Relevant bibliographies by topics / Multi-Objective Optimization

Academic literature on the topic 'Multi-Objective Optimization'

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Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Multi-Objective Optimization"

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Xu, Liansong, and Dazhi Pan. "Multi-objective Optimization Based on Chaotic Particle Swarm Optimization." International Journal of Machine Learning and Computing 8, no. 3 (June 2018): 229–35. http://dx.doi.org/10.18178/ijmlc.2018.8.3.692.

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Mueller, Carsten. "Multi-Objective Optimization of Software Architectures Using Ant Colony Optimization." Lecture Notes on Software Engineering 2, no. 4 (2014): 371–74. http://dx.doi.org/10.7763/lnse.2014.v2.152.

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Velea, Marian N., and Simona Lache. "Decision Making Process on Multi-Objective Optimization Results." International Journal of Materials, Mechanics and Manufacturing 4, no. 3 (2015): 213–17. http://dx.doi.org/10.7763/ijmmm.2016.v4.259.

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Lee, Chen Jian Ken, and Hirohisa Noguchi. "515 Multi-objective topology optimization involving 3D surfaces." Proceedings of The Computational Mechanics Conference 2008.21 (2008): 233–34. http://dx.doi.org/10.1299/jsmecmd.2008.21.233.

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Coello Coello, Carlos A., Arturo Hernández Aguirre, and Eckart Zitzler. "Evolutionary multi-objective optimization." European Journal of Operational Research 181, no. 3 (September 2007): 1617–19. http://dx.doi.org/10.1016/j.ejor.2006.08.003.

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Sörensen, Kenneth, and Johan Springael. "Progressive Multi-Objective Optimization." International Journal of Information Technology & Decision Making 13, no. 05 (September 2014): 917–36. http://dx.doi.org/10.1142/s0219622014500308.

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This paper introduces progressive multi-objective optimization (PMOO), a novel technique to include the decision maker's preferences into the multi-objective optimization process. PMOO integrates a well-known method for multi-criteria decision making (PROMETHEE) into a simple multi-objective metaheuristic by maintaining and updating a small reference archive of nondominated solutions throughout the search. By applying this novel technique to a set of instances of the multi-objective knapsack problem, the superiority of PMOO over the commonly accepted sequential approach of generating a Pareto set approximation first and selecting a single solution afterwards is demonstrated.
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Luo, Jianping, Yun Yang, Qiqi Liu, Xia Li, Minrong Chen, and Kaizhou Gao. "A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization." Information Sciences 448-449 (June 2018): 164–86. http://dx.doi.org/10.1016/j.ins.2018.03.012.

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Zhang, Kai, Minshi Chen, Xin Xu, and Gary G. Yen. "Multi-objective evolution strategy for multimodal multi-objective optimization." Applied Soft Computing 101 (March 2021): 107004. http://dx.doi.org/10.1016/j.asoc.2020.107004.

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Feng, Huijun, Wei Tang, Lingen Chen, Junchao Shi, and Zhixiang Wu. "Multi-Objective Constructal Optimization for Marine Condensers." Energies 14, no. 17 (September 5, 2021): 5545. http://dx.doi.org/10.3390/en14175545.

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A marine condenser with exhausted steam as the working fluid is researched in this paper. Constructal designs of the condenser are numerically conducted based on single and multi-objective optimizations, respectively. In the single objective optimization, there is an optimal dimensionless tube diameter leading to the minimum total pumping power required by the condenser. After constructal optimization, the total pumping power is decreased by 42.3%. In addition, with the increase in mass flow rate of the steam and heat transfer area and the decrease in total heat transfer rate, the minimum total pumping power required by the condenser decreases. In the multi-objective optimization, the Pareto optimal set of the entropy generation rate and total pumping power is gained. The optimal results gained by three decision methods in the Pareto optimal set and single objective optimizations are compared by the deviation index. The optimal construct gained by the TOPSIS decision method corresponding to the smallest deviation index is recommended in the optimal design of the condenser. These research ideas can also be used to design other heat transfer devices.
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Kaliszewski, Ignacy, Janusz Miroforidis, and Jarosław Stańczak. "THE AIRPORT GATE ASSIGNMENT PROBLEM – MULTI-OBJECTIVE OPTIMIZATION VERSUS EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION." Computer Science 18, no. 1 (2017): 41. http://dx.doi.org/10.7494/csci.2017.18.1.41.

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Dissertations / Theses on the topic "Multi-Objective Optimization"

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Amouzgar, Kaveh. "Metamodel based multi-objective optimization." Licentiate thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH. Forskningsmiljö Produktutveckling - Simulering och optimering, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-28432.

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As a result of the increase in accessibility of computational resources and the increase in the power of the computers during the last two decades, designers are able to create computer models to simulate the behavior of a complex products. To address global competitiveness, companies are forced to optimize their designs and products. Optimizing the design needs several runs of computationally expensive simulation models. Therefore, using metamodels as an efficient and sufficiently accurate approximate of the simulation model is necessary. Radial basis functions (RBF) is one of the several metamodeling methods that can be found in the literature. The established approach is to add a bias to RBF in order to obtain a robust performance. The a posteriori bias is considered to be unknown at the beginning and it is defined by imposing extra orthogonality constraints. In this thesis, a new approach in constructing RBF with the bias to be set a priori by using the normal equation is proposed. The performance of the suggested approach is compared to the classic RBF with a posteriori bias. Another comprehensive comparison study by including several modeling criteria, such as problem dimension, sampling technique and size of samples is conducted. The studies demonstrate that the suggested approach with a priori bias is in general as good as the performance of RBF with a posteriori bias. Using the a priori RBF, it is clear that the global response is modeled with the bias and that the details are captured with radial basis functions. Multi-objective optimization and the approaches used in solving such problems are briefly described in this thesis. One of the methods that proved to be efficient in solving multi-objective optimization problems (MOOP) is the strength Pareto evolutionary algorithm (SPEA2). Multi-objective optimization of a disc brake system of a heavy truck by using SPEA2 and RBF with a priori bias is performed. As a result, the possibility to reduce the weight of the system without extensive compromise in other objectives is found. Multi-objective optimization of material model parameters of an adhesive layer with the aim of improving the results of a previous study is implemented. The result of the original study is improved and a clear insight into the nature of the problem is revealed.
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Roland, Julien. "Inverse multi-objective combinatorial optimization." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209383.

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The initial question addressed in this thesis is how to take into account the multi-objective aspect of decision problems in inverse optimization. The most straightforward extension consists of finding a minimal adjustment of the objective functions coefficients such that a given feasible solution becomes efficient. However, there is not only a single question raised by inverse multi-objective optimization, because there is usually not a single efficient solution. The way we define inverse multi-objective

optimization takes into account this important aspect. This gives rise to many questions which are identified by a precise notation that highlights a large collection of inverse problems that could be investigated. In this thesis, a selection of inverse problems are presented and solved. This selection is motivated by their possible applications and the interesting theoretical questions they can rise in practice.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished

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Rollón, Emma. "Multi-objective optimization in graphical models." Doctoral thesis, Universitat Politècnica de Catalunya, 2008. http://hdl.handle.net/10803/108180.

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Many real-life optimization problems are combinatorial, i.e. they concern a choice of the best solution from a finite but exponentially large set of alternatives. Besides, the solution quality of many of these problems can often be evaluated from several points of view (a.k.a. criteria). In that case, each criterion may be described by a different objective function. Some important and well-known multicriteria scenarios are: · In investment optimization one wants to minimize risk and maximize benefits. · In travel scheduling one wants to minimize time and cost. · In circuit design one wants to minimize circuit area, energy consumption and maximize speed. · In knapsack problems one wants to minimize load weight and/or volume and maximize its economical value. The previous examples illustrate that, in many cases, these multiple criteria are incommensurate (i.e., it is difficult or impossible to combine them into a single criterion) and conflicting (i.e., solutions that are good with respect one criterion are likely to be bad with respect to another). Taking into account simultaneously the different criteria is not trivial and several notions of optimality have been proposed. Independently of the chosen notion of optimality, computing optimal solutions represents an important current research challenge. Graphical models are a knowledge representation tool widely used in the Artificial Intelligence field. They seem to be specially suitable for combinatorial problems. Roughly, graphical models are graphs in which nodes represent variables and the (lack of) arcs represent conditional independence assumptions. In addition to the graph structure, it is necessary to specify its micro-structure which tells how particular combinations of instantiations of interdependent variables interact. The graphical model framework provides a unifying way to model a broad spectrum of systems and a collection of general algorithms to efficiently solve them. In this Thesis we integrate multi-objective optimization problems into the graphical model paradigm and study how algorithmic techniques developed in the graphical model context can be extended to multi-objective optimization problems. As we show, multiobjective optimization problems can be formalized as a particular case of graphical models using the semiring-based framework. It is, to the best of our knowledge, the first time that graphical models in general, and semiring-based problems in particular are used to model an optimization problem in which the objective function is partially ordered. Moreover, we show that most of the solving techniques for mono-objective optimization problems can be naturally extended to the multi-objective context. The result of our work is the mathematical formalization of multi-objective optimization problems and the development of a set of multiobjective solving algorithms that have been proved to be efficient in a number of benchmarks.
Muchos problemas reales de optimización son combinatorios, es decir, requieren de la elección de la mejor solución (o solución óptima) dentro de un conjunto finito pero exponencialmente grande de alternativas. Además, la mejor solución de muchos de estos problemas es, a menudo, evaluada desde varios puntos de vista (también llamados criterios). Es este caso, cada criterio puede ser descrito por una función objetivo. Algunos escenarios multi-objetivo importantes y bien conocidos son los siguientes: · En optimización de inversiones se pretende minimizar los riesgos y maximizar los beneficios. · En la programación de viajes se quiere reducir el tiempo de viaje y los costes. · En el diseño de circuitos se quiere reducir al mínimo la zona ocupada del circuito, el consumo de energía y maximizar la velocidad. · En los problemas de la mochila se quiere minimizar el peso de la carga y/o el volumen y maximizar su valor económico. Los ejemplos anteriores muestran que, en muchos casos, estos criterios son inconmensurables (es decir, es difícil o imposible combinar todos ellos en un único criterio) y están en conflicto (es decir, soluciones que son buenas con respecto a un criterio es probable que sean malas con respecto a otra). Tener en cuenta de forma simultánea todos estos criterios no es trivial y para ello se han propuesto diferentes nociones de optimalidad. Independientemente del concepto de optimalidad elegido, el cómputo de soluciones óptimas representa un importante desafío para la investigación actual. Los modelos gráficos son una herramienta para la represetanción del conocimiento ampliamente utilizados en el campo de la Inteligencia Artificial que parecen especialmente indicados en problemas combinatorios. A grandes rasgos, los modelos gráficos son grafos en los que los nodos representan variables y la (falta de) arcos representa la interdepencia entre variables. Además de la estructura gráfica, es necesario especificar su (micro-estructura) que indica cómo interactúan instanciaciones concretas de variables interdependientes. Los modelos gráficos proporcionan un marco capaz de unificar el modelado de un espectro amplio de sistemas y un conjunto de algoritmos generales capaces de resolverlos eficientemente. En esta tesis integramos problemas de optimización multi-objetivo en el contexto de los modelos gráficos y estudiamos cómo diversas técnicas algorítmicas desarrolladas dentro del marco de los modelos gráficos se pueden extender a problemas de optimización multi-objetivo. Como mostramos, este tipo de problemas se pueden formalizar como un caso particular de modelo gráfico usando el paradigma basado en semi-anillos (SCSP). Desde nuestro conocimiento, ésta es la primera vez que los modelos gráficos en general, y el paradigma basado en semi-anillos en particular, se usan para modelar un problema de optimización cuya función objetivo está parcialmente ordenada. Además, mostramos que la mayoría de técnicas para resolver problemas monoobjetivo se pueden extender de forma natural al contexto multi-objetivo. El resultado de nuestro trabajo es la formalización matemática de problemas de optimización multi-objetivo y el desarrollo de un conjunto de algoritmos capaces de resolver este tipo de problemas. Además, demostramos que estos algoritmos son eficientes en un conjunto determinado de benchmarks.
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Amouzgar, Kaveh. "Multi-objective optimization using Genetic Algorithms." Thesis, Högskolan i Jönköping, Tekniska Högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-19851.

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In this thesis, the basic principles and concepts of single and multi-objective Genetic Algorithms (GA) are reviewed. Two algorithms, one for single objective and the other for multi-objective problems, which are believed to be more efficient are described in details. The algorithms are coded with MATLAB and applied on several test functions. The results are compared with the existing solutions in literatures and shows promising results. Obtained pareto-fronts are exactly similar to the true pareto-fronts with a good spread of solution throughout the optimal region. Constraint handling techniques are studied and applied in the two algorithms. Constrained benchmarks are optimized and the outcomes show the ability of algorithm in maintaining solutions in the entire pareto-optimal region. In the end, a hybrid method based on the combination of the two algorithms is introduced and the performance is discussed. It is concluded that no significant strength is observed within the approach and more research is required on this topic. For further investigation on the performance of the proposed techniques, implementation on real-world engineering applications are recommended.
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Nezhadali, Vaheed. "Multi-objective optimization of Industrial robots." Thesis, Linköpings universitet, Maskinkonstruktion, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-113283.

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Industrial robots are the most widely manufactured and utilized type of robots in industries. Improving the design process of industrial robots would lead to further developments in robotics industries. Consequently, other dependant industries would be benefited. Therefore, there is an effort to make the design process more and more efficient and reliable. The design of industrial robots requires studies in various fields. Engineering softwares are the tools which facilitate and accelerate the robot design processes such as dynamic simulation, structural analysis, optimization, control and so forth. Therefore, designing a framework to automate the robot design process such that different tools interact automatically would be beneficial. In this thesis, the goal is to investigate the feasibility of integrating tools from different domains such as geometry modeling, dynamic simulation, finite element analysis and optimization in order to obtain an industrial robot design and optimization framework. Meanwhile, Meta modeling is used to replace the time consuming design steps. In the optimization step, various optimization algorithms are compared based on their performance and the best suited algorithm is selected. As a result, it is shown that the objectives are achievable in a sense that finite element analysis can be efficiently integrated with the other tools and the results can be optimized during the design process. A holistic framework which can be used for design of robots with several degrees of freedom is introduced at the end.
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Msaaf, Khaoula. "Multi-Objective optimization of arch bridges." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111519.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 83-84).
Trussed arch bridges are commonly used to attain big spans. They are efficient structures that offer a wide range of geometries, materials, and topologies. This thesis studies the influence of the geometry and topology of arch bridges on both their structural performance relayed by the maximum deflection and their structural weight. Various materials are also considered to calculate the embodied carbon emission and investigate the environmental impact of arch bridges. Gustave Eiffel's Garabit Viaduct is used as a design precedent for this study. 2-D and 3-D parametric models of the arch bridge are realized using Grasshopper [8]. Changing the geometric parameters in addition to the topology enables the investigation of the bridge's performance. The cross sections are automatically optimized in each case. Furthermore, a multi-objective optimization process was run on the bridge to examine the tradeoffs between the deflection and the self-weight. The weight-oriented optimization allows saving more than 60% of the weight compared to the original structure. Analyzing the different resulting designs proves that increasing the depth at the arch's crown and the depth at the base of the arch leads to better deflection results. It also demonstrates that using a denser truss structure leads to a lighter structure.
by Khaoula Msaaf.
M. Eng.
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Gaudrie, David. "High-Dimensional Bayesian Multi-Objective Optimization." Thesis, Lyon, 2019. https://tel.archives-ouvertes.fr/tel-02356349.

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Dans cette thèse, nous nous intéressons à l'optimisation simultanée de fonctions coûteuses à évaluer et dépendant d'un grand nombre de paramètres. Cette situation est rencontrée dans de nombreux domaines tels que la conception de systèmes en ingénierie au moyen de simulations numériques. L'optimisation bayésienne, reposant sur des méta-modèles (processus gaussiens) est particulièrement adaptée à ce contexte.La première partie de cette thèse est consacrée au développement de nouvelles méthodes d'optimisation multi-objectif assistées par méta-modèles. Afin d'améliorer le temps d'atteinte de solutions Pareto optimales, un critère d'acquisition est adapté pour diriger l'algorithme vers une région de l'espace des objectifs plébiscitée par l'utilisateur ou, en son absence, le centre du front de Pareto introduit dans nos travaux. Outre le ciblage, la méthode prend en compte le budget d'optimisation, afin de restituer un éventail de solutions optimales aussi large que possible, dans la limite des ressources disponibles.Dans un second temps, inspirée par l'optimisation de forme, une approche d'optimisation avec réduction de dimension est proposée pour contrer le fléau de la dimension. Elle repose sur la construction, par analyse en composantes principales de solutions candidates, de variables auxiliaires adaptées au problème, hiérarchisées et plus à même de décrire les candidats globalement. Peu d'entre elles suffisent à approcher les solutions, et les plus influentes sont sélectionnées et priorisées au sein d'un processus gaussien additif. Cette structuration des variables est ensuite exploitée dans l'algorithme d'optimisation bayésienne qui opère en dimension réduite
This thesis focuses on the simultaneous optimization of expensive-to-evaluate functions that depend on a high number of parameters. This situation is frequently encountered in fields such as design engineering through numerical simulation. Bayesian optimization relying on surrogate models (Gaussian Processes) is particularly adapted to this context.The first part of this thesis is devoted to the development of new surrogate-assisted multi-objective optimization methods. To improve the attainment of Pareto optimal solutions, an infill criterion is tailored to direct the search towards a user-desired region of the objective space or, in its absence, towards the Pareto front center introduced in our work. Besides targeting a well-chosen part of the Pareto front, the method also considers the optimization budget in order to provide an as wide as possible range of optimal solutions in the limit of the available resources.Next, inspired by shape optimization problems, an optimization method with dimension reduction is proposed to tackle the curse of dimensionality. The approach hinges on the construction of hierarchized problem-related auxiliary variables that can describe all candidates globally, through a principal component analysis of potential solutions. Few of these variables suffice to approach any solution, and the most influential ones are selected and prioritized inside an additive Gaussian Process. This variable categorization is then further exploited in the Bayesian optimization algorithm which operates in reduced dimension
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Ledéus, Johan. "Multi-Objective Optimization on Flexible Spaces." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280797.

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Virtual Reality is a growing sector with applications in cybertherapy, video games, and entertainment. There exist several locomotion techniques that al low movement in the virtual environment. A joystick, treadmill, or gestures that mimic walking are some techniques used. However, none of these approaches are as intuitive and immersive as real walking in Virtual Reality. How far a user can walk in Virtual Reality is limited by ones’ surroundings. The virtual environments need to fit inside the Tracked Space in which the trackers of the VR-headset reach. Impossible Spaces introduced the concept of overlap ping layouts. A side effect of using overlapping layouts is that users perceive the Tracked Space to be larger. Flexible Spaces is a procedural approach for redirection as it generates walkable corridors inside the virtual environment. The corridors are randomly generated and connect rooms inside the virtual environment. The features of the corridors impact the users’ sensation of space inside the Tracked Space. This research investigates the properties of Flexi ble Spaces and examines if it is advantageous to extend it with multi-objective optimization. It does so by giving designers the ability to have preferences over the corridors concerning length and amount of corners and optimize to decrease the overlap perception. It was evaluated with rectangular and com plex layouts. Initial findings suggest that Flexible Spaces are appropriate to extend with multi-objective optimization. The generated corridors for the testing environment decreased overlapping close to doors and lied within the given preferences. In an unoptimized state, it was able to produce more than 25 optimal, or near-optimal corridors in a second. Extending Flexible Spaces with multi-objective optimization shows potential as it restricts the randomness of the generated corridors. However, it is important to understand what the algorithm is optimizing towards, and the trade-offs of the objectives concerning spatial perception on overlapping layouts.
Virtual Reality är en växande sektor med tillämpningar inom terapi, spel och underhållning. Det finns flertalet förflyttningstekniker som möjliggör förflyttning i virtuella miljöer. Handkontroll, gångband och gester som imiterar gång, är beprövade tekniker. Men ingen är lika intuitiv och uppslukande som naturlig gång. Dock så begränsas den naturliga gången av den fysiska omgivning en, vilket även gäller för virtuella miljöer. Impossible Spaces introducerade konceptet med överlappande planlösningar i virtuella miljöer. En bieffekt av överlappande planlösningar är att den begränsade ytan kan upplevas större. Flexible Spaces är en procedurell förflyttningsteknik. Användaren förflyttas mellan olika rum i den virtuella miljön genom att gå i virtuella korridorer. Planlösningen och korridorens utformning har en inverkan i användarens upp levda rymd. Den här uppsatsen undersöker egenskaperna i Flexible Spaces och utvidgar den med flermålsoptimering. Optimeringsalgoritmen är utformad att ge designers förmågan att ha preferenser över korridorens längd, antal hörn, samtidigt som den optimerar mot att minska den upplevda överlappningen. Algoritmen testades mot en rektangulär och en komplex planlösning. Inledande resultat föreslår att Flexible Spaces är lämplig att utvidga med flermålsoptimering. De genererade korridorerna efterliknade den föreslagna designen och minskade överlappningen nära rummens dörrar. I ett ooptimerat tillstånd, så genererade den mer än 25 korridorer under en sekund. Notera att det är av hög relevans att förstå de underliggande principerna som algoritmen optimerar mot, samt att vara medveten om avvägningen mellan de olika målen relaterat till upplevelsen av överlappande planlösningar.
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Yuan, Xiaoyan. "Multi-Functional Reconfigurable Antenna Development by Multi-Objective Optimization." DigitalCommons@USU, 2012. https://digitalcommons.usu.edu/etd/1326.

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This dissertation work builds upon the theoretical and experimental studies of radio frequency micro- and nano-electromechanical systems (RF M/NEMS) integrated multifunctional reconfigurable antennas (MRAs). This work focuses on three MRAs with an emphasis on a wireless local area network (WLAN), 5-6 GHz, beam tilt, and polarization reconfigurable parasitic layer-based MRA with inset micro-strip feed. The other two antennas are an X band (8-12 GHz) beam steering MRA with aperture-coupled micro-strip fed and wireless personal area network (WPAN), 60 GHz, inset micro-strip fed MRA for dual frequency and dual polarization operations. For the WLAN (5-6 GHz) MRA, a detailed description of the design methodology, which is based on the joint utilization of electromagnetic (EM) full-wave analysis and multi-objective genetic algorithm, and fundamental theoretical background of parasitic layer-based antennas are given. Various prototypes of this MRA have been fabricated and measured. The measured and simulated results for both impedance and radiation characteristics are given. The work on the MRAs operating in the X band and 60 GHz region focuses on the theoretical aspects of the designs. Different than the WLAN MRA, which uses inset fed structure, the aperture-coupled feed mechanism has been investigated with the goal of improving the bandwidth and beam-tilt capabilities of these MRAs. The simulated results are provided and the working mechanisms are described. The results show that the aperture-coupled feed mechanism is advantageous both in terms of enhanced bandwidth and beam-steering capabilities. Finally, this dissertation work concludes with plans for future work, which will build upon the findings and the results presented herein.
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Soylu, Banu. "An Evolutionary Algorithm For Multiple Criteria Problems." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608134/index.pdf.

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In this thesis, we develop an evolutionary algorithm for approximating the Pareto frontier of multi-objective continuous and combinatorial optimization problems. The algorithm tries to evolve the population of solutions towards the Pareto frontier and distribute it over the frontier in order to maintain a well-spread representation. The fitness score of each solution is computed with a Tchebycheff distance function and non-dominating sorting approach. Each solution chooses its own favorable weights according to the Tchebycheff distance function. Some seed solutions at initial population and a crowding measure also help to achieve satisfactory results. In order to test the performance of our evolutionary algorithm, we use some continuous and combinatorial problems. The continuous test problems taken from the literature have special difficulties that an evolutionary algorithm has to deal with. Experimental results of our algorithm on these problems are provided. One of the combinatorial problems we address is the multi-objective knapsack problem. We carry out experiments on test data for this problem given in the literature. We work on two bi-criteria p-hub location problems and propose an evolutionary algorithm to approximate the Pareto frontiers of these problems. We test the performance of our algorithm on Turkish Postal System (PTT) data set (TPDS), AP (Australian Post) and CAB (US Civil Aeronautics Board) data sets. The main contribution of this thesis is in the field of developing a multi-objective evolutionary algorithm and applying it to a number of multi-objective continuous and combinatorial optimization problems.
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Books on the topic "Multi-Objective Optimization"

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Mandal, Jyotsna K., Somnath Mukhopadhyay, and Paramartha Dutta, eds. Multi-Objective Optimization. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1.

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Lobato, Fran Sérgio, and Valder Steffen. Multi-Objective Optimization Problems. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58565-9.

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Dey, Nilanjan, ed. Applied Multi-objective Optimization. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0353-1.

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Pardalos, Panos M., Antanas Žilinskas, and Julius Žilinskas. Non-Convex Multi-Objective Optimization. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61007-8.

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Serafini, Paolo, ed. Mathematics of Multi Objective Optimization. Vienna: Springer Vienna, 1985. http://dx.doi.org/10.1007/978-3-7091-2822-0.

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P, Serafini, ed. Mathematics of multi objective optimization. Wien: Springer, 1985.

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P, Serafini, and International Centre for Mechanical Sciences., eds. Mathematics of multi objective optimization. Wien: Springer-Verlag, 1985.

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Rangaiah, Gade Pandu, and Adrián Bonilla-Petriciolet, eds. Multi-Objective Optimization in Chemical Engineering. Oxford, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118341704.

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Mankowski, Michal, and Mikhail Moshkov. Dynamic Programming Multi-Objective Combinatorial Optimization. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63920-4.

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Mirjalili, Seyedali, and Jin Song Dong. Multi-Objective Optimization using Artificial Intelligence Techniques. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-24835-2.

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Book chapters on the topic "Multi-Objective Optimization"

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Seada, Haitham, and Kalyanmoy Deb. "Non-dominated Sorting Based Multi/Many-Objective Optimization: Two Decades of Research and Application." In Multi-Objective Optimization, 1–24. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_1.

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Bhunia, Asoke Kumar, Amiya Biswas, and Ali Akbar Shaikh. "Extended Nondominated Sorting Genetic Algorithm (ENSGA-II) for Multi-Objective Optimization Problem in Interval Environment." In Multi-Objective Optimization, 215–41. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_10.

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Das, Asit Kumar, and Sunanda Das. "A Comparative Study on Different Versions of Multi-Objective Genetic Algorithm for Simultaneous Gene Selection and Sample Categorization." In Multi-Objective Optimization, 243–67. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_11.

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Datta, Niladri Sekhar, Himadri Sekhar Dutta, Koushik Majumder, Sumana Chatterjee, and Najir Abdul Wasim. "A Survey on the Application of Multi-Objective Optimization Methods in Image Segmentation." In Multi-Objective Optimization, 269–78. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_12.

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Das, Asit Kumar, and Soumen Kumar Pati. "Bi-objective Genetic Algorithm with Rough Set Theory for Important Gene Selection in Disease Diagnosis." In Multi-Objective Optimization, 279–98. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_13.

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Das, Amit Kumar, Debasish Das, and Dilip Kumar Pratihar. "Multi-Objective Optimization and Cluster-Wise Regression Analysis to Establish Input–Output Relationships of a Process." In Multi-Objective Optimization, 299–318. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_14.

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Majumder, Saibal, Samarjit Kar, and Tandra Pal. "Mean-Entropy Model of Uncertain Portfolio Selection Problem." In Multi-Objective Optimization, 25–54. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_2.

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Mukhopadhyay, Anirban. "Incorporating Gene Ontology Information in Gene Expression Data Clustering Using Multiobjective Evolutionary Optimization: Application in Yeast Cell Cycle Data." In Multi-Objective Optimization, 55–78. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_3.

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Pal, Bijay Baran. "Interval-Valued Goal Programming Method to Solve Patrol Manpower Planning Problem for Road Traffic Management Using Genetic Algorithm." In Multi-Objective Optimization, 79–113. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_4.

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Gunasekara, R. Chulaka, Chilukuri K. Mohan, and Kishan Mehrotra. "Multi-objective Optimization to Improve Robustness in Networks." In Multi-Objective Optimization, 115–39. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_5.

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Conference papers on the topic "Multi-Objective Optimization"

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Kumawat, Ishwar Ram, Satyasai Jagannath Nanda, and Ravi Kumar Maddila. "Multi-objective whale optimization." In TENCON 2017 - 2017 IEEE Region 10 Conference. IEEE, 2017. http://dx.doi.org/10.1109/tencon.2017.8228329.

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Miletic, S., and D. Karavidovic. "Multi-objective maintenance optimization." In 22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.1123.

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Deb, Kalyanmoy. "Evolutionary multi-objective optimization." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377929.3389864.

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Helbig, Mardé. "Dynamic multi-objective optimization." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3461413.

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Pettersson, William, and Melih Ozlen. "Multi-objective mixed integer programming: An objective space algorithm." In PROCEEDINGS LEGO – 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP. Author(s), 2019. http://dx.doi.org/10.1063/1.5090006.

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Gantovnik, Vladimir, Santosh Tiwari, Georges Fadel, and Yi Miao. "Multi-Objective Vehicle Layout Optimization." In 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-6978.

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Liang, Chen, and Sankaran Mahadevan. "Multi-Objective Optimization Under Uncertainty." In 16th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-3438.

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Ishibuchi, Hisao, Hiroyuki Masuda, and Yusuke Nojima. "Meta-level multi-objective formulations of set optimization for multi-objective optimization problems." In GECCO '14: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2598394.2598484.

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Zeng, Yi, Hongcheng Zhao, Chuanping Liu, Silin Chen, Xinghong Hao, Xiaojia Sun, and Junjie Zhang. "Multi objective optimization of microgrid based on Improved Multi-objective Particle Swarm Optimization." In 2022 International Seminar on Computer Science and Engineering Technology (SCSET). IEEE, 2022. http://dx.doi.org/10.1109/scset55041.2022.00027.

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Zhang, Song, Hongfeng Wang, Di Yang, and Min Huang. "Hybrid multi-objective genetic algorithm for multi-objective optimization problems." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162243.

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Reports on the topic "Multi-Objective Optimization"

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Raji, David. Applied Multi-Objective Modelling & Optimization. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1888185.

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Waddell, Lucas, John Gauthier, Matthew Hoffman, Denise Padilla, Stephen Henry, Alexander Dessanti, and Adam Pierson. Estimating the Adequacy of a Multi-Objective Optimization . Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1833178.

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Kuprowicz, Nicholas J. The Integrated Multi-Objective Multi-Disciplinary Jet Engine Design Optimization Program. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada372032.

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Wenren, Yonghu, Joon Lim, Luke Allen, Robert Haehnel, and Ian Dettwiler. Helicopter rotor blade planform optimization using parametric design and multi-objective genetic algorithm. Engineer Research and Development Center (U.S.), December 2022. http://dx.doi.org/10.21079/11681/46261.

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Abstract:
In this paper, an automated framework is presented to perform helicopter rotor blade planform optimization. This framework contains three elements, Dakota, ParBlade, and RCAS. These elements are integrated into an environment control tool, Galaxy Simulation Builder, which is used to carry out the optimization. The main objective of this work is to conduct rotor performance design optimizations for forward flight and hover. The blade design variables manipulated by ParBlade are twist, sweep, and anhedral. The multi-objective genetic algorithm method is used in this study to search for the optimum blade design; the optimization objective is to minimize the rotor power required. Following design parameter substitution, ParBlade generates the modified blade shape and updates the rotor blade properties in the RCAS script before running RCAS. After the RCAS simulations are complete, the desired performance metrics (objectives and constraints) are extracted and returned to the Dakota optimizer. Demonstrative optimization case studies were conducted using a UH-60A main rotor as the base case. Rotor power in hover and forward flight, at advance ratio 𝜇𝜇 = 0.3, are used as objective functions. The results of this study show improvement in rotor power of 6.13% and 8.52% in hover and an advance ratio of 0.3, respectively. This configuration also yields greater reductions in rotor power for high advance ratios, e.g., 12.42% reduction at 𝜇𝜇 = 0.4.
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Sauser, Brian J., and Jose E. Ramirez-Marquez. Multi-Objective Optimization of System Capability Satisficing in Defense Acquisition. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada589350.

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Richie, David A., James A. Ross, Song J. Park, and Dale R. Shires. A Monte Carlo Method for Multi-Objective Correlated Geometric Optimization. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada603830.

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Nenoff, Tina M., Sarah E. Moore, Sera Mirchandani, Vasiliki Karanikola, Robert G. Arnold, and Eduardo Saez. Multi-objective Optimization of Solar-driven Hollow-fiber Membrane Distillation Systems. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1395756.

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Huang, Ke, and Xianfeng Yang. Eco-Driving Systems for Connected Automated Vehicles: Multi-Objective Trajectory Optimization. Mineta Transportation Institute, August 2020. http://dx.doi.org/10.31979/mti.2020.1924.

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Fernandez, Ruben, Hernando Lugo, and Georfe Dulikravich. Aerodynamic Shape Multi-Objective Optimization for SAE Aero Design Competition Aircraft. Florida International University, October 2021. http://dx.doi.org/10.25148/mmeurs.009778.

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The SAE Regular Class Aero Design Competition requires students to design a radio-controlled aircraft with limits to the aircraft power consumption, take-off distance, and wingspan, while maximizing the amount of payload it can carry. As a result, the aircraft should be designed subject to these simultaneous and contradicting objectives: 1) minimize the aerodynamic drag force, 2) minimize the aerodynamic pitching moment, and 3) maximize the aerodynamic lift force. In this study, we optimized the geometric design variables of a biplane configuration using 3D aerodynamic analysis using the ANSYS Fluent. Coefficients of lift, drag, and pitching moment were determined from the completed 3D CFD simulations. Extracted coefficients were used in modeFRONTIER multi-objective optimization software to find a set of non-dominated (Pareto-optimal or best trade-off) optimized 3D aircraft shapes from which the winner was selected based to the desired plane performance.
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Choi, Yong-Joon, Junyung Kim, Mohammad M Mostafa Abdo, and Congjian Wang. Development of Genetic Algorithm Based Multi-Objective Plant Reload Optimization Platform. Office of Scientific and Technical Information (OSTI), March 2023. http://dx.doi.org/10.2172/2004907.

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