Dissertations / Theses: '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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Lokman, Banu. "Approaches For Multi-objective Combinatorial Optimization Problems." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608443/index.pdf.

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In this thesis, we develop two exact algorithms and a heuristic procedure for Multiobjective Combinatorial Optimization Problems (MOCO). Our exact algorithms guarantee to generate all nondominated solutions of any MOCO problem. We test the performance of the algorithms on randomly generated problems including the Multiobjective Knapsack Problem, Multi-objective Shortest Path Problem and Multi-objective Spanning Tree Problem. Although we showed the algorithms work much better than the previous ones, we also proposed a fast heuristic method to approximate efficient frontier since it will also be applicable for real-sized problems. Our heuristic approach is based on fitting a surface to approximate the efficient frontier. We experiment our heuristic on randomly generated problems to test how well the heuristic procedure approximates the efficient frontier. Our results showed the heuristic method works well.

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Bozkurt, Bilge. "Performance Measurement In Multi Objective Combinatorial Optimization." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608843/index.pdf.

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ABSTRACT PERFORMANCE MEASUREMENT IN MULTI OBJECTIVE COMBINATORIAL OPTIMIZATION Bozkurt, Bilge M.Sc., Department of Industrial Engineering Supervisor: Prof. Dr. Murat Kö
ksalan September 2007, 96 pages In this study we address the problem of measuring the quality of different sets of nondominated solutions obtained by different approaches in multi objective combinatorial optimization (MOCO). We propose a new measure that quantitatively compares the sets of nondominated solutions, without needing an efficient frontier. We develop the measure for bi-criteria and more than two criteria cases separately. Rather than considering only the supported solutions in the evaluation, the measure captures both supported and unsupported solutions through utilizing weighted Tchebycheff function characteristics. We also adapt this method for determining the neighborhood relations on the weight space for both bi-criteria and more than two criteria cases. We check the consistency of the neighborhood assumption on the objective space with the neighborhood relations on the weight space by this measure and obtain highly good results. Keywords: Multi objective combinatorial optimization, performance measurement

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Ozsayin, Burcu. "Multi-objective Combinatorial Optimization Using Evolutionary Algorithms." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12610866/index.pdf.

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Due to the complexity of multi-objective combinatorial optimization problems (MOCO), metaheuristics like multi-objective evolutionary algorithms (MOEA) are gaining importance to obtain a well-converged and well-dispersed Pareto-optimal frontier approximation. In this study, of the well-known MOCO problems, single-dimensional multi-objective knapsack problem and multi-objective assignment problem are taken into consideration. We develop a steady-state and elitist MOEA in order to approximate the Pareto-optimal frontiers. We utilize a territory concept in order to provide diversity over the Pareto-optimal frontiers of various problem instances. The motivation behind the territory definition is to attach the algorithm the advantage of fast execution by eliminating the need for an explicit diversity preserving operator. We also develop an interactive preference incorporation mechanism to converge to the regions that are of special interest for the decision maker by interacting with him/her during the optimization process.

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Riauke, Jelena. "SPEA2-based safety system multi-objective optimization." Thesis, Loughborough University, 2009. https://dspace.lboro.ac.uk/2134/5514.

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Safety systems are designed to prevent the occurrence of certain conditions and their future development into a hazardous situation. The consequence of the failure of a safety system of a potentially hazardous industrial system or process varies from minor inconvenience and cost to personal injury, significant economic loss and death. To minimise the likelihood of a hazardous situation, safety systems must be designed to maximise their availability. Therefore, the purpose of this thesis is to propose an effective safety system design optimization scheme. A multi-objective genetic algorithm has been adopted, where the criteria catered for includes unavailability, cost, spurious trip and maintenance down time. Analyses of individual system designs are carried out using the latest advantages of the fault tree analysis technique and the binary decision diagram approach (BDD). The improved strength Pareto evolutionary approach (SPEA2) is chosen to perform the system optimization resulting in the final design specifications. The practicality of the developed approach is demonstrated initially through application to a High Integrity Protection System (HIPS) and subsequently to test scalability using the more complex Firewater Deluge System (FDS). Computer code has been developed to carry out the analysis. The results for both systems are compared to those using a single objective optimization approach (GASSOP) and exhaustive search. The overall conclusions show a number of benefits of the SPEA2 based technique application to the safety system design optimization. It is common for safety systems to feature dependency relationships between its components. To enable the use of the fault tree analysis technique and the BDD approach for such systems, the Markov method is incorporated into the optimization process. The main types of dependency which can exist between the safety system component failures are identified. The Markov model generation algorithms are suggested for each type of dependency. The modified optimization tool is tested on the HIPS and FDS. Results comparison shows the benefit of using the modified technique for safety system optimization. Finally the effectiveness and application to general safety systems is discussed.

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Praharaj, Blake. "AIMOS| Automated Inferential Multi-Objective Optimization System." Thesis, Southern Connecticut State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10249184.

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Many important modern engineering problems involve satisfying multiple objectives. Simultaneous optimization of these objectives can be difficult as they compete for the same set of any given resources. One way to solve multiple-objective optimization is with the use of genetic algorithms (GA’s).

One can break down the structure of these multi-objective genetic algorithms (MOGA’s) into two different approaches. One approach is based on incorporating multiple objectives into a single fitness function which will evaluate how well a given solution solves the issue. The other approach uses multiple fitness functions, each representing a different objective, which when combined create a solution set of possible solutions to the problem. This project focuses on combining these approaches in order to make a hybrid model, which can benefit from combining the results of the previous two methods; incorporating a level of automation that allows for inference of a final solution based on different prioritization of each objective. This solution would not have been previously attainable by either standalone method.

This project is named the Automated Inferential Multi-Objective Optimization System (AIMOS), and it can be applied to a multitude of different problem types. In order to show its capabilities, AIMOS has been applied to a theoretical optimization problem used to measure the effectiveness of GA’s.

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Atiah, Frederick Ditliac. "Dynamic multi-objective optimization for financial markets." Diss., University of Pretoria, 2019. http://hdl.handle.net/2263/79571.

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The foreign exchange (Forex) market has over 5 trillion USD turnover per day. In addition, it is one of the most volatile and dynamic markets in the world. Market conditions continue to change every second. Algorithmic trading in Financial markets have received a lot of attention in recent years. However, only few literature have explored the applicability and performance of various dynamic multi-objective algorithms (DMOAs) in the Forex market. This dissertation proposes a dynamic multi-swarm multi-objective particle swarm optimization (DMS-MOPSO) to solve dynamic MOPs (DMOPs). In order to explore the performance and applicability of DMS-MOPSO, the algorithm is adapted for the Forex market. This dissertation also explores the performance of di erent variants of dynamic particle swarm optimization (PSO), namely the charge PSO (cPSO) and quantum PSO (qPSO), for the Forex market. However, since the Forex market is not only dynamic but have di erent con icting objectives, a single-objective optimization algorithm (SOA) might not yield pro t over time. For this reason, the Forex market was de ned as a multi-objective optimization problem (MOP). Moreover, maximizing pro t in a nancial time series, like Forex, with computational intelligence (CI) techniques is very challenging. It is even more challenging to make a decision from the solutions of a MOP, like automated Forex trading. This dissertation also explores the e ects of ve decision models (DMs) on DMS-MOPSO and other three state-of-the-art DMOAs, namely the dynamic vector-evaluated particle swarm optimization (DVEPSO) algorithm, the multi-objective particle swarm optimization algorithm with crowded distance (MOPSOCD) and dynamic non-dominated sorting genetic algorithm II (DNSGA-II). The e ects of constraints handling and the, knowledge sharing approach amongst sub-swarms were explored for DMS-MOPSO. DMS-MOPSO is compared against other state-of-the-art multi-objective algorithms (MOAs) and dynamic SOAs. A sliding window mechanism is employed over di erent types of currency pairs. The focus of this dissertation is to optimized technical indicators to maximized the pro t and minimize the transaction cost. The obtained results showed that both dynamic single-objective optimization (SOO) algorithms and dynamic multi-objective optimization (MOO) algorithms performed better than static algorithms on dynamic poroblems. Moreover, the results also showed that a multi-swarm approach for MOO can solve dynamic MOPs.
Dissertation (MEng)--University of Pretoria, 2019.
Computer Science
MSc
Unrestricted

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Zamani, Moslem. "Scalarization and stability in multi-objective optimization." Thesis, Avignon, 2016. http://www.theses.fr/2016AVIG0414/document.

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Cette thèse porte sur trois questions qui se posent en optimisation multi-objectif. Dansun premier temps, nous étudions l’existence de solutions efficaces via des techniquesde scalarisation. On étend le théorème de Benson du cas convexe à un cas général.De plus, nous examinons d’autres techniques de scalarisation. Dans un second temps,nous abordons la question de robustesse. Nous examinons les concepts proposés dansla littérature sur le sujet. On étend au cas d’optimisation multi-objectif non-linéairela définition de Georgiev et ses collaborateurs. Quelques conditions nécessaires etsuffisantes pour obtenir une solution robuste moyennant des hypothèses appropriéessont données. Les relations entre cette notion de robustesse et certaines définitionsmentionnées sont mises en évidence. Deux types de modifications des fonctions objectifsont traités et les relations entre les solutions faibles/propres/ robustes efficacessont établies. Le dernier chapitre est consacré à l’analyse de sensibilité et de stabilitéen optimisation multi-objectif paramétrée. On montre sous des conditions faibles quela multi-application de l’ensemble des solutions réalisables et des valeurs réalisablessont strictement semi-différentiables. On donne quelques conditions suffisantes pourla semi-différentiabilité de l’ensemble efficace et des valeurs efficaces. De plus, nousétudions la pseudo-Lipschitz continuité des multi-applications ci dessus citées
In this thesis, three crucial questions arising in multi-objective optimization are investigated.First, the existence of properly efficient solutions via scalarization toolsis studied. A basic theorem credited to Benson is extended from the convex caseto the general case. Some further scalarization techniques are also discussed. Thesecond part of the thesis is devoted to robustness. Various notions from the literatureare briefly reviewed. Afterwards, a norm-based definition given by Georgiev, Lucand Pardalos is generalized to nonlinear multi-objective optimization. Necessary andsufficient conditions for robust solutions under appropriate assumptions are given.Relationships between new robustness notion and some known ones are highlighted.Two kinds of modifications in the objective functions are dealt with and relationshipsbetween the weak/proper/robust efficient solutions of the problems, before and afterthe perturbation, are established. Finally, we discuss the sensitivity analysis andstability in parametrized multi-objective optimization. Strict semi-differentiability ofset-valued mappings of feasible sets and feasible values is proved under appropriateassumptions. Furthermore, some sufficient conditions for semi-differentiability of efficientsets and efficient values are presented. Finally, pseudo-Lipschitz continuity ofaforementioned set-valued mappings is investigated

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El-Sayed, Jacqueline Johnson. "Multi-objective optimization of manufacturing processes design /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841282.

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Pieri, Stefano <1977&gt. "Multi-objective optimization of microgas turbine recuperatos." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2007. http://amsdottorato.unibo.it/415/1/Tesi_Pieri_Stefano.pdf.

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Pieri, Stefano <1977&gt. "Multi-objective optimization of microgas turbine recuperatos." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2007. http://amsdottorato.unibo.it/415/.

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GOMEZ, GOMEZ MANUEL. "Multi-objective optimization of power electronic converters." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2903502.

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Lidberg, Simon. "Evolving Cuckoo Search : From single-objective to multi-objective." Thesis, Högskolan i Skövde, Institutionen för teknik och samhälle, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-5309.

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This thesis aims to produce a novel multi-objective algorithm that is based on Cuckoo Search by Dr. Xin-She Yang. Cuckoo Search is a promising nature-inspired meta-heuristic optimization algorithm, which currently is only able to solve single-objective optimization problems. After an introduction, a number of theoretical points are presented as a basis for the decision of which algorithms to hybridize Cuckoo Search with. These are then reviewed in detail and verified against current benchmark algorithms to evaluate their efficiency. To test the proposed algorithm in a new setting, a real-world combinatorial problem is used. The proposed algorithm is then used as an optimization engine for a simulation-based system and compared against a current implementation.

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Büche, Dirk. "Multi-objective evolutionary optimization of gas turbine components /." [S.l.] : [s.n.], 2003. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=15240.

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Siegmund, Florian. "Sequential Sampling in Noisy Multi-Objective Evolutionary Optimization." Thesis, University of Skövde, University of Skövde, School of Humanities and Informatics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-3390.

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Most real-world optimization problems behave stochastically. Evolutionary optimization algorithms have to cope with the uncertainty in order to not loose a substantial part of their performance. There are diﬀerent types of uncertainty and this thesis studies the type that is commonly known as noise and the use of resampling techniques as countermeasure in multi-objective evolutionary optimization. Several diﬀerent types of resampling techniques have been proposed in the literature. The available techniques vary in adaptiveness, type of information they base their budget decisions on and in complexity. The results of this thesis show that their performance is not necessarily increasing as soon as they are more complex and that their performance is dependent on optimization problem and environment parameters. As the sampling budget or the noise level increases the optimal resampling technique varies. One result of this thesis is that at low computing budgets or low noise strength simple techniques perform better than complex techniques but as soon as more budget is available or as soon as the algorithm faces more noise complex techniques can show their strengths. This thesis evaluates the resampling techniques on standard benchmark functions. Based on these experiences insights have been gained for the use of resampling techniques in evolutionary simulation optimization of real-world problems.

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Wenzel, Simone [Verfasser]. "Sequential multi-objective target value optimization / Simone Wenzel." Dortmund : Universitätsbibliothek Technische Universität Dortmund, 2011. http://d-nb.info/1018126848/34.

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Castro, Junior Olacir Rodrigues. "Bio-inspired optimization algorithms for multi-objective problems." reponame:Repositório Institucional da UFPR, 2017. http://hdl.handle.net/1884/46312.

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Orientador : Aurora Trinidad Ramirez Pozo
Coorientador : Roberto Santana Hermida
Tese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 06/03/2017
Inclui referências : f. 161-72
Área de concentração : Computer Science
Resumo: Problemas multi-objetivo (MOPs) são caracterizados por terem duas ou mais funções objetivo a serem otimizadas simultaneamente. Nestes problemas, a meta é encontrar um conjunto de soluções não-dominadas geralmente chamado conjunto ótimo de Pareto cuja imagem no espaço de objetivos é chamada frente de Pareto. MOPs que apresentam mais de três funções objetivo a serem otimizadas são conhecidos como problemas com muitos objetivos (MaOPs) e vários estudos indicam que a capacidade de busca de algoritmos baseados em Pareto é severamente deteriorada nesses problemas. O desenvolvimento de otimizadores bio-inspirados para enfrentar MOPs e MaOPs é uma área que vem ganhando atenção na comunidade, no entanto, existem muitas oportunidades para inovar. O algoritmo de enxames de partículas multi-objetivo (MOPSO) é um dos algoritmos bio-inspirados adequados para ser modificado e melhorado, principalmente devido à sua simplicidade, flexibilidade e bons resultados. Para melhorar a capacidade de busca de MOPSOs, seguimos duas linhas de pesquisa diferentes: A primeira foca em métodos de líder e arquivamento. Trabalhos anteriores apontaram que esses componentes podem influenciar no desempenho do algoritmo, porém a seleção desses componentes pode ser dependente do problema. Uma alternativa para selecioná-los dinamicamente é empregando hiper-heurísticas. Ao combinar hiper-heurísticas e MOPSO, desenvolvemos um novo framework chamado H-MOPSO. A segunda linha de pesquisa também é baseada em trabalhos anteriores do grupo que focam em múltiplos enxames. Isso é feito selecionando como base o framework multi-enxame iterado (I-Multi), cujo procedimento de busca pode ser dividido em busca de diversidade e busca com múltiplos enxames, e a última usa agrupamento para dividir um enxame em vários sub-enxames. Para melhorar o desempenho do I-Multi, exploramos duas possibilidades: a primeira foi investigar o efeito de diferentes características do mecanismo de agrupamento do I-Multi. A segunda foi investigar alternativas para melhorar a convergência de cada sub-enxame, como hibridizá-lo com um algoritmo de estimativa de distribuição (EDA). Este trabalho com EDA aumentou nosso interesse nesta abordagem, portanto seguimos outra linha de pesquisa, investigando alternativas para criar versões multi-objetivo de um dos EDAs mais poderosos da literatura, chamado estratégia de evolução baseada na adaptação da matriz de covariância (CMA-ES). Para validar o nosso trabalho, vários estudos empíricos foram conduzidos para investigar a capacidade de busca das abordagens propostas. Em todos os estudos, nossos algoritmos investigados alcançaram resultados competitivos ou melhores do que algoritmos bem estabelecidos da literatura. Palavras-chave: multi-objetivo, algoritmo de estimativa de distribuição, otimização por enxame de partículas, multiplos enxames, híper-heuristicas.
Abstract: Multi-Objective Problems (MOPs) are characterized by having two or more objective functions to be simultaneously optimized. In these problems, the goal is to find a set of non-dominated solutions usually called Pareto optimal set whose image in the objective space is called Pareto front. MOPs presenting more than three objective functions to be optimized are known as Many-Objective Problems (MaOPs) and several studies indicate that the search ability of Pareto-based algorithms is severely deteriorated in such problems. The development of bio-inspired optimizers to tackle MOPs and MaOPs is a field that has been gaining attention in the community, however there are many opportunities to innovate. Multi-objective Particle Swarm Optimization (MOPSO) is one of the bio-inspired algorithms suitable to be modified and improved, mostly due to its simplicity, flexibility and good results. To enhance the search ability of MOPSOs, we followed two different research lines: The first focus on leader and archiving methods. Previous works have pointed that these components can influence the algorithm performance, however the selection of these components can be problem-dependent. An alternative to dynamically select them is by employing hyper-heuristics. By combining hyper-heuristics and MOPSO, we developed a new framework called H-MOPSO. The second research line, is also based on previous works of the group that focus on multi-swarm. This is done by selecting as base framework the iterated multi swarm (I-Multi) algorithm, whose search procedure can be divided into diversity and multi-swarm searches, and the latter employs clustering to split a swarm into several sub-swarms. In order to improve the performance of I-Multi, we explored two possibilities: the first was to further investigate the effect of different characteristics of the clustering mechanism of I-Multi. The second was to investigate alternatives to improve the convergence of each sub-swarm, like hybridizing it to an Estimation of Distribution Algorithm (EDA). This work on EDA increased our interest in this approach, hence we followed another research line by investigating alternatives to create multi-objective versions of one of the most powerful EDAs from the literature, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). In order to validate our work, several empirical studies were conducted to investigate the search ability of the approaches proposed. In all studies, our investigated algorithms have reached competitive or better results than well established algorithms from the literature. Keywords: multi-objective, estimation of distribution algorithms, particle swarm optimization, multi-swarm, hyper-heuristics.

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Faragalli, Michele. "Multi-objective design optimization of compliant lunar wheels." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=117030.

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The development of the wire-mesh wheel of the Apollo Lunar Roving Vehicle was realized through a time consuming trial and error design process, primarily driven by manufacturability and physical testing. Recent wheel development, motivated by renewed interest in lunar surface exploration, utilizes more sophisticated numerical simulation tools. However, many researchers still employ trial and error or parametric approaches to designing the wheels. This thesis proposes a systematic approach to the design optimization of compliant lunar wheels. The problem is decomposed into system and component level analyses. The system level analysis investigates the effect of elastic wheel behaviour on rover and mission performance metrics. This is realized by optimizing concept independent wheel design variables using multi-disciplinary models coupled with optimization algorithms. Wheel concepts are explored by prototyping and physical testing, as well as numerical modelling. The mobility performance metrics of cellular, segmented and iRings wheels are compared to a baseline rubber wheel. In the component level analysis, a multi-objective optimization algorithm is coupled with numerical simulations of wheel-ground interaction to find optimal cellular wheel designs. The effectiveness of the methodology to optimize cellular wheel concepts is verified, and the limitations of the approach examined. Finally, a discussion to extend the proposed methodology to alternative wheel concepts is provided.
Le développement de la roue treillis métallique de l'Apollo Lunar Roving Vehicle a été réalisé par un processus d'essais et d'erreurs. Les récents développements de roues flexibles, motivé par un regain d'intérêt pour l'exploration lunaire, ont maintenant à leur disposition des outils de simulation numérique plus sophistiqués. Cependant, la majorité des chercheurs emploient toujours des méthodes expérimentales ou paramétriques pour développer leurs roues. Cette thèse propose une nouvelle approche systématique pour l'optimisation de concepts de roues lunaires flexibles. Le problème est décomposé en deux analyses se rapportant au niveau du système et celui des composantes. L'analyse au niveau du système étudie l'effet du comportement de la roue élastique sur des mesures de performance lors d'une mission du rover. Ceci est réalisé en optimisant les paramètres décrivant une roue flexible à l'aide de modèles multidisciplinaires. Différents concepts de roues sont explorés à l'aide de prototypes et d'essais physiques, ainsi que de modélisations numériques. La performance de chacun des concepts de roues flexibles cellulaires, iRings et segmentés sont comparées à un pneu standard. L'analyse au niveau des composantes effectue une optimisation multi-objective afin de déterminer, par le biais de simulations numériques, le concept optimal de roues flexibles cellulaires. L'efficacité de la méthodologie pour optimiser la roue cellulaire est ensuite vérifiée et les limites de cette approche sont examinées en détail. Finalement, une discussion sur l'application de la méthodologie proposée à des concepts de roues arbitraires est abordée.

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Harris, Irina. "Multi-objective optimization for environmentally friendly logistics network." Thesis, Cardiff University, 2011. http://orca.cf.ac.uk/54204/.

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In contrast to the existing model formulation and solutions, we consider economic and environmental (CO₂) objectives as part of the design. Lack of benchmark data for multi-objective FLP with environmental objectives created initial difficulties in our research. However, the opportunity to work with industry ensured that we had real data to work with, and also provided a good basis for generating artificial data sets to extend our experimental work and test our techniques on very large instances with a good range of parameter setting.

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Alanis, Dimitrios. "Quantum-assisted multi-objective optimization of heterogeneous networks." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/419588/.

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Some of the Heterogeneous Network (HetNet) components may act autonomously for the sake of achieving the best possible performance. The attainable routing performance depends on a delicate balance of diverse and often conflicting Quality-of-Service (QoS)requirements. Finding the optimal solution typically becomes an NP-hard problem, as the network size increases in terms of the number of nodes. Moreover, the employment of user defined utility functions for the aggregation of the different objective functions often leads to suboptimal solutions. On the other hand, Pareto Optimality is capable of amalgamating the different design objectives by relying on an element of elitism. Although there is a plethora of bio-inspired algorithms that attempt to address the associated multi-component optimization problem, they often fail to generate all the routes constituting the Optimal Pareto Front (OPF). As a remedy, we initially propose an optimal multi-objective quantum-assisted algorithm, namely the Non-dominated Quantum Optimization (NDQO) algorithm, which evaluates the legitimate routes using the concept of Pareto Optimality at a reduced complexity. We then compare the performance of the NDQO algorithm to the state-of-the-art evolutionary algorithms, demonstrating that the NDQO algorithm achieves a near-optimal performance. Furthermore, we analytically derive the upper and lower bounds of the NDQO’s algorithmic complexity, which is of the order of O(N) and O(N√N) in the best- and worst-case scenario, respectively. This corresponds to a substantial complexity reduction of the NDQO from the order of O(N2)imposed by the brute-force (BF) method. However again, as the number of nodes increases, the total number of routes increases exponentially, making its employment infeasible despite the complexity reduction offered. Therefore, we propose a novel optimal quantum-assisted algorithm, namely the Non-Dominated Quantum Iterative Optimization (NDQIO) algorithm, which exploits the synergy between the hardware parallelism and the quantum parallelism for the sake of achieving a further complexity reduction, which is on the order of O(√N) and O(N√N)in the best- and worst-case scenarios, respectively. Additionally, we provide simulation results for demonstrating that our NDQIO algorithm achieves an average complexity reduction of almost an order of magnitude compared to the near-optimal NDQO algorithm,while activating the same order of comparison operators. Apart from the traditional QoS requirements, the network design also has to consider the nodes’ user-centric social behavior. Hence, the employment of socially-aware load balancing becomes imperative for avoiding the potential formation of bottlenecks in the network’s packet-flow. Therefore, we also propose a novel algorithm, referred to as the Multi-Objective Decomposition Quantum Optimization (MODQO) algorithm, which exploits the quantum parallelism to its full potential by exploiting the database correlations for performing multi-objective routing optimization, while at the same time balancing the tele-traffic load among the nodes without imposing a substantial degradation on the network’s delay and power consumption. Furthermore, we introduce a novel socially-aware load balancing metric, namely the normalized entropy of the normalized composite betweenness of the associated socially-aware network, for striking a better trade-off between the network’s delay and power consumption. We analytically prove that the MODQO algorithm achieves the full-search based accuracy at a significantly reduced complexity, which is several orders of magnitude lower than that of the full-search. Finally, we compare the MODQO algorithm to the classic NSGA-II evolutionary algorithm and demonstrate that the MODQO succeeds in halving the network’s average delay, whilst simultaneously reducing the network’s average power consumption by 6 dB without increasing the computational complexity.

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Hatzakis, Iason. "Multi-objective evolutionary optimization in time-changing environments." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39842.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 127-135).
This research is focused on the creation of evolutionary optimization techniques for the solution of time-changing multi-objective problems. Many optimization problems, ranging from the design of controllers for time-variant systems to the optimal asset allocation in financial portfolios, need to satisfy multiple conflicting objectives that change in time. Since most practical problems involve costly numerical simulations, the goal was to create algorithmic architectures that increase computational efficiency while being robust and widely applicable. A combination of two elements lies at the core of the proposed algorithm. First, there is an anticipatory population that helps the algorithm discover the new optimum when the objective landscape moves in time. Second, a preservation of the balance between convergence and diversity in the population which provides an exploration ability to the algorithm. If there is an amount of predictability in the landscape's temporal change pattern the anticipatory population increases performance by discovering each timestep's optimal solution using fewer function evaluations. It does so by estimating the optimal solution's motion with a forecasting model and then placing anticipatory individuals at the estimated future location.
(cont.) In parallel, the preservation of diversity ensures that the optimum will be discovered even if the objectives motion is unpredictable. Together these two elements aim to create a well-performing and robust algorithmic architecture. Experiments show that the overall concept functions well and that the anticipatory population increases algorithm performance by up to 30%. Constraint handling methods for evolutionary algorithms are also implemented, since most of the problems treated in this work are constrained. In its final form the constraint handling method applied is a hybrid variant of the Superiority of Feasible Points, which works in a staged manner. Three different real-world applications are explored. Initially a radar telescope array is optimized for cost and performance as a practical example of a static multi-objective constrained problem. Subsequently, two time-changing problems are studied: the design of an industrial controller and the optimal asset allocation for a financial portfolio. These problems serve as examples of applications for time-changing multi-objective evolutionary algorithms and inspire the improvement of the methods proposed in this work.
by Iason Hatzakis.
Ph.D.

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Haseeb, Nablul. "Multi-objective optimization of vertically mixed lateral systems." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111552.

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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 50-51).
This thesis explores the advantages of using vertically mixed lateral systems in rectangular buildings consisting of uniform bay dimensions. Three forms of lateral systems i.e. moment frames, steel cross bracings and concrete shear walls are utilized at varying elevations of the building to determine an optimal set of results. Besides analyzing structural optimality, the MATLAB algorithm developed as a part of this research paper also evaluates each system for its overall structural weight, material cost and embodied carbon. By taking a multi-objective optimization approach at the design of lateral load-resisting systems in buildings, this research devises a practical tool that can be used by designers to assess and examine the advantages and disadvantages of various layouts of lateral systems. The algorithm also enables the user to specify the location and type of certain lateral elements, which may correspond to practical architectural constraints, and juxtapose results from user-defined layouts with the optimized solution.
by Nablul Haseeb.
M. Eng.

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Zhong, Hongliang. "Bandit feedback in Classification and Multi-objective Optimization." Thesis, Ecole centrale de Marseille, 2016. http://www.theses.fr/2016ECDM0004/document.

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Des problèmes de Bandit constituent une séquence d’allocation dynamique. D’une part, l’agent de système doit explorer son environnement ( à savoir des bras de machine) pour recueillir des informations; d’autre part, il doit exploiter les informations collectées pour augmenter la récompense. Comment d’équilibrer adéquatement la phase d’exploration et la phase d’exploitation, c’est une obscurité des problèmes de Bandit, et la plupart des chercheurs se concentrent des efforts sur les stratégies d’équilibration entre l’exploration et l’exploitation. Dans cette dissertation, nous nous concentrons sur l’étude de deux problèmes spécifiques de Bandit: les problèmes de Bandit contextuel et les problèmes de Bandit Multi- objectives. Cette dissertation propose deux aspects de contributions. La première concerne la classification sous la surveillance partielle, laquelle nous codons comme le problème de Bandit contextuel avec des informations partielles. Ce type des problèmes est abondamment étudié par des chercheurs, en appliquant aux réseaux sociaux ou systèmes de recommandation. Nous proposons une série d’algorithmes sur la base d’algorithme Passive-Aggressive pour résoudre des problèmes de Bandit contextuel. Nous profitons de sa fondations, et montrons que nos algorithmes sont plus simples à mettre en œuvre que les algorithmes en état de l’art. Ils réalisent des biens performances de classification. Pour des problèmes de Bandit Multi-objective (MOMAB), nous proposons une méthode motivée efficace et théoriquement à identifier le front de Pareto entre des bras. En particulier, nous montrons que nous pouvons trouver tous les éléments du front de Pareto avec un budget minimal dans le cadre de PAC borne
Bandit problems constitute a sequential dynamic allocation problem. The pulling agent has to explore its environment (i.e. the arms) to gather information on the one hand, and it has to exploit the collected clues to increase its rewards on the other hand. How to adequately balance the exploration phase and the exploitation phase is the crux of bandit problems and most of the efforts devoted by the research community from this fields has focused on finding the right exploitation/exploration tradeoff. In this dissertation, we focus on investigating two specific bandit problems: the contextual bandit problems and the multi-objective bandit problems. This dissertation provides two contributions. The first contribution is about the classification under partial supervision, which we encode as a contextual bandit problem with side informa- tion. This kind of problem is heavily studied by researchers working on social networks and recommendation systems. We provide a series of algorithms to solve the Bandit feedback problem that pertain to the Passive-Aggressive family of algorithms. We take advantage of its grounded foundations and we are able to show that our algorithms are much simpler to implement than state-of-the-art algorithms for bandit with partial feedback, and they yet achieve better perfor- mances of classification. For multi-objective multi-armed bandit problem (MOMAB), we propose an effective and theoretically motivated method to identify the Pareto front of arms. We in particular show that we can find all elements of the Pareto front with a minimal budget

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Шендрик, Віра Вікторівна, Вера Викторовна Шендрик, Vira Viktorivna Shendryk, O. Romanko, A. Deza, and A. Cherednychenko. "Using of multi-objective optimization in financial portfolios." Thesis, Сумський державний університет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/31633.

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The main goal of this work was the improvement of calculation and graphing for analysis of different dimension financial portfolios, which will increase Matlab-tools work accuracy and efficiency. Was made comparative analysis of Matlab instruments for working with financial portfolios, and was offered methods to improve graphing and increase the accuracy of the calculation. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/31633

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Усенко, Наталія Миколаївна, Наталия Николаевна Усенко, Nataliia Mykolaivna Usenko, and O. Shcherbacov. "Multi-objective optimization of a 3D vaneless diffuser." Thesis, Видавництво СумДУ, 2010. http://essuir.sumdu.edu.ua/handle/123456789/17187.

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Tuyiragize, Richard. "Multi-objective optimization techniques in electricity generation planning." Doctoral thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/10720.

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The objective of this research is to develop a framework of multi-objective optimization (MOO) models that are better capable of providing decision support on future long-term electricity generation planning (EGP), in the context of insufficient electricity capacity and to apply it to the electricity system for a developing country. The problem that motivated this study was a lack of EGP models in developing countries to keep pace with the countries' socio-economic and demographic dynamics. This research focused on two approaches: mathematical programming (MP) and system dynamics (SD). Detailed model descriptions, formulations, and implementation results are presented in the thesis along with the observations and insights obtained during the course of this research.

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Gandhi, Ansh, and Ankur Fartyal. "Multi-Objective Optimization of Torque Distribution inHybrid Vehicles." Thesis, KTH, Fordonsdynamik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280548.

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Electrification is one of the mega-trends in the transportation and automotive industry today. Boththe alarming environmental conditions and the ever decreasing fuel reserves are driving the shifttowards hybrid, all electric and alternative fuel source vehicles. This thesis work is another smallstep towards studying, addressing and handling this issue while also laying the groundwork for developingand moving towards more efficient and commercially viable vehicles.This thesis work aims at investigating the trade-off offered by optimal control techniques betweenenergy consumption and reference tracking for torque allocation to the various actuators available topropel a hybrid electric vehicle. The particular vehicle under consideration has two electric motorsat the rear wheels and an internal combustion engine along with an integrator starter generatordriving the front wheels. The torque allocation problem is originally solved by proposing a one stageoptimization strategy (OSOS) that takes into account actuator limits, losses, and objectives throughconstraints. The performance of this formulation is presented over two simulated test tracks on apareto front where the advantage on relaxing complete reference tracking becomes visible. Next,two new formulations each as a two stage optimization strategies (TSOS) are proposed, the mainobjective being to split the original formulation into two parts. One addressing energy optimalityand the other addressing reference tracking of total wheel torque and yaw moment request fulfilment.These formulations are then similarly investigated and presented in comparison with the originalformulation. In developing the formulations, an assumption about the loss models is made andthe problem size of the second stage quadratic program is significantly reduced. The problems areappropriately scaled and made mathematically robust to handle the constraints and inputs in theoperating range. As reference tracking for the vehicle is split into lateral and longitudinal torquerequests from the vehicle, this becomes a multi-objective optimization problem. To further studythe behaviour of these formulations, they are given constant inputs and simulated over a single timestep. The effect of changing hybridization level, i.e, the amount of electrical energy used comparedto fuel energy on the behaviour of these formulations is also explored. One of the effects of the twostage formulations was the confinement of solutions within a reasonable error for the majority ofchosen weights due to the energy considerations in the first stage. The proposed formulations wereable to generate results close but not equal to the original formulation on the pareto front. Anotherfinding was that due to the implementation of two actuators at the rear of the vehicle, a desired yawrate could be achieved at no additional energy cost because of regenerative and propulsive torquesgenerated respectively on either side of rear axle for torque vectoring. Furthermore with a dedicatedsolver, the TSOS could present an interesting alternative to enhance independent development invehicle dynamics control and energy management of the vehicle.
För att minska miljöpåverkan av utsläpp från bilkörning är det ett ökat fokus på elektrifieringav bilar och förnybara bränslen. Detta examensarbete försöker att bidra till smartare användningav energiresurser under körning. Syftet är att bygga upp kunskap om effektiv energihantering ihybridfordon. En optimeringsbaserad reglerteknik används för att allokera drivmoment till olikaaktuatorer på drivlinan av ett hybridfordon och bestämma en balans mellan energieffektivitet ochmomenthantering för bättre körbarhet. Det studerade hybridfordonet har två elektriska motorermonterade på bakaxeln därtill har en förbränningsmotor och integrerad startmotor och generator påframaxeln. Momentallokeringsproblemet är ursprungligen löst med hjälp av en optimeringsstrategisom sker i ett steg som tar hänsyn till exempelvis systemets begränsningar och energiförluster.Denna formulering är förändrad för att skapa två olika formuleringar av en optimeringsstrategi somsker i två steg. Prestandan för de olika strategierna jämförs därefter på två olika simulerade testbanoroch visualiserades med hjälp av en paretofront. Syftet med att dela upp beräkningen i tvåsteg är för att göra det modulärt och förenkla gränssnittet mellan fordonsdynamik och fordonsenergihantering.Med denna uppdelning kan det ske oberoende forskning och utveckling inom bådaområdena. Det första steget hanterar energin och det andra steget hanterar hur väl systemet kanuppnå referensfördelningen av drivmoment och girmoment. De nya formuleringarna är baserade påen antagen förlustmodell och är omvandlade via skalning för att därefter jämföras med den ursprungligaformuleringen för att identifiera unika beteenden. En effekt av två-stegsformuleringen var attlösningarna begränsades inom ett rimligt felområde som en följd av energihanteringen i det förstasteget . Lösningarna från två-stegsformuleringen kom väldigt nära resultaten för den ursprungligaformuleringen. Två-stegsformuleringen kan vara ett bättre alternativ för att ha en modulär reglersystemsarkitekturom den kan utvecklas vidare med en dedikerad programvara i fordonets styrenhetsom använder ett lämpligt sätt för att räkna ut det optimeringsproblemet.

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Li, Lisa. "Multi-objective A* Route Optimization for Terrain Vehicles." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285951.

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To investigate the extent for which the A* algorithm could find non-dominated routes, A* was compared to the NAMOA* algorithm for multi-objective terrain route optimization. NAMOA* is a multi-objective generalization of A*. NAMOA* has previously been proven to return all non-dominated routes. The algorithms were compared on a terrain route problem inspired by military planning. The minimization objectives were distance, travel time, accumulated forest volume, and accumulated likelihood of detection by static observers. The likelihood of detection by static observers depended on if the vehicle was within any observer’s line of sight. The algorithms were evaluated on four scenarios that were based on geodata. A* minimized an objective function created by weighting the minimization objectives. A* was executed multiple times with 200 randomized weights and 7 set weights to obtain a set of solution routes. The solution routes were compared geographically, through visualization of the Pareto front, and using the hypervolume indicator. The distribution of A* routes was similar to the distribution of NAMOA* routes. The A* routes were not always non-dominated, but close to nondominated except for outliers. A possible preference for extremes in the likelihood of detection objective was identified for A*. For the following results, the average refers to the mean of the evaluation scenarios. The hypervolume was on average 17.4% lower for A*, likely due to it having a lower number of routes compared to NAMOA*. The execution time of A* was on average 518 times faster than NAMOA*, which was expected since NAMOA* returns all non-dominated routes.
A* algoritmen jämfördes med NAMOA* algoritmen för flermålsoptimering av terrängrutter. Syftet var att undersöka till vilken grad A* kunde hitta ickedominerade rutter. NAMOA* är en generalisering av A* för flermålsoptimering. NAMOA* har tidigare bevisats ge alla icke-dominerade rutter. Algoritmerna jämfördes på ett terrängruttsproblem inspirerat av planeringsverksamhet för militäroperationer. Målfunktionerna som minimerades var distans, restid, ackumulerad skogsvolym, och ackumulerad upptäcktssannolikhet av statiska observatörer. Upptäcktssannolikheten av statiska observatörer berodde på om fordonet uppehöll sig inom någon observatörs siktlinje. Algoritmerna jämfördes på fyra evalueringsscenarion som var baserade på geodata. A* minimerade en målfunktion som viktade samman minimeringsmålen, och exekverades flera gånger med 200 slumpmässiga och 7 fixerade vikter för att skapa en mängd av rutter. Algoritmernas ruttmängder jämfördes geografiskt, genom visualisering av Paretofronten, och genom hypervolym. Fördelningen av A* rutter liknade fördelningen av NAMOA* rutter. A* rutter var inte alltid icke-dominerade, men nära icke-dominerade förutom för några avvikande rutter. En möjlig preferens för extrema upptäcktssannolikhetsvärden identifierades. Hypervolymen var i medelvärde 17.4% lägre för A*, antagligen en konsekvens av att A* rutterna var färre än NAMOA* rutter. Exekveringstiden för A* var i medelvärde 518 gånger snabbare än NAMOA*, vilket var förväntat då NAMOA* ger alla icke-dominerade rutter.

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Radtke, Paulo Vinicius Wolski. "Classification systems optimization with multi-objective evolutionary algorithms." Thèse, Montréal : École de technologie supérieure, 2006. http://proquest.umi.com/pqdweb?did=1251872111&sid=5&Fmt=2&clientId=46962&RQT=309&VName=PQD.

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Thèse (Ph.D.)-- École de technologie supérieure, Montréal, 2006.
"A thesis presented to the École de technologie supérieure in fullfilment of the thesis requirement for the degree of philosophiae doctor in engineering". CaQMUQET Bibliogr.: f. [163]-173. Également disponible en version électronique. CaQMUQET

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Aguirre, Ortega Oswaldo. "Multi-objective network reliability optimization using evolutionary algorithms." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Song, Xiaoxiao. "Layout optimization based on multi-objective interactive approach." Thesis, Ecole centrale de Nantes, 2022. http://www.theses.fr/2022ECDN0051.

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Dans tous les problèmes d’agencement, les contraintes de non-chevauchement entre composants et les contraintes d’appartenanceau conteneur sont présentes. Un modèle d’agencement multiobjectif avec contraintes fonctionnelles est développé. Intégrer l’accessibilité des composants comme contraintes fonctionnelles assure la maintenance ou le fonctionnement des composants. Cependant, les contraintes fonctionnelles augmentent la complexité d’optimisation d’agencement, notée indice de capacité. Par conséquent, un nouvel algorithme d’optimisation multiobjectif est proposé en utilisant le placement constructif et le recuit simulé pour rechercher des solutions de compromis entre les objectifs multiples. Ensuite, un indicateur de similarité est défini pour évaluer les similaires entre les solutions proposées par l’algorithme. Les expériences indiquent que l’approche d’optimistion proposée fonctionne bien pour garantir l’accessibilité et trouver efficacement des solutions optimales dans les problèmes industriels d’agencement d’espace à un ou plusieurs conteneurs, où l’analyse de similarité démontre une bonne diversité solutions proposées par l’algorithme, qui peut être appliqué en tant qu’outil interactif outil pour leconcepteur
The conventional layout problem is concerned with finding the arrangements of components inside the container to optimize objectives under geometrical constraints, i.e., no component overlap and no container protrusion. A multi-objective layout model with functional constraints is developed. Integrating the accessibility of components as functional constraints ensures components maintenance or proper operation. However, thefunctional constraints increase the layout optimization complexity, denoted as capacity index. Therefore, a novel multi-objective optimizationalgorithm is proposed using the constructive placement and the simulated annealing to search for compromised solutions between the multiple objectives. Thereafter, a similarity indicator is defined to evaluate how similar optimized layout designs are. The experiments indicate that the proposed optimization approach performs well in ensuring accessibility and efficiently finding high-qualified solutions in single- and multi- container layoutapplications, where the similarity analysis demonstrates good diversity of the obtained layout set, which can be applied as an interactive tool

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Tangpattanakul, Panwadee. "Multi-objective optimization of earth observing satellite missions." Thesis, Toulouse, INSA, 2013. http://www.theses.fr/2013ISAT0023/document.

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Cette thèse considère le problème de sélection et d’ordonnancement des prises de vue d’un satellite agile d’observation de la Terre. La mission d’un satellite d’observation est d’obtenir des photographies de la surface de la Terre afin de satisfaire des requêtes d’utilisateurs. Les demandes, émanant de différents utilisateurs, doivent faire l’objet d’un traitement avant transmission d’un ordre vers le satellite, correspondant à une séquence d’acquisitions sélectionnées. Cette séquence doit optimiser deux objectifs sous contraintes d’exploitation. Le premier objectif est de maximiser le profit global des acquisitions sélectionnées. Le second est d’assurer l’équité du partage des ressources en minimisant la différence maximale de profit entre les utilisateurs. Deux métaheuristiques, composées d’un algorithme génétique à clé aléatoire biaisées (biased random key genetic algorithm - BRKGA) et d’une recherche locale multi-objectif basée sur des indicateurs (indicator based multi-objective local search - IBMOLS), sont proposées pour résoudre le problème. Pour BRKGA, trois méthodes de sélection, empruntées à NSGA-II, SMS-EMOA, et IBEA, sont proposées pour choisir un ensemble de chromosomes préférés comme ensemble élite. Trois stratégies de décodage, parmi lesquelles deux sont des décodages uniques et la dernière un décodage hybride, sont appliquées pour décoder les chromosomes afin d’obtenir des solutions. Pour IBMOLS, plusieurs méthodes pour générer la population initiale sont testées et une structure de voisinage est également proposée. Des expériences sont menées sur des cas réalistes, issus d’instances modifiées du challenge ROADEF 2003. On obtient ainsi les fronts de Pareto approximés de BRKGA et IBMOLS dont on calcule les hypervolumes. Les résultats de ces deux algorithmes sont comparés
This thesis considers the selection and scheduling problem of observations for agile Earth observing satellites. The mission of Earth observing satellites is to obtain photographs of the Earth surface to satisfy user requirements. Requests from several users have to be managed before transmitting an order, which is a sequence of selected acquisitions, to the satellite. The obtained sequence must optimize two objectives under operation constraints. The first objective is to maximize the total profit of the selected acquisitions. The second one is to ensure the fairness of resource sharing by minimizing the maximum profit difference between users. Two metaheuristic algorithms, consisting of a biased random key genetic algorithm (BRKGA) and an indicator-based multi-objective local search (IBMOLS), are proposed to solve the problem. For BRKGA, three selection methods, borrowed from NSGA-II, SMS-EMOA, and IBEA, are proposed to select a set of preferred chromosomes to be the elite set. Three decoding strategies, which are two single decoding and a hybrid decoding, are applied to decode chromosomes to become solutions. For IBMOLS, several methods for generating the initial population are tested and the neighborhood structure according to the problem is also proposed. Experiments are conducted on realistic instances based on ROADEF 2003 challenge instances. Hypervolumes of the approximate Pareto fronts are computed and the results from the two algorithms are compared

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Kaddani, Sami. "Partial preference models in discrete multi-objective optimization." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLED012/document.

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Les problèmes d’optimisation multi-objectifs mènent souvent à considérer des ensembles de points non-dominés très grands à mesure que la taille et le nombre d’objectifs du problème augmentent. Générer l’ensemble de ces points demande des temps de calculs prohibitifs. De plus, la plupart des solutions correspondantes ne sont pas pertinentes pour un décideur. Une autre approche consiste à utiliser des informations de préférence, ce qui produit un nombre très limité de solutions avec des temps de calcul réduits. Cela nécessite la plupart du temps une élicitation précise de paramètres. Cette étape est souvent difficile pour un décideur et peut amener à délaisser certaines solutions intéressantes. Une approche intermédiaire consiste à raisonner avec des relations de préférences construites à partir d’informations partielles. Nous présentons dans cette thèse plusieurs modèles de relations partielles de préférences. En particulier, nous nous sommes intéressés à la génération de l’ensemble des points non-dominés selon ces relations. Les expérimentations démontrent la pertinence de notre approche en termes de temps de calcul et qualité des points générés
Multi-objective optimization problems often lead to large nondominated sets, as the size of the problem or the number of objectives increases. Generating the whole nondominated set requires significant computation time, while most of the corresponding solutions are irrelevant to the decision maker. Another approach consists in obtaining preference information, which reduces the computation time and produces one or a very limited number of solutions. This requires the elicitation of precise preference parameters most of the time, which is often difficult and partly arbitrary, and might discard solutions of interest. An intermediate approach consists in using partial preference models.In this thesis, we present several partial preference models. We especially focused on the generation of the nondominated set according to these preference relations. This approach shows competitive performances both on computation time and quality of the generated preferred sets

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Tezcaner, Diclehan. "Multi-objective Route Selection." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12610767/index.pdf.

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In this thesis, we address the route selection problem for Unmanned Air Vehicles (UAV) under multiple objectives. We consider a general case for this problem where the UAV has to visit several targets and return to the base. For this case, there are multiple combinatorial problems to be considered. First, the paths to be followed between any pairs of targets should be determined. This part can be considered as a multi-objective shortest path problem. Additionally, we need to determine the order of the targets to be visited. This in turn, is a multi-objective traveling salesperson problem. The overall problem is a combination of these two combinatorial problems. The route selection for UAVs has been studied by several researchers, mainly in the military context. They considered a linear combination of the two objectives
minimizing distance traveled and minimizing radar detection threat
and proposed heuristics for the minimization of the composite single objective problem. We treat these two objectives separately. We develop an evolutionary algorithm to determine the efficient tours. We also consider an exact interactive approach to identify the best paths and tours of a decision maker. We tested the two solution approaches on both small-sized and large-sized problem instances.

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Scheepers, Christiaan. "Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/64041.

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An exploratory analysis in low-dimensional objective space of the vector evaluated particle swarm optimization (VEPSO) algorithm is presented. A novel visualization technique is presented and applied to perform the exploratory analysis. The exploratory analysis together with a quantitative analysis revealed that the VEPSO algorithm continues to explore without exploiting the well-performing areas of the search space. A detailed investigation into the influence that the choice of archive implementation has on the performance of the VEPSO algorithm is presented. Both the Pareto-optimal front (POF) solution diversity and convergence towards the true POF is considered during the investigation. Attainment surfaces are investigated for their suitability in efficiently comparing two multi-objective optimization (MOO) algorithms. A new measure to objectively compare algorithms in multi-dimensional objective space, based on attainment surfaces, is presented. This measure, referred to as the porcupine measure, adapts the attainment surface measure by using a statistical test along with weighted intersection lines. Loosely based on the VEPSO algorithm, the multi-guided particle swarm optimization (MGPSO) algorithm is presented and evaluated. The results indicate that the MGPSO algorithm overcomes the weaknesses of the VEPSO algorithm and also outperforms a number of state of the art MOO algorithms on at least two benchmark test sets.
Thesis (PhD)--University of Pretoria, 2017.
Computer Science
PhD
Unrestricted

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Maach, Fouad. "Bi-objective multi-assignment capacitated location-allocation problem." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/31558.

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Optimization problems of location-assignment correspond to a wide range of real situations, such as factory network design. However most of the previous works seek in most cases at minimizing a cost function. Traffic incidents routinely impact the performance and the safety of the supply. These incidents can not be totally avoided and must be regarded. A way to consider these incidents is to design a network on which multiple assignments are performed.

Precisely, the problem we focus on deals with power supplying that has become a more and more complex and crucial question. Many international companies have customers who are located all around the world; usually one customer per country. At the other side of the scale, power extraction or production is done in several sites that are spread on several continents and seas. A strong willing of becoming less energetically-dependent has lead many governments to increase the diversity of supply locations. For each kind of energy, many countries expect to deal ideally with 2 or 3 location sites. As a decrease in power supply can have serious consequences for the economic performance of a whole country, companies prefer to balance equally the production rate among all sites as the reliability of all the sites is considered to be very similar. Sharing equally the demand between the 2 or 3 sites assigned to a given area is the most common way. Despite the cost of the network has an importance, it is also crucial to balance the loading between the sites to guarantee that no site would take more importance than the others for a given area. In case an accident happens in a site or in case technical problems do not permit to satisfy the demand assigned to the site, the overall power supply of this site is still likely to be ensured by the one or two available remaining site(s). It is common to assign a cost per open power plant and another cost that depends on the distance between the factory or power extraction point and the customer. On the whole, such companies who are concerned in the quality service of power supply have to find a good trade-off between this factor and their overall functioning cost. This situation exists also for companies who supplies power at the national scale. The expected number of areas as well that of potential sites, can reach 100. However the targeted size of problem to be solved is 50.

This thesis focuses on devising an efficient methodology to provide all the solutions of this bi-objective problem. This proposal is an investigation of close problems to delimit the most relevant approaches to this untypical problem. All this work permits us to present one exact method and an evolutionary algorithm that might provide a good answer to this problem.
Master of Science

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Lin, Maokai. "Multi-objective constrained optimization for decision making and optimization for system architectures." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/58188.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 171-174).
This thesis proposes new methods to solve three problems: 1) how to model and solve decision-making problems, 2) how to translate between a graphical representation of systems and a matrix representation of systems, and 3) how to cluster single and multiple Design Structure Matrices (DSM). To solve the first problem, the thesis provides an approach to model decisionmaking problems as multi-objective Constraint Optimization Problems (COP) based on their common structures. A set of new algorithms to find Pareto front of multi objective COP is developed by generalizing upon the Conflict-directed A* (CDA*) algorithm for single-objective COPs. Two case studies - Apollo mission mode study and earth science decadal survey study - are provided to demonstrate the effectiveness of the modelling approach and the set of algorithms when they are applied to real world problems. For the second problem, the thesis first extends classical DSMs to incorporate different relations between components in a system. The Markov property of the extended DSM is then revealed. Furthermore, the thesis introduces the concept of "projection", which maps and condenses a system graph to a DSM based on the Markov property of DSM. For the last problem, an integer programming model is developed to encode the single DSM clustering problem. The thesis tests the effectiveness of the model by applying it to a part of a real-world jet engine design project. The model is further extended to solve the multiple DSM clustering problems.
by Maokai Lin.
S.M.

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Lokman, Banu. "Converging Preferred Regions In Multi-objective Combinatorial Optimization Problems." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613379/index.pdf.

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Finding the true nondominated points is typically hard for Multi-objective Combinatorial Optimization (MOCO) problems. Furthermore, it is not practical to generate all of them since the number of nondominated points may grow exponentially as the problem size increases. In this thesis, we develop an exact algorithm to find all nondominated points in a specified region. We combine this exact algorithm with a heuristic algorithm that approximates the possible locations of the nondominated points. Interacting with a decision maker (DM), the heuristic algorithm first approximately identifies the region that is of interest to the DM. Then, the exact algorithm is employed to generate all true nondominated points in this region. We conduct experiments on Multi-objective Assignment Problems (MOAP), Multi-objective Knapsack Problems (MOKP) and Multi-objective Shortest Path (MOSP) Problems
and the algorithms work well. Finding the worst possible value for each criterion among the set of efficient solutions has important uses in multi-criteria problems since the proper scaling of each criterion is required by many approaches. Such points are called nadir points. v It is not straightforward to find the nadir points, especially for large problems with more than two criteria. We develop an exact algorithm to find the nadir values for multi-objective integer programming problems. We also find bounds with performance guarantees. We demonstrate that our algorithms work well in our experiments on MOAP, MOKP and MOSP problems. Assuming that the DM'
s preferences are consistent with a quasiconcave value function, we develop an interactive exact algorithm to solve MIP problems. Based on the convex cones derived from pairwise comparisons of the DM, we generate constraints to prevent points in the implied inferior regions. We guarantee finding the most preferred point and our computational experiments on MOAP, MOKP and MOSP problems show that a reasonable number of pairwise comparisons are required.

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Bhargava, Suvrat. "Multi-objective optimization of the molecular structure of refrigerants." FIU Digital Commons, 2010. http://digitalcommons.fiu.edu/etd/1672.

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The aim of this work was to develop a new methodology, which can be used to design new refrigerants that are better than the currently used refrigerants. The methodology draws some parallels with the general approach of computer aided molecular design. However, the mathematical way of representing the molecular structure of an organic compound and the use of meta models during the optimization process make it different. In essence, this approach aimed to generate molecules that conform to various property requirements that are known and specified a priori. A modified way of mathematically representing the molecular structure of an organic compound having up to four carbon atoms, along with atoms of other elements such as hydrogen, oxygen, fluorine, chlorine and bromine, was developed. The normal boiling temperature, enthalpy of vaporization, vapor pressure, tropospheric lifetime and biodegradability of 295 different organic compounds, were collected from open literature and data bases or estimated. Surrogate models linking the previously mentioned quantities with the molecular structure were developed. Constraints ensuring the generation of structurally feasible molecules were formulated and used in commercially available optimization algorithms to generate molecular structures of promising new refrigerants. This study was intended to serve as a proof-of-concept of designing refrigerants using the newly developed methodology.

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Desjardins, Benjamin. "Reliable Robot-Assisted Sensor Relocation via Multi-Objective Optimization." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35034.

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Wireless sensor networks (WSNs) are an emerging area of technology that have applications across many domains. By adding a mobile platform to the WSN we can increase its capabilities. One such scenario involves a mobile platform relocating sensors to fill sensing holes that are the result of sensor failure. We examine this problem, known as robot-assisted sensor relocation (RASR), and propose our own, multi-objective version, that we call reliable robot-assisted sensor relocation. We solve this problem using a set of state-of-the-art evolutionary multi-objective optimization algorithms. Additionally, we examine the multi-robot model, which we christen reliable multiple robot-assisted sensor relocation (RMRASR). The works collected within define these problems as well as provide empirical insight into the performance of well-known algorithms using these problems as a test-bed.

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Nordström, Peter. "Multi-objective optimization and Pareto navigation for voyage planning." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-220338.

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The shipping industry is very large and ships require a substantial amount of fuel. However, fuel consumption is not the only concern. Time of arrival, safety concerns, distance travelled etc. are also of importance and these objectives might be inherently conflicting. This thesis aims to demonstrate multi-objective optimization and Pareto navigation for application in voyage planning. In order to perform this optimization, models of weather, ocean conditions, ship dynamics and propulsion system are needed. Statistical methods for estimation of resistance experienced in calm and rough sea are used. An earlier developed framework is adopted to perform the optimization and Pareto navigation. The results show that it is a suitable approach in voyage planning. A strength of the interactive Pareto navigation is the overview of the solution space presented to the decision maker and the control of the spread of the objective space. Another benefit is the possibilities of assigning specific values on objectives and setting thresholds in order to narrow down the solution space. The numerical results reinforces the trend of slow steaming to decrease fuel consumption.

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

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