Relevant bibliographies by topics / Multicriteria 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 'Multicriteria Optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Multicriteria Optimization"

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Craft, David, Dualta McQuaid, Jeremiah Wala, Wei Chen, Ehsan Salari, and Thomas Bortfeld. "Multicriteria VMAT optimization." Medical Physics 39, no. 2 (January 12, 2012): 686–96. http://dx.doi.org/10.1118/1.3675601.

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Beaulieu, Luc, Hania Al-Hallaq, Benjamin S. Rosen, and David J. Carlson. "Multicriteria Optimization in Brachytherapy." International Journal of Radiation Oncology*Biology*Physics 114, no. 2 (October 2022): 177–80. http://dx.doi.org/10.1016/j.ijrobp.2022.05.022.

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Murdoch, Dr Tim. "Multicriteria optimization and engineering." Materials & Design 16, no. 2 (January 1995): 120–21. http://dx.doi.org/10.1016/0261-3069(95)90021-7.

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Lupşa, Liana, and Nicolae Popovici. "Generalized unimodal multicriteria optimization." Journal of Numerical Analysis and Approximation Theory 35, no. 1 (February 1, 2006): 65–70. http://dx.doi.org/10.33993/jnaat351-1012.

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The aim of this paper is to characterize the sets of weakly-efficient solutions and efficient solutions for multicriteria optimization problem involving generalized unimodal objective functions. An implementable algorithm which completely determines these sets is given for the particular framework of discrete feasible domains.
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Statnikov, Roman, Josef Matusov, Kirill Pyankov, and Alexander Statnikov. "Multicriteria Optimization of Cellular Networks." Open Journal of Optimization 02, no. 03 (2013): 53–60. http://dx.doi.org/10.4236/ojop.2013.23008.

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Popovici *, Nicolae. "Pareto reducible multicriteria optimization problems." Optimization 54, no. 3 (June 2005): 253–63. http://dx.doi.org/10.1080/02331930500096213.

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La Torre, Davide, and Nicolae Popovici. "Arcwise cone-quasiconvex multicriteria optimization." Operations Research Letters 38, no. 2 (March 2010): 143–46. http://dx.doi.org/10.1016/j.orl.2009.11.003.

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Chen, Huixiao, David L. Craft, and David P. Gierga. "Multicriteria optimization informed VMAT planning." Medical Dosimetry 39, no. 1 (2014): 64–73. http://dx.doi.org/10.1016/j.meddos.2013.10.001.

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Fliege, J., and L. N. Vicente. "Multicriteria Approach to Bilevel Optimization." Journal of Optimization Theory and Applications 131, no. 2 (November 8, 2006): 209–25. http://dx.doi.org/10.1007/s10957-006-9136-2.

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Frangopol, Dan M. "Multicriteria reliability-based structural optimization." Structural Safety 3, no. 1 (October 1985): 23–28. http://dx.doi.org/10.1016/0167-4730(85)90004-9.

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Dissertations / Theses on the topic "Multicriteria Optimization"

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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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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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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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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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Sarma, Kamal C. "Fuzzy discrete multicriteria cost optimization of steel structures using genetic algorithm /." The Ohio State University, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488205318509081.

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Buonanno, Michael Alexander. "A Method for Aircraft Concept Exploration using Multicriteria Interactive Genetic Algorithms." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7571.

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The problem of aircraft concept selection has become increasingly difficult in recent years due to changes in the primary evaluation criteria of concepts. In the past, performance was often the primary discriminator whereas modern programs have placed increased emphasis on factors such as environmental impact, economics, supportability, aesthetics, and other metrics. The revolutionary nature of the vehicles required to simultaneously meet these conflicting requirements has prompted a shift from design using historical data regression techniques for metric prediction to the use of sophisticated physics-based analysis tools that are capable of analyzing designs outside of the historical database. The use of optimization methods with these physics-based tools, however, has proven difficult because of the tendency of optimizers to exploit assumptions present in the models and drive the design towards a solution which, while promising to the computer, may be infeasible due to factors not considered by the computer codes. In addition to this difficulty, the number of discrete options available at this stage may be unmanageable due to the combinatorial nature of the concept selection problem, leading the analyst to select a sub-optimum baseline vehicle. Some extremely important concept decisions, such as the type of control surface arrangement to use, are frequently made without sufficient understanding of their impact on the important system metrics due to a lack of historical guidance, computational resources, or analysis tools. This thesis discusses the difficulties associated with revolutionary system design, and introduces several new techniques designed to remedy them. First, an interactive design method has been developed that allows the designer to provide feedback to a numerical optimization algorithm during runtime, thereby preventing the optimizer from exploiting weaknesses in the analytical model. This method can be used to account for subjective criteria, or as a crude measure of un-modeled quantitative criteria. Other contributions of the work include a modified Structured Genetic Algorithm that enables the efficient search of large combinatorial design hierarchies and an improved multi-objective optimization procedure that can effectively optimize several objectives simultaneously. A new conceptual design method has been created by drawing upon each of these new capabilities and aspects of more traditional design methods. The ability of this new technique to assist in the design of revolutionary vehicles has been demonstrated using a problem of contemporary interest: the concept exploration of a supersonic business jet. This problem was found to be a good demonstration case because of its novelty and unique requirements, and the results of this proof of concept exercise indicate that the new method is effective at providing additional insight into the relationship between a vehicle's requirements and its favorable attributes.
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Books on the topic "Multicriteria 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., 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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1948-, Matusov Joseph B., ed. Multicriteria optimization and engineering. New York: Chapman & Hall, 1995.

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

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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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Hans, Eschenauer, Koski Juhani 1947-, and Osyczka Andrzej, eds. Multicriteria design optimization: Procedures and applications. Berlin: Springer-Verlag, 1990.

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

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Book chapters on the topic "Multicriteria Optimization"

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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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Statnikov, Roman B. "Multicriteria Identification." In Applied Optimization, 143–72. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-2363-3_6.

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Xidonas, Panos, George Mavrotas, Theodore Krintas, John Psarras, and Constantin Zopounidis. "Portfolio Optimization." In Multicriteria Portfolio Management, 57–83. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3670-6_4.

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Koski, Juhani. "Multicriteria Truss Optimization." In Multicriteria Optimization in Engineering and in the Sciences, 263–307. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4899-3734-6_9.

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Eschenauer, H. A., J. Koski, and A. Osyczka. "Multicriteria Optimization — Fundamentals and Motivation." In Multicriteria Design Optimization, 1–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-48697-5_1.

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Eschenauer, H., W. Fuchs, P. U. Post, S. Adali, K. J. Duffy, K. H. Stenvers, J. Koski, and R. Silvennoinen. "Structures Made of Advanced Materials." In Multicriteria Design Optimization, 397–463. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-48697-5_10.

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Bremicker, M., H. A. Eschenauer, and P. U. Post. "Optimization Procedure SAPOP — A General Tool for Multicriteria Structural Designs." In Multicriteria Design Optimization, 35–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-48697-5_2.

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Eschenauer, H. A., A. Osyczka, and E. Schäfer. "Interactive Multicriteria Optimization in Design Process." In Multicriteria Design Optimization, 71–114. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-48697-5_3.

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Balachandran, M., and J. S. Gero. "Knowledge Engineering and Multicriteria Optimization." In Multicriteria Design Optimization, 115–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-48697-5_4.

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Koski, J., A. Osyczka, J. Zajac, F. Pfeiffer, H. H. Müller-Slany, D. H. van Campen, R. Nagtegaal, and A. J. G. Schoofs. "Mechanisms and Dynamic Systems." In Multicriteria Design Optimization, 151–228. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-48697-5_5.

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Conference papers on the topic "Multicriteria Optimization"

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Astapov, Victor, and Jelena Shuvalova. "Factors influencing multicriteria optimization process." In 2015 16th International Scientific Conference on Electric Power Engineering (EPE). IEEE, 2015. http://dx.doi.org/10.1109/epe.2015.7161190.

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Bucoń, Robert, and Michał Tomczak. "MULTICRITERIA OPTIMIZATION OF BUILDING RENOVATION." In 24th International Academic Conference, Barcelona. International Institute of Social and Economic Sciences, 2016. http://dx.doi.org/10.20472/iac.2016.024.013.

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Bobak, Martin, Ladislav Hluchy, and Viet Tran. "Methodology for intercloud multicriteria optimization." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7382217.

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Joswig, Michael, and Georg Loho. "Monomial tropical cones for multicriteria optimization." In PROCEEDINGS LEGO – 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP. Author(s), 2019. http://dx.doi.org/10.1063/1.5089992.

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Vittal, Sameer, and Prabhat Hajela. "Approaches to Reliability Based Multicriteria Optimization." In 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-5583.

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Pavlichenko, Dmytro, and Sven Behnke. "Efficient stochastic multicriteria arm trajectory optimization." In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017. http://dx.doi.org/10.1109/iros.2017.8206256.

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Nelson, Sigurd A., Matthew B. Parkinson, and Panos Y. Papalambros. "Multicriteria Optimization in Product Platform Design." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dac-8676.

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Abstract A product platform is a set of common components, modules or parts from which a stream of derivative products can be created. Product platform design requires selection of the shared parts and assessment of the potential sacrifices in individual product performance that result from parts sharing. A multicriteria optimization problem can be formulated to study such decisions in a quantitative manner at the product performance level. Studying the Pareto sets that correspond to various derivative products leads to a systematic methodology for design decision making. A brad-nail gun is used to illustrate the concepts presented.
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Bezruk, Valery, and Dariy Rybalko. "Multicriteria Optimization in Telecommunication Networks Planning." In 2007 17th International Crimean Conference - Microwave & Telecommunication Technology. IEEE, 2007. http://dx.doi.org/10.1109/crmico.2007.4368739.

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Koski, Juhani. "Multicriteria Optimization in Structural Design: State of the Art." In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0353.

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Abstract The purpose of this article is to give a general description of the research work made in the field of multicriteria structural optimization. More than eighty publications have been considered in this study where completed works rather than open questions in the field are particularly emphasized. The basic concepts, especially Pareto optimality, and the motivation of the multicriteria approach are briefly discussed. The classification of the multicriteria structural design process is proposed and it is used in describing the published applications.
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Evtushenko, Yuri, and Mikhail Posypkin. "A deterministic method for constrained multicriteria optimization." In 2015 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO). IEEE, 2015. http://dx.doi.org/10.1109/icmsao.2015.7152218.

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Reports on the topic "Multicriteria 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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