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Готові списки джерел за темами / Multicriterial optimization

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Добірка наукової літератури з теми "Multicriterial optimization"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 21 червня 2025

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Статті в журналах з теми "Multicriterial optimization"

1

Podinovski, V. V. "Potential optimality in multicriterial optimization." Computational Mathematics and Mathematical Physics 54, no. 3 (2014): 429–38. http://dx.doi.org/10.1134/s0965542514030154.

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2

Zypkin, Ya Z., and A. S. Krasnenker. "Man Machine Methods for Multicriterial Optimization." IFAC Proceedings Volumes 21, no. 19 (1988): 271–72. http://dx.doi.org/10.1016/s1474-6670(17)54504-8.

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3

Vuleta, Jovo. "Visekriterijumska optimizacija izbora izvodjaca projekta." Ekonomski anali 44, no. 157 (2003): 7–40. http://dx.doi.org/10.2298/eka0357007v.

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The selection of the best (multicriterial optimal) contractors for project realization is analised in this paper. This problem is one of the most important problems that occurs during the realization of every project, especially the complex one. First we point the problem importance and past experiences and results in its solving. As a conclusion, we state that the problem of selection project realization contractors has been solved by discovering any possible solution, not necessary the optimal one. We have tried to solve one real problem using the model of integer multicriterial optimization type 0-1. The problem was presented by the appropriate mathematical model whose solving leads to multicriterial optimal solution. The special attention was paid to technique and procedure for solving the given model of integer multicriterial optimization. In order to minimize the efforts, the model has been transformed in corresponding network model whose further solving is based on the theory of graphs. The presented procedure decreases the number of mathematical operations and is more simply than most of the usual methods for solving the integer multicriterial type 0-1 optimization problems. At the end, the recommended procedure has been illustrated by a numerical example.

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4

Bogó-Tóth, Zs, and Z. Lakner. "Multicriterial optimization of liquid food packaging systems." Acta Alimentaria 43, Supplement 1 (2014): 29–35. http://dx.doi.org/10.1556/aalim.43.2014.suppl.5.

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5

Vladimirova, L. V. "Multicriterial approach to beam dynamics optimization problem." Journal of Physics: Conference Series 747 (September 2016): 012070. http://dx.doi.org/10.1088/1742-6596/747/1/012070.

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6

Gawlicki, Michał, and Łukasz Jankowski. "Trajectory Identification for Moving Loads by Multicriterial Optimization." Sensors 21, no. 1 (2021): 304. http://dx.doi.org/10.3390/s21010304.

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Moving load is a fundamental loading pattern for many civil engineering structures and machines. This paper proposes and experimentally verifies an approach for indirect identification of 2D trajectories of moving loads. In line with the “structure as a sensor” paradigm, the identification is performed indirectly, based on the measured mechanical response of the structure. However, trivial solutions that directly fit the mechanical response tend to be erratic due to measurement and modeling errors. To achieve physically meaningful results, these solutions need to be numerically regularized with respect to expected geometric characteristics of trajectories. This paper proposes a respective multicriterial optimization framework based on two groups of criteria of a very different nature: mechanical (to fit the measured response of the structure) and geometric (to account for the geometric regularity of typical trajectories). The state-of-the-art multiobjective genetic algorithm NSGA-II is used to find the Pareto front. The proposed approach is verified experimentally using a lab setup consisting of a plate instrumented with strain gauges and a line-follower robot. Three trajectories are tested, and in each case the determined Pareto front is found to properly balance between the mechanical response fit and the geometric regularity of the trajectory.

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7

Staib, Tilo. "Necessary Optimality Conditions for Nonsmooth Multicriterial Optimization Problems." SIAM Journal on Optimization 2, no. 1 (1992): 153–71. http://dx.doi.org/10.1137/0802009.

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8

Kokhanovskii, V. A., and D. V. Glazunov. "Multicriterial Optimization of the Composition of a Lubricant." Journal of Machinery Manufacture and Reliability 49, no. 7 (2020): 624–32. http://dx.doi.org/10.3103/s1052618820070080.

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9

Koleva, E., L. Koleva, Dm Trushnikov, G. Kolev, and Z. Petrova. "Multicriterial optimization strategies for electron beam welding processes." Journal of Physics: Conference Series 2240, no. 1 (2022): 012038. http://dx.doi.org/10.1088/1742-6596/2240/1/012038.

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Abstract The electron beam welding process is analyzed of high-strength steel type 15Cr5Mo samples with deflection oscillations along the zone of interaction. The geometries of the molten and the heat affected zones are studied in order to investigate the influence of the deflection oscillations parameters and to improve the seam quality. A robust engineering approach is implemented for the case of production conditions considering the errors in the process parameter settings. The problem of quality improvement through fulfilling pre-set technological and quality requirements is solved by scalarization of the vector criteria by implementing a reference point strategy. Other multicriterial optimization strategies are also proposed.

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10

Bucur, Amelia. "Aspects Of Multicriterial Mathematical Modeling And Of The Fuzzy Formalism For The Hierarchization Of Study Programs Based On Several Quality Characteristics." ACTA Universitatis Cibiniensis 67, no. 1 (2015): 1–6. http://dx.doi.org/10.1515/aucts-2015-0055.

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Анотація:

Abstract The aim of this paper is to present aspects of mathematical modeling for the hierarchization of study programs from universities, based on several quality characteristics. The tools used pertain to multicriterial optimization, to the different methods of assessing importance coefficients, to the utility theory, the fuzzy formalism, and to the fuzzy simple additive weighting method. The conclusion is that multicriterial decision-making methods can be efficiently used in assessing the quality of study programs, noting that, just like other methods from the decision theory, the multicriterial decision-making methods highlight aspects of problems differently, therefore, there can be no comparison or competitiveness between them, and choosing one over the other is up to the decision-maker.

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