, Cagdas Hakan, Erol Egrioglu, Suleyman Gunay, and Murat A. Basaran. "Improving weighted information criterion by using optimization." Journal of Computational and Applied Mathematics 233, no. 10 (March 2010): 2683–87. http://dx.doi.org/10.1016/j.cam.2009.11.016.

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22

Tang, Zhili, and Lianhe Zhang. "Nash equilibrium and multi criterion aerodynamic optimization." Journal of Computational Physics 314 (June 2016): 107–26. http://dx.doi.org/10.1016/j.jcp.2016.03.001.

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23

Rhinehart, R. Russell. "Convergence criterion in optimization of stochastic processes." Computers & Chemical Engineering 68 (September 2014): 1–6. http://dx.doi.org/10.1016/j.compchemeng.2014.04.011.

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24

Chee Sun Won. "An optimization for classification maximum likelihood criterion." Pattern Recognition Letters 14, no. 5 (May 1993): 363–67. http://dx.doi.org/10.1016/0167-8655(93)90113-r.

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25

Dang, Hieu V., and Witold Kinsner. "Adaptive Multiobjective Memetic Optimization." International Journal of Cognitive Informatics and Natural Intelligence 10, no. 4 (October 2016): 21–58. http://dx.doi.org/10.4018/ijcini.2016100102.

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Multiobjective memetic optimization algorithms (MMOAs) are recently applied to solve nonlinear optimization problems with conflicting objectives. An important issue in an MMOA is how to identify the relative best solutions to guide its adaptive processes. In this paper, the authors introduce a framework of adaptive multiobjective memetic optimization algorithms (AMMOA) with an information theoretic criterion for guiding the adaptive selection, clustering, local learning processes, and a robust stopping criterion of AMMOA. The implementation of AMMOA is applied to several benchmark test problems with remarkable results. The paper also presents the application of AMMOA in designing an optimal image watermarking to maximize the quality of the watermarked images and the robustness of the watermark.

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26

Jiang, Quan Sheng, and Su Ping Li. "Classification Criterion Based Neighborhood Optimization Method on Laplacian Eigenmaps." Advanced Materials Research 403-408 (November 2011): 2679–82. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2679.

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Manifold learning algorithms are nonlinear dimensionality reduction algorithms rising in recent years. Laplacian Eigenmaps is a typical manifold learning algorithms. Aim to the difficulty of selecting neighborhood parameter on the algorithm, a neighborhood parameter optimization method based on classification criterion is proposed in the paper. From the point of the classification performance, the classification criterion function is constructed to reflect the distance of within-class and between-class. The optimization of the neighborhood is obtained according to the minimum of the criterion function. The experimental results on IRIS validate the optimization of the neighborhood and the effectiveness of feature classification.

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27

Liu, Kexin, Weimin Bao, Yufeng Hu, Yiqun Sun, Dongjing Li, Kuang Li, and Lili Liang. "Improvement in Ridge Coefficient Optimization Criterion for Ridge Estimation-Based Dynamic System Response Curve Method in Flood Forecasting." Water 13, no. 24 (December 7, 2021): 3483. http://dx.doi.org/10.3390/w13243483.

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The ridge estimation-based dynamic system response curve (DSRC-R) method, which is an improvement of the dynamic system response curve (DSRC) method via the ridge estimation method, has illustrated its good robustness. However, the optimization criterion for the ridge coefficient in the DSRC-R method still needs further study. In view of this, a new optimization criterion called the balance and random degree criterion considering the sum of squares of flow errors (BSR) is proposed in this paper according to the properties of model-simulated residuals. In this criterion, two indexes, namely, the random degree of simulated residuals and the balance degree of simulated residuals, are introduced to describe the independence and the zero mean property of simulated residuals, respectively. Therefore, the BSR criterion is constructed by combining the sum of squares of flow errors with the two indexes. The BSR criterion, L-curve criterion and the minimum sum of squares of flow errors (MSSFE) criterion are tested on both synthetic cases and real-data cases. The results show that the BSR criterion is better than the L-curve criterion in minimizing the sum of squares of flow residuals and increasing the ridge coefficient optimization speed. Moreover, the BSR criterion has an advantage over the MSSFE criterion in making the estimated rainfall error more stable.

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28

Ostrovskaya, N. V., and D. E. Bondarev. "Criteria for damping parameters optimization in seismic isolated structures." Вестник гражданских инженеров 17, no. 5 (2020): 94–100. http://dx.doi.org/10.23968/1999-5571-2020-17-5-94-100.

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All kinds of seismic insulation systems are widely used to protect various structures and buildings, including unique ones, against earthquakes. The efficiency of such systems depends significantly on the competent selection of damping parameters. The article considers a general approach to selecting optimal damping parameters, both by the criterion of absolute accelerations and by the kinematic criterion. The optimization criterion for unique structures is also proposed.

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29

Omkar, S. N., G. Narayana Naik, Kiran Patil, and Mrunmaya Mudigere. "Vector Evaluated and Objective Switching Approaches of Artificial Bee Colony Algorithm (ABC) for Multi-Objective Design Optimization of Composite Plate Structures." International Journal of Applied Metaheuristic Computing 2, no. 3 (July 2011): 1–26. http://dx.doi.org/10.4018/jamc.2011070101.

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In this paper, a generic methodology based on swarm algorithms using Artificial Bee Colony (ABC) algorithm is proposed for combined cost and weight optimization of laminated composite structures. Two approaches, namely Vector Evaluated Design Optimization (VEDO) and Objective Switching Design Optimization (OSDO), have been used for solving constrained multi-objective optimization problems. The ply orientations, number of layers, and thickness of each lamina are chosen as the primary optimization variables. Classical lamination theory is used to obtain the global and local stresses for a plate subjected to transverse loading configurations, such as line load and hydrostatic load. Strength of the composite plate is validated using different failure criteria—Failure Mechanism based failure criterion, Maximum stress failure criterion, Tsai-Hill Failure criterion and the Tsai-Wu failure criterion. The design optimization is carried for both variable stacking sequences as well as standard stacking schemes and a comparative study of the different design configurations evolved is presented. Performance of Artificial Bee Colony (ABC) is compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for both VEDO and OSDO approaches. The results show ABC yielding a better optimal design than PSO and GA.

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30

Bu, Yun, and Wan Xin Kang. "An Adaptive Prediction Algorithm Based on Maximum Correntropy Criterion." Applied Mechanics and Materials 380-384 (August 2013): 1310–13. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1310.

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The traditional cost function, minimization mean square prediction error is a second order statistic, and it is based on the error Gaussian distribution and linear assumption. But chaotic signals are non-Gaussian, so the optimization criterion is not suitable. Then we present using the robust optimization criterion, maximum correntropy to replace the popular minima mean square error criterion minimization error. In simulation, the algorithm shows an improved performance to a common three-order Volterra prediction.

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31

Arias-Hernández, L. A., G. Ares de Parga, and F. Angulo-Brown. "A Variational Ecological-Type Optimization of Some Thermal-Engine Models." Open Systems & Information Dynamics 11, no. 02 (June 2004): 123–38. http://dx.doi.org/10.1023/b:opsy.0000034191.86187.f0.

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In a recent work [23] we have analyzed a nonendoreversible thermal engine model under two maximization criteria: the maximum power regime and the so-called ecological criterion. In the present work, we extend the study of the same class of nonendoreversible models, but by using the so-named generalized ecological criterion [15]. This criterion is based on a family of ecological-type functions depending on a parameter associated to the particular heat transfer law used in the engine model. By means of this criterion we find a simplification of some of the results obtained in [23] by using both conventional and variational calculus. Besides, we propose a simple procedure to calculate the parameter involved in the generalized ecological function.

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32

Zabolotnii, Serhii, Artem Honcharov, and Sergii Mogilei. "FACTOR ANALYSIS METHOD APPLICATION FOR CONSTRUCTING OBJECTIVE FUNCTIONS OF OPTIMIZATION IN MULTIMODAL TRANSPORT PROBLEMS." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 11, no. 4 (December 20, 2021): 28–31. http://dx.doi.org/10.35784/iapgos.2788.

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The paper regards a specific class of optimization criteria that possess features of probability. Therefore, constructing objective function of optimization problem, the importance is attached to probability indices that show the probability of some criterial event or events to occur. Factor analysis has been taken for the main method of constructing objective function. Algorithm for constructing objective function of optimization is done for criterion of minimization risk level in multimodal transportations that demanded demonstration data. The application of factor analysis in classical problem solution was shown to give the problem a more distinct analytical interpretation in solving it.

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33

Li, Han, Leonardo Gutierrez, Masakazu Kobayashi, Osamu Kuwazuru, Hiroyuki Toda, and Rafael Batres. "A Numerical Evaluation of an Infill Sampling Criterion in Artificial Neural Network-Based Optimization." International Journal of Computer Theory and Engineering 6, no. 3 (2014): 272–77. http://dx.doi.org/10.7763/ijcte.2014.v6.874.

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34

Abuhamdah, Anmar. "Adaptive Acceptance Criterion (AAC) Algorithm for Optimization Problems." Journal of Computer Science 11, no. 4 (April 1, 2015): 675–91. http://dx.doi.org/10.3844/jcssp.2015.675.691.

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35

Takezawa, Kunio. "Tree Model Optimization Criterion without Using Prediction Error." Open Journal of Statistics 02, no. 05 (2012): 478–83. http://dx.doi.org/10.4236/ojs.2012.25061.

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36

Zhong, Li Yun, and Yu Ze Liu. "Using Kernel Fisher Criterion for Gaussian Kernel Optimization." Applied Mechanics and Materials 734 (February 2015): 534–38. http://dx.doi.org/10.4028/www.scientific.net/amm.734.534.

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Empirical success of kernel-based learning methods is very much dependent on the kernel used. We propose an effective Gaussian kernel optimization approach for support vector machine (SVM). The key property of the proposed approach is that it adopts the kernel Fisher criterion (KFC) as the evaluation criterion to measure the goodness of the kernel used. After introducing a distance-based representation of KFC, we optimize the Gaussian kernel by using a gradient-based algorithm, which is based on the possibility of computing the gradient of KFC with respect to the width parameter of Gaussian kernel. The proposed approach is demonstrated with two popular UCI machine learning benchmark examples.

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37

Pereverzev, P. P., A. V. Akintseva, and D. V. Ardashev. "Two-Criterion Optimization of Automatic CNC Grinding Cycles." Russian Engineering Research 40, no. 4 (April 2020): 333–35. http://dx.doi.org/10.3103/s1068798x20040152.

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38

Lemos, Lívia Pereira, Enrique Luis Lima, and José Carlos Pinto. "New Decision Making Criterion for Multiobjective Optimization Problems." Industrial & Engineering Chemistry Research 57, no. 3 (January 11, 2018): 1014–25. http://dx.doi.org/10.1021/acs.iecr.7b04196.

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39

Li, Min, Rodrigo Silva, Frederico Guimaraes, and David Lowther. "A New Robust Dominance Criterion for Multiobjective Optimization." IEEE Transactions on Magnetics 51, no. 3 (March 2015): 1–4. http://dx.doi.org/10.1109/tmag.2014.2372692.

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40

Chen, W., and U. Mitra. "Training sequence optimization: comparisons and an alternative criterion." IEEE Transactions on Communications 48, no. 12 (2000): 1987–91. http://dx.doi.org/10.1109/26.891207.

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41

Zhang, Roxin, Bao Truong, and Qinghong Zhang. "Multistage hierarchical optimization problems with multi-criterion objectives." Journal of Industrial & Management Optimization 7, no. 1 (2011): 103–15. http://dx.doi.org/10.3934/jimo.2011.7.103.

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42

Costa, Nuno Ricardo, and Zulema Lopes Pereira. "Multiple response optimization: a global criterion-based method." Journal of Chemometrics 24, no. 6 (April 20, 2010): 333–42. http://dx.doi.org/10.1002/cem.1312.

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43

Sy, Bon K. "Probability model selection using information-theoretic optimization criterion." Journal of Statistical Computation and Simulation 69, no. 3 (June 2001): 203–24. http://dx.doi.org/10.1080/00949650108812091.

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44

Spiegel, Bernhard. "An interactive procedure for experimental multi-criterion optimization." Annual Review in Automatic Programming 12 (January 1985): 239–45. http://dx.doi.org/10.1016/0066-4138(85)90034-5.

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45

Gurevskii, E. E., V. A. Emelichev, and K. G. Kuz’min. "Stability criterion for a lexicographic Boolean optimization problem." Cybernetics and Systems Analysis 43, no. 4 (July 2007): 571–75. http://dx.doi.org/10.1007/s10559-007-0082-2.

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46

Esogbue, Augustine O., and Baoding Liu. "Reservoir operations optimization via fuzzy criterion decision processes." Fuzzy Optimization and Decision Making 5, no. 3 (July 2006): 289–305. http://dx.doi.org/10.1007/s10700-006-0015-y.

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47

Kravtsov, M. K., and A. Kh Sherman. "Solution of combinatorial optimization problems with minimax criterion." Cybernetics 25, no. 3 (1990): 371–79. http://dx.doi.org/10.1007/bf01069994.

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48

Sadek, Edward A. "An optimality criterion method for structural optimization problems." Computers & Structures 22, no. 5 (January 1986): 823–29. http://dx.doi.org/10.1016/0045-7949(86)90271-3.

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49

Bestle, D., and A. Wachal. "Multi-Criterion Optimization of a Pneumatic Vibroinsulation System." Multibody System Dynamics 13, no. 1 (February 2005): 39–52. http://dx.doi.org/10.1007/s11044-005-2518-7.

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50

Chen, Lingen, and Mohammad Hossein Ahmadi. "Thermodynamic optimization criterion for practical Meletis–Georgiou cycle." Energy Reports 8 (November 2022): 6023–34. http://dx.doi.org/10.1016/j.egyr.2022.04.039.

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