Relevant bibliographies by topics / Multicriterial optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multicriterial optimization'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Multicriterial optimization"

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

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

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

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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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Gawlicki, Michał, and Łukasz Jankowski. "Trajectory Identification for Moving Loads by Multicriterial Optimization." Sensors 21, no. 1 (January 5, 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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Staib, Tilo. "Necessary Optimality Conditions for Nonsmooth Multicriterial Optimization Problems." SIAM Journal on Optimization 2, no. 1 (February 1992): 153–71. http://dx.doi.org/10.1137/0802009.

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

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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 (March 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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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 (September 1, 2015): 1–6. http://dx.doi.org/10.1515/aucts-2015-0055.

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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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Dissertations / Theses on the topic "Multicriterial optimization"

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Burggraf, Timo Verfasser], Stefan [Akademischer Betreuer] [Ulbrich, and Christian [Akademischer Betreuer] Beidl. "Development of an automatic, multidimensional, multicriterial optimization algorithm for the calibration of internal combustion engines / Timo Burggraf. Betreuer: Stefan Ulbrich ; Christian Beidl." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2015. http://d-nb.info/1111112231/34.

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Burggraf, Timo [Verfasser], Stefan [Akademischer Betreuer] Ulbrich, and Christian [Akademischer Betreuer] Beidl. "Development of an automatic, multidimensional, multicriterial optimization algorithm for the calibration of internal combustion engines / Timo Burggraf. Betreuer: Stefan Ulbrich ; Christian Beidl." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2015. http://nbn-resolving.de/urn:nbn:de:tuda-tuprints-43987.

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Dächert, Kerstin [Verfasser]. "Adaptive Parametric Scalarizations in Multicriteria Optimization / Kerstin Dächert." Wuppertal : Universitätsbibliothek Wuppertal, 2014. http://d-nb.info/1054221308/34.

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Filomeno, Coelho Rajan. "Multicriteria optimization with expert rules for mechanical design." Doctoral thesis, Universite Libre de Bruxelles, 2004. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211184.

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Though lots of numerical methods have been proposed in the literature to optimize me-chanical structures at the final stage of the design process, few designers use these tools since the first stage. However, a minor modification at the first step can bring significant change to the global performances of the structure. Usually, during the initial stage, models are based on theoretical and empirical equations, which are often characterized by mixed variables: continuous (e.g. geometrical dimensions), discrete (e.g. the cross section of a beam available in a catalogue) and/or integer (e.g. the number of layers in a composite material). Furthermore, the functions involved may be non differentiable, or even discontinuous. Therefore, classical algorithms based on the computation of sensi-tivities are no more applicable.

Consequently, to solve these problems, the most wide-spread meta-heuristic methods are evolutionary algorithms (EAs), which work as follows: the best individuals among an initial population of randomly generated potential solutions are favoured and com-bined (by specific operators like crossover and mutation) in order to create potentially better individuals at the next generation. The creation of new generations is repeated till the convergence is reached. The ability of EAs to explore widely the design space is useful to solve single-objective unconstrained optimization problems, because it gener-ally prevents from getting trapped into a local optimum, but it is also well known that they do not perform very efficiently in the presence of constraints. Furthermore, in many industrial applications, multiple objectives are pursued together.

Therefore, to take into account the constrained and multicriteria aspects of optimization problems in EAs, a new method called PAMUC (Preferences Applied to MUltiobjectiv-ity and Constraints) has been proposed in this dissertation. First the user has to assign weights to the m objectives. Then, an additional objective function is built by linearly aggregating the normalized constraints. Finally, a multicriteria decision aid method, PROMETHEE II, is used in order to rank the individuals of the population following the m+1 objectives.

PAMUC has been validated on standard multiobjective test cases, as well as on the pa-rametrical optimization of the purge valve and the feed valve of the Vinci engine, both designed by Techspace Aero for launcher Ariane 5.

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Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished

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Zhang, Tianfang. "Machine learning multicriteria optimization in radiation therapy treatment planning." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-257509.

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In radiation therapy treatment planning, recent works have used machine learning based on historically delivered plans to automate the process of producing clinically acceptable plans. Compared to traditional approaches such as repeated weighted-sum optimization or multicriteria optimization (MCO), automated planning methods have, in general, the benefits of low computational times and minimal user interaction, but on the other hand lack the flexibility associated with general-purpose frameworks such as MCO. Machine learning approaches can be especially sensitive to deviations in their dose prediction due to certain properties of the optimization functions usually used for dose mimicking and, moreover, suffer from the fact that there exists no general causality between prediction accuracy and optimized plan quality.In this thesis, we present a means of unifying ideas from machine learning planning methods with the well-established MCO framework. More precisely, given prior knowledge in the form of either a previously optimized plan or a set of historically delivered clinical plans, we are able to automatically generate Pareto optimal plans spanning a dose region corresponding to plans which are achievable as well as clinically acceptable. For the former case, this is achieved by introducing dose--volume constraints; for the latter case, this is achieved by fitting a weighted-data Gaussian mixture model on pre-defined dose statistics using the expectation--maximization algorithm, modifying it with exponential tilting and using specially developed optimization functions to take into account prediction uncertainties.Numerical results for conceptual demonstration are obtained for a prostate cancer case with treatment delivered by a volumetric-modulated arc therapy technique, where it is shown that the methods developed in the thesis are successful in automatically generating Pareto optimal plans of satisfactory quality and diversity, while excluding clinically irrelevant dose regions. For the case of using historical plans as prior knowledge, the computational times are significantly shorter than those typical of conventional MCO.
Inom strålterapiplanering har den senaste forskningen använt maskininlärning baserat på historiskt levererade planer för att automatisera den process i vilken kliniskt acceptabla planer produceras. Jämfört med traditionella angreppssätt, såsom upprepad optimering av en viktad målfunktion eller flermålsoptimering (MCO), har automatiska planeringsmetoder generellt sett fördelarna av lägre beräkningstider och minimal användarinteraktion, men saknar däremot flexibiliteten hos allmänna ramverk som exempelvis MCO. Maskininlärningsmetoder kan vara speciellt känsliga för avvikelser i dosprediktionssteget på grund av särskilda egenskaper hos de optimeringsfunktioner som vanligtvis används för att återskapa dosfördelningar, och lider dessutom av problemet att det inte finns något allmängiltigt orsakssamband mellan prediktionsnoggrannhet och kvalitet hos optimerad plan. I detta arbete presenterar vi ett sätt att förena idéer från maskininlärningsbaserade planeringsmetoder med det väletablerade MCO-ramverket. Mer precist kan vi, givet förkunskaper i form av antingen en tidigare optimerad plan eller en uppsättning av historiskt levererade kliniska planer, automatiskt generera Paretooptimala planer som täcker en dosregion motsvarande uppnåeliga såväl som kliniskt acceptabla planer. I det förra fallet görs detta genom att introducera dos--volym-bivillkor; i det senare fallet görs detta genom att anpassa en gaussisk blandningsmodell med viktade data med förväntning--maximering-algoritmen, modifiera den med exponentiell lutning och sedan använda speciellt utvecklade optimeringsfunktioner för att ta hänsyn till prediktionsosäkerheter.Numeriska resultat för konceptuell demonstration erhålls för ett fall av prostatacancer varvid behandlingen levererades med volymetriskt modulerad bågterapi, där det visas att metoderna utvecklade i detta arbete är framgångsrika i att automatiskt generera Paretooptimala planer med tillfredsställande kvalitet och variation medan kliniskt irrelevanta dosregioner utesluts. I fallet då historiska planer används som förkunskap är beräkningstiderna markant kortare än för konventionell MCO.
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Bokrantz, Rasmus. "Multicriteria optimization for managing tradeoffs in radiation therapy treatment planning." Doctoral thesis, KTH, Optimeringslära och systemteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-122663.

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Treatment planning for radiation therapy inherently involves tradeoffs, such as between tumor control and normal tissue sparing, between time-efficiency and dose quality, and between nominal plan quality and robustness. The purpose of this thesis is to develop methods that can facilitate decision making related to such tradeoffs. The main focus of the thesis is on multicriteria optimization methods where a representative set of treatment plans are first calculated and the most appropriate plan contained in this representation then selected by the treatment planner through continuous interpolation between the precalculated alternatives. These alternatives constitute a subset of the set of Pareto optimal plans, meaning plans such that no criterion can be improved without a sacrifice in another. Approximation of Pareto optimal sets is first studied with respect to fluence map optimization for intensity-modulated radiation therapy. The approximation error of a discrete representation is minimized by calculation of points one at the time at the location where the distance between an inner and outer approximation of the Pareto set currently attains its maximum. A technique for calculating this distance that is orders of magnitude more efficient than the best previous method is presented. A generalization to distributed computational environments is also proposed. Approximation of Pareto optimal sets is also considered with respect to direct machine parameter optimization. Optimization of this form is used to calculate representations where any interpolated treatment plan is directly deliverable. The fact that finite representations of Pareto optimal sets have approximation errors with respect to Pareto optimality is addressed by a technique that removes these errors by a projection onto the exact Pareto set. Projections are also studied subject to constraints that prevent the dose-volume histogram from deteriorating. Multicriteria optimization is extended to treatment planning for volumetric-modulated arc therapy and intensity-modulated proton therapy. Proton therapy plans that are robust against geometric errors are calculated by optimization of the worst case outcome. The theory for multicriteria optimization is extended to accommodate this formulation. Worst case optimization is shown to be preferable to a previous more conservative method that also protects against uncertainties which cannot be realized in practice.
En viktig aspekt av planering av strålterapibehandlingar är avvägningar mellan behandlingsmål vilka står i konflikt med varandra. Exempel på sådana avvägningar är mellan tumörkontroll och dos till omkringliggande frisk vävnad, mellan behandlingstid och doskvalitet, och mellan nominell plankvalitet och robusthet med avseende på geometriska fel. Denna avhandling syftar till att utveckla metoder som kan underlätta beslutsfattande kring motstridiga behandlingsmål. Primärt studeras en metod för flermålsoptimering där behandlingsplanen väljs genom kontinuerlig interpolation över ett representativt urval av förberäknade alternativ. De förberäknade behandlingsplanerna utgör en delmängd av de Paretooptimala planerna, det vill säga de planer sådana att en förbättring enligt ett kriterium inte kan ske annat än genom en försämring enligt ett annat. Beräkning av en approximativ representation av mängden av Paretooptimala planer studeras först med avseende på fluensoptimering för intensitetsmodulerad strålterapi. Felet för den approximativa representationen minimeras genom att innesluta mängden av Paretooptimala planer mellan inre och yttre approximationer. Dessa approximationer förfinas iterativt genom att varje ny plan genereras där avståndet mellan approximationerna för tillfället är som störst. En teknik för att beräkna det maximala avståndet mellan approximationerna föreslås vilken är flera storleksordningar snabbare än den bästa tidigare kända metoden. En generalisering till distribuerade beräkningsmiljöer föreslås även. Approximation av mängden av Paretooptimala planer studeras även för direkt maskinparameteroptimering, som används för att beräkna representationer där varje interpolerad behandlingsplan är direkt levererbar. Det faktum att en ändlig representation av mängden av Paretooptimala lösningar har ett approximationsfel till Paretooptimalitet hanteras via en metod där en interpolerad behandlingsplan projiceras på Paretomängden. Projektioner studeras även under bivillkor som förhindrar att den interpolerade planens dos-volym histogram kan försämras. Flermålsoptimering utökas till planering av rotationsterapi och intensitetsmodulerad protonterapi. Protonplaner som är robusta mot geometriska fel beräknas genom optimering med avseende på det värsta möjliga utfallet av de föreliggande osäkerheterna. Flermålsoptimering utökas även teoretiskt till att innefatta denna formulering. Nyttan av värsta fallet-optimering jämfört med tidigare mer konservativa metoder som även skyddar mot osäkerheter som inte kan realiseras i praktiken demonstreras experimentellt.

QC 20130527

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Sánchez, Corrales Helem Sabina. "Multi-objective optimization and multicriteria design of PI /PID controllers." Doctoral thesis, Universitat Autònoma de Barcelona, 2016. http://hdl.handle.net/10803/393990.

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Hoy en día, los controladores proporcionales integrales y proporcionales integrales derivativos son los algoritmos de control más utilizado en la industria. Por otra parte, los controladores fraccionarios han recibido atención recientemente, por parte de la comunidad científica y desde el punto de vista industrial. Debido a esto, en esta tesis algunos de los escenarios implican la sintonización de estos controladores mediante el procedimiento de diseño mediante la optimización multi-objetivo. Este procedimiento se centra en proporcionar un equilibrio razonable entre los objetivos en conflicto y brinda al diseñador la posibilidad de apreciar la comparación de los objetivos de diseño. Esta tesis se divide en tres partes. La primera parte, presenta los fundamentos del sistema de control y discusión de los diferentes compromisos: entre los modos de operación servo / regulación y del rendimiento / robustez. Por otro lado, se ha proporcionado un marco conceptual acerca de la optimización multi-objetivo. La segunda parte, introduce la solución de Nash como una técnica de selección multi-criterio, para seleccionar un punto del frente de Pareto, que represente el mejor compromiso entre los objetivos de diseño. Esta solución es una selección semi-automática escogida en la aproximación del frente de Pareto y ofrece un buen compromiso entre los objetivos de diseño. Luego, se presenta el Multi-stage approach para el proceso de optimización multi-objetivo. Este enfoque implica dos algoritmos: un algoritmo determinista y algoritmo evolutivo. En el cual ambos algoritmos se complementen entre sí a pesar de sus desventajas y mejoran los resultados de la optimización en términos de convergencia y precisión. Además, se introduce el objetivo basado en la fiabilidad, en la descripción del problema multi-objetivo, este se utiliza para medir la degradación del rendimiento. Vale la pena mencionar que, debido a la existencia de incertidumbres en el diseño y fabricación, teniendo este objetivo de diseño le dará otra perspectiva al diseñador en el mundo real. Con el fin de validar el método, dos casos de estudios se ha considerado, el problema de control de la caldera (The Boiler Control Benchmark) para la sintonización de controladores y como segundo caso, una pila Peltier nolineal. Por último, la tercera parte de esta tesis, presentan las contribuciones a la sintonización de controladores. En primer lugar, se propone un conjunto de reglas de sintonía basado en la solución de Nash para un controlador proporcional-integral, en donde la robustez / rendimiento han sido considerados. Por otra parte, como un segundo caso se presenta las reglas de sintonía para un controlador proporcional-integral-derivativo, donde se han considerado el compromiso de robustez/rendimiento y los modos de operación servo / regulación. Además, se proponen reglas de sintonía para el controlador proporcional-integral-derivativo-fraccional-orden implementado el Multi-stage approach para la optimización multi-objetivo.
Nowadays, the proportional integral and proportional integral derivatives are the most used control algorithm in the industry. Moreover, the fractional controllers have received attention recently for both, the research community and from the industrial point of view. Owing to this, in this thesis some of the scenarios involve the tuning of these controllers by using the Multiobjective Optimization Design procedure. This procedure focuses on providing reasonable trade-off among the conflictive objectives and brings the designer the possibility to appreciate the comparison of the design objectives. This thesis is divided in three parts. The first part, presented the fundamentals of the control system showing and discussing the different trade-offs between performance/robustness and servo/regulation operation modes. On the other hand a background on multi-objective optimization has been provided. The second part, introduces the Nash solution as a multi-criteria decision making technique, to select a point from the Pareto front that represent the best compromise among the design objective. This solution provides a semi-automatic selection from the Pareto front approximation and offers a good trade-off between the goal objectives. Hereafter, a Multi-stage approach for the multi-objective optimization process is presented. This approach involves two algorithms: a deterministic and evolutionary algorithm. In which both algorithms complement each other in despite of their drawbacks and improve the results of the overall optimization in terms of convergence and accuracy. Further, the introduction of reliability based objective into the multi-objective problem is carried out, to measure the performance degradation. It is worthwhile to mention that, due to the existence of uncertainties in real-world designing and manufacturing having this design objective will give another perspective to the designer. In order to validate the approach, two different case studies has been considered, the Boiler control problem for controller tuning and as second case, a non-linear Peltier Cell. Finally, the third part of this thesis, the contributions on controller tuning have been presented. First, a set of tuning rules based on the NS for a proportional-integral (PI) controller have been devised, where the robustness/performance trade-off have been considered. Moreover, as a second case it is presented a tuning for proportional-integral-derivative controller where the trade-off of the performance/robustness and servo/regulation operation mode has been considered. Moreover, the fractional-order-proportional-integral-derivative controller is tuned by using the Multi-stage approach for the MOO process.
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Schott, Jason R. (Jason Ramon). "Fault tolerant design using single and multicriteria genetic algorithm optimization." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11582.

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Arreola-Risa, Jesus S. "Multicriteria optimization for design of multivariate control charts for manufacturing processes." Diss., Georgia Institute of Technology, 1989. http://hdl.handle.net/1853/27997.

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Heiserer, Daniel F. [Verfasser]. "Fast Reanalysis for Large Scale Multicriteria Structural Optimization / Daniel F Heiserer." Aachen : Shaker, 2005. http://d-nb.info/1186576960/34.

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Books on the topic "Multicriterial optimization"

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Ehrgott, Matthias. Multicriteria Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-22199-0.

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Multicriteria optimization. 2nd ed. Berlin: Springer, 2005.

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Eschenauer, Hans, Juhani Koski, and Andrzej Osyczka, eds. Multicriteria Design Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-48697-5.

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Statnikov, Roman B. Multicriteria Optimization and Engineering. Boston, MA: Springer US, 1995.

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1948-, Matusov Joseph B., ed. Multicriteria optimization and engineering. New York: Chapman & Hall, 1995.

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Statnikov, Roman B., and Joseph B. Matusov. Multicriteria Optimization and Engineering. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2089-4.

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Statnikov, Roman B. Multicriteria Design: Optimization and Identification. Dordrecht: Springer Netherlands, 1999.

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B, Statnikov R. Multicriteria design: Optimization and identification. Dordrecht: Kluwer Academic, 1999.

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Eschenauer, Hans. Multicriteria Design Optimization: Procedures and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990.

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Pitel, Jozef. Multicriterion optimization and its utilization in agriculture. Amsterdam: Elsevier, 1990.

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Book chapters on the topic "Multicriterial optimization"

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Klapka, Jindřich, Petr Piňos, and Vítězslav Ševčík. "Multicriterial Projects Selection." In Handbook of Optimization, 245–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30504-7_10.

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Rabinovitch, Mark. "Multicriterial Optimization in Production and Management." In Operations Research ’93, 405–7. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-46955-8_99.

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Gergel, Victor, and Evgeny Kozinov. "GPU-Based Parallel Computations in Multicriterial Optimization." In Communications in Computer and Information Science, 88–100. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05807-4_8.

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Gergel, Victor, and Evgeny Kozinov. "Parallel Computing for Time-Consuming Multicriterial Optimization Problems." In Lecture Notes in Computer Science, 446–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62932-2_43.

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Rotar, Corina. "An Evolutionary Technique for Multicriterial Optimization Based on Endocrine Paradigm." In Genetic and Evolutionary Computation – GECCO 2004, 414–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24855-2_50.

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Muzalewska, Małgorzata, and Wojciech Moczulski. "Methodology of multicriterial optimization of geometric features of an orthopedic implant." In Innovations in Biomedical Engineering, 289–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70063-2_31.

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Zawidzki, Machi, and Łukasz Jankowski. "Multicriterial Optimization of Geometrical and Structural Properties of the Basic Module of a Single-Branch Truss-Z Structure." In Advances in Structural and Multidisciplinary Optimization, 163–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67988-4_11.

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Gergel, Victor, and Evgeny Kozinov. "Efficient Methods of Multicriterial Optimization Based on the Intensive Use of Search Information." In Springer Proceedings in Mathematics & Statistics, 27–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56829-4_3.

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Gergel, Victor, and Evgeny Kozinov. "An Approach for Parallel Solving the Multicriterial Optimization Problems with Non-convex Constraints." In Communications in Computer and Information Science, 121–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71255-0_10.

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Spillers, William R., and Keith M. MacBain. "Multicriteria Optimization." In Structural Optimization, 175–78. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-95865-1_8.

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Conference papers on the topic "Multicriterial optimization"

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Bezruk, Valery, Daria Chebotaryova, and Yuliia Skoryk. "Multicriterial optimization of communication means." In 2022 IEEE 16th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). IEEE, 2022. http://dx.doi.org/10.1109/tcset55632.2022.9766957.

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Rijavec, Nenad, and Arianne Hinds. "Multicriterial Optimization Approach to Eliminating Multiplications." In 2006 IEEE Workshop on Multimedia Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/mmsp.2006.285332.

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Zanic, Vedran, Stanislav Kitarovic, and Pero Prebeg. "Safety as Objective in Multicriterial Structural Optimization." In ASME 2010 29th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/omae2010-20712.

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Abstract:
Multicriterial design methodology with safety as one of the design objectives is presented. The aim of the paper is to analyze the influence of safety based design objectives on generated nondominated designs on the Pareto frontier. Possible improvements in nondominated designs are investigated by comparison to ones obtained with the standard design procedure when safety criteria are used as design constraints only. It is assumed that safety based objectives and targets act as attractors, driving nondominated designs along the constant cost/weight contours in design space towards its safer regions. Global safety objectives (for hogging/sagging modes), are based on the maximization of ultimate longitudinal strength in vertical bending calculated via the extended IACS incremental-iterative method. Applied compound safety measures for gross-panel (stiffened panel with associated girders) are based upon 34 failure modes, belonging to serviceability/collapse subsets. Objectives based on the maximization of safety measures are applied together with standard design objectives such as minimization of initial cost and weight. The following problems were solved with different sets of objectives: (a) minimize cost and weight objectives subject to safety constraints (used for reference), (b) only the maximization of local safety measures is added to (a) as additional objectives, (c) only the maximization of global safety measures is added to (a) as additional objectives, (d) maximization of safety measures ad (b) and (c) are added to (a) as additional objectives. For each of the problems (a–d) the developed design procedure is executed. It contains two basic tasks for structural design of realistic (non-academic) problems: (1) multicriterial optimization with topology / geometry design variables; (2) multicriterial optimization of gross-panels with scantling / material design variables. Design procedure steps are executed using a fast and balanced collection of analysis and synthesis modules/methods of the OCTOPUS design system: • Determination of design load sets; • MOGA / MOPSO based generation of nondominated designs for the selected ship structure; • For each design the following analysis blocks are executed: – calculation of ship’s primary and racking response fields, – calculation of ship’s ultimate longitudinal strength, – calculation of serviceability and collapse safety criteria on the gross-panel level. Comparisons of results, based on generated Pareto hyper-surfaces and on subset of preferred designs, are given for problems (a–d). Insights into the results of optimization process, using 5-D graphics for design and attribute spaces, are also presented. Design problems of modern RoPax and SWATH structures are used in case studies.
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Zavalishchin, Dmitry, and Galina Timofeeva. "Multicriterial optimization of transportation based on customers probabilistic preferences." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2019 (ICCMSE-2019). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5137935.

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Gergel, Victor, and Evgeny Kozinov. "Accelerating multicriterial optimization by the intensive exploitation of accumulated search data." In NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2016): Proceedings of the 2nd International Conference “Numerical Computations: Theory and Algorithms”. Author(s), 2016. http://dx.doi.org/10.1063/1.4965367.

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Guini, Fatimazahra, Abdellah El Barkany, and Abdelouahhab Jabri. "Multicriterial evaluation of process planning of a new product in the stage of its design." In 2018 4th International Conference on Optimization and Applications (ICOA). IEEE, 2018. http://dx.doi.org/10.1109/icoa.2018.8370571.

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Kishkin, Krasimir, Dimitar Arnaudov, Venelin Todorov, and Stefka Fidanova. "Multicriterial evaluation and optimization of an algorithm for charging energy storage elements." In 16th Conference on Computer Science and Intelligence Systems. PTI, 2021. http://dx.doi.org/10.15439/2021f55.

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Forth, Kasimir, Jimmy Abualdenien, André Borrmann, Sabrina Fellermann, and Christian Schunicht. "Design optimization approach comparing multicriterial variants using BIM in early design stages." In 38th International Symposium on Automation and Robotics in Construction. International Association for Automation and Robotics in Construction (IAARC), 2021. http://dx.doi.org/10.22260/isarc2021/0034.

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Emelichev, Vladimir, and Vladimir Korotkov. "Investigation in stability of Markowitz's multicriterial portfolio optimization problem with Wald's maximin criteria in euclidean metric." In 2012 IV International Conference "Problems of Cybernetics and Informatics" (PCI). IEEE, 2012. http://dx.doi.org/10.1109/icpci.2012.6486478.

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Shorikov, A. F., and E. V. Butsenko. "Network models for solving the problem of multicriterial adaptive optimization of investment projects control with several acceptable technologies." In APPLICATION OF MATHEMATICS IN TECHNICAL AND NATURAL SCIENCES: 9th International Conference for Promoting the Application of Mathematics in Technical and Natural Sciences - AMiTaNS’17. Author(s), 2017. http://dx.doi.org/10.1063/1.5007385.

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Reports on the topic "Multicriterial optimization"

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Stepanović, Milica, Dragoljub Bajić, and Dušan Polomši. Multicriteria Analysis and Optimization of Groundwater Control Systems with Variable Values of Criterion over Predefined Time Points. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, August 2021. http://dx.doi.org/10.7546/crabs.2021.08.09.

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