Rozprawy doktorskie na temat „Multi-Objective”
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Maashi, Mashael. "An investigation of multi-objective hyper-heuristics for multi-objective optimisation". Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/14171/.
Pełny tekst źródłaTezcaner, Diclehan. "Multi-objective Route Selection". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12610767/index.pdf.
Pełny tekst źródłaminimizing 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.
Chatterjee, H. K. "Multi-objective, interactive programming". Thesis, University of Manchester, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.376590.
Pełny tekst źródłaLewis, Alyn Martyn. "Multi-objective bandit problems". Thesis, Keele University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283977.
Pełny tekst źródłaJamil, Ramey. "Multi-objective control allocation". Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/10735.
Pełny tekst źródłaLidberg, 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.
Pełny tekst źródłaDasgupta, Sumantra. "Multi-objective stochastic path planning". [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2755.
Pełny tekst źródłaAmouzgar, 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.
Pełny tekst źródłaRoland, 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.
Pełny tekst źródłaoptimization 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
Wang, Weijia. "Multi-objective sequential decision making". Phd thesis, Université Paris Sud - Paris XI, 2014. http://tel.archives-ouvertes.fr/tel-01057079.
Pełny tekst źródłaKipouros, Timoleon. "Multi-objective aerodynamic design optimisation". Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614261.
Pełny tekst źródłaSoylu, Banu. "An Evolutionary Algorithm For Multiple Criteria Problems". Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608134/index.pdf.
Pełny tekst źródłaFieldsend, Jonathan E. "Novel algorithms for multi-objective search and their application in multi-objective evolutionary neural network training". Thesis, University of Exeter, 2003. http://hdl.handle.net/10871/11706.
Pełny tekst źródłaYuan, Xiaoyan. "Multi-Functional Reconfigurable Antenna Development by Multi-Objective Optimization". DigitalCommons@USU, 2012. https://digitalcommons.usu.edu/etd/1326.
Pełny tekst źródłaRollón, Emma. "Multi-objective optimization in graphical models". Doctoral thesis, Universitat Politècnica de Catalunya, 2008. http://hdl.handle.net/10803/108180.
Pełny tekst źródłaMuchos 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.
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.
Pełny tekst źródłaClark, Andrew Robert James. "Multi-objective ROC learning for classification". Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3530.
Pełny tekst źródłaMohamed, Radzi Nor Haizan. "Multi-objective planning using linear programming". Thesis, University of Strathclyde, 2010. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=15344.
Pełny tekst źródłaNezhadali, Vaheed. "Multi-objective optimization of Industrial robots". Thesis, Linköpings universitet, Maskinkonstruktion, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-113283.
Pełny tekst źródłaMao, K. "Multi-objective search-based mobile testing". Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/1553273/.
Pełny tekst źródłaMsaaf, Khaoula. "Multi-Objective optimization of arch bridges". Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111519.
Pełny tekst źródłaCataloged 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.
Elia, Nicola. "Computational methods for multi-objective control". Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10679.
Pełny tekst źródłaPuthiya, Parambath Shameem Ahamed. "New methods for multi-objective learning". Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2322/document.
Pełny tekst źródłaMulti-objective problems arise in many real world scenarios where one has to find an optimal solution considering the trade-off between different competing objectives. Typical examples of multi-objective problems arise in classification, information retrieval, dictionary learning, online learning etc. In this thesis, we study and propose algorithms for multi-objective machine learning problems. We give many interesting examples of multi-objective learning problems which are actively persuaded by the research community to motivate our work. Majority of the state of the art algorithms proposed for multi-objective learning comes under what is called “scalarization method”, an efficient algorithm for solving multi-objective optimization problems. Having motivated our work, we study two multi-objective learning tasks in detail. In the first task, we study the problem of finding the optimal classifier for multivariate performance measures. The problem is studied very actively and recent papers have proposed many algorithms in different classification settings. We study the problem as finding an optimal trade-off between different classification errors, and propose an algorithm based on cost-sensitive classification. In the second task, we study the problem of diverse ranking in information retrieval tasks, in particular recommender systems. We propose an algorithm for diverse ranking making use of the domain specific information, and formulating the problem as a submodular maximization problem for coverage maximization in a weighted similarity graph. Finally, we conclude that scalarization based algorithms works well for multi-objective learning problems. But when considering algorithms for multi-objective learning problems, scalarization need not be the “to go” approach. It is very important to consider the domain specific information and objective functions. We end this thesis by proposing some of the immediate future work, which are currently being experimented, and some of the short term future work which we plan to carry out
Gaudrie, David. "High-Dimensional Bayesian Multi-Objective Optimization". Thesis, Lyon, 2019. https://tel.archives-ouvertes.fr/tel-02356349.
Pełny tekst źródłaThis 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
Shavazipour, Babooshka. "Multi-objective optimisation under deep uncertainty". Doctoral thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/28122.
Pełny tekst źródłaLedéus, 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.
Pełny tekst źródłaVirtual 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.
MUPPIDI, SRINIVAS REDDY. "GENETIC ALGORITHMS FOR MULTI-OBJECTIVE PARTITIONING". University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1080827924.
Pełny tekst źródłaStrano, Giovanni. "Multi-objective optimisation in additive manufacturing". Thesis, University of Exeter, 2012. http://hdl.handle.net/10871/8405.
Pełny tekst źródłaMuppidi, Srinivas R. "Genetic algorithims for multi-objective partitioning". Cincinnati, Ohio : University of Cincinnati, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=ucin1080827924.
Pełny tekst źródłaJiang, Lin. "Robust and Multi-objective Portfolio Selection". Thesis, Curtin University, 2020. http://hdl.handle.net/20.500.11937/82486.
Pełny tekst źródłaAit, Saadi Nadjib. "Multi-objective wireless sensor network deployment". Paris 6, 2010. http://www.theses.fr/2010PA066004.
Pełny tekst źródłaLokman, Banu. "Approaches For Multi-objective Combinatorial Optimization Problems". Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608443/index.pdf.
Pełny tekst źródłaBozkurt, Bilge. "Performance Measurement In Multi Objective Combinatorial Optimization". Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608843/index.pdf.
Pełny tekst źródłaksalan 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
Balibek, Emre. "Multi-objective Approaches To Public Debt Management". Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609305/index.pdf.
Pełny tekst źródłaOzsayin, Burcu. "Multi-objective Combinatorial Optimization Using Evolutionary Algorithms". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12610866/index.pdf.
Pełny tekst źródłaOral, Tugcem. "Multi-objective Path Planning For Virtual Environments". Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614643/index.pdf.
Pełny tekst źródłaLiu, Wei. "A multi-objective approach for RMT design". Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27149.
Pełny tekst źródłaTønder, Lars Solvoll, i Ole-Petter Olsen. "Multi-Objective Neuroevolution in Super Mario Bros". Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-23600.
Pełny tekst źródłaZhou, Xiaojie. "Characterizations of optimality in multi-objective programming". Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=61040.
Pełny tekst źródłaLee, Ji Young. "Multi-objective optimisation using the Bees Algorithm". Thesis, Cardiff University, 2010. http://orca.cf.ac.uk/55028/.
Pełny tekst źródłaRiauke, Jelena. "SPEA2-based safety system multi-objective optimization". Thesis, Loughborough University, 2009. https://dspace.lboro.ac.uk/2134/5514.
Pełny tekst źródłaLee, Michael. "Product modularity : a multi-objective configuration approach". Thesis, Loughborough University, 2010. https://dspace.lboro.ac.uk/2134/6208.
Pełny tekst źródłaRios, Insua David. "Sensitivity analysis in multi-objective decision making". Thesis, University of Leeds, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.236870.
Pełny tekst źródłaLu, Ke. "Evolutionary multi-objective worst-case robust optimisation". Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/109864/.
Pełny tekst źródłaFranklin, Chris. "Multi-objective optimisation using agent-based modelling". Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71788.
Pełny tekst źródłaAFRIKAANSE OPSOMMING: Daar is weinig besluitnemingsprobleme waar slegs 'n enkele waarde of doelwit ter sprake is. Die proses waar twee of meer doelwitte, wat in konflik staan met mekaar, gelyktydig optimiseer word, staan bekend as multi-doelwit optimisering (MOO). 'n Aantal metaheuristieke is al suksesvol aangepas vir MOO. Die doelwit van hierdie studie was om ondersoek in te stel na die lewensvatbaarheid van die toepassing van 'n agent gebasseerde modelerings benadering tot MOO. As toepassingsveld vir hierdie benadering was die (s; S) voorraad probleem gekies en Anylogic was gebruik as model platform. In die model was agente verantwoordelik vir voorraad- en verkope bestuur. Hulle moes onderling met mekaar onderhandel om die optimale bestelling strategiee te verkry. Konsepte soos agentbevrediging, aggressie faktore en herinneringsvermoens is ingestel om die onderhandeling tussen die agente te bewerkstellig. Die resultate het gewys dat die agente oor die vermoe beskik om met goeie strategiee vorendag te kom. Die Pareto fronte wat gegenereer is deur hul voorgestelde strategiee was 'n goeie benadering tot die bekende front. Die benadering was ook suksesvol toegepas op 'n erkende MOO toets-probleem wat bewys het dat dit oor die potensiaal beskik om 'n verskeidenheid van MOO probleme op te los. Toekomstige navorsing kan daarop fokus om hierdie benadering verder te ontwikkel vir meer praktiese toepassings soos komplekse voorsieningskettingstelsels, finnansiele modelle, risiko-analises en ekonomie.
Baruani, Atumbe Jules. "Network engineering using multi-objective evolutionary algorithms". Thesis, Stellenbosch : Stellenbosch University, 2007. http://hdl.handle.net/10019.1/21548.
Pełny tekst źródłaENGLISH ABSTRACT: We use Evolutionary Multi-Objective Optimisation (EMOO) algorithms to optimise objective functions that reflect situations in communication networks. These include functions that optimise Network Engineering (NE) objective functions in core, metro and wireless sensor networks. The main contributions of this thesis are threefold. Routing and Wavelength Assignment (RWA) for IP backbone networks. Routing and Wavelength Assignment (RWA) is a problem that has been widely addressed by the optical research community. A recent interest in this problem has been raised by the need to achieve routing optimisation in the emerging generation multilayer networks where data networks are layered above a Dense Wavelength Division Multiplexing (DWDM) network. We formulate the RWA as both a single and a multi-objective optimisation problem which are solved using a two-step solution where (1) a set of paths are found using genetic optimisation and (2) a graph coloring approach is implemented to assign wavelengths to these paths. The experimental results from both optimisation scenarios reveal the impact of (1) the cost metric used which equivalently defines the fitness function (2) the algorithmic solution adopted and (3) the topology of the network on the performance achieved by the RWA procedure in terms of path quality and wavelength assignment. Optimisation of Arrayed Waveguide Grating (AWG) Metro Networks. An Arrayed Waveguide Grating (AWG) is a device that can be used as a multiplexer or demultiplexer in WDM systems. It can also be used as a drop-and-insert element or even a wavelength router. We take a closer look at how the hardware and software parameters of an AWG can be fine tuned in order to maximise throughput and minimise the delay. We adopt a multi-objective optimisation approach for multi-service AWG-based single hop metro WDM networks. Using a previously proposed multi-objective optimisation model as a benchmark, we propose several EMOO solutions and compare their efficiency by evaluating their impact on the performance achieved by the AWG optimisation process. Simulation reveals that (1) different EMOO algorithms can exhibit different performance patterns and (2) good network planning and operation solutions for a wide range of traffic scenarios can result from a well selected EMOO algorithm. Wireless Sensor Networks (WSNs) Topology (layout) Optimisation. WSNs have been used in a number of application areas to achieve vital functions in situations where humans cannot constantly be available for certain tasks such as in hostile areas like war zones, seismic sensing where continuous inspection and detection are needed, and many other applications such as environment monitoring, military operations and surveillance. Research and practice have shown that there is a need to optimise the topology (layout) of such sensors on the ground because the position on which they land may affect the sensing efficiency. We formulate the problem of layout optimisation as a multi-objective optimisation problem consisting of maximising both the coverage (area) and the lifetime of the wireless sensor network. We propose different algorithmic evolutionary multi-objective methods and compare their performance in terms of Pareto solutions. Simulations reveal that the Pareto solutions found lead to different performance patterns and types of layouts.
AFRIKAANSE OPSOMMING: Ons gebruik ”Evolutionary Multi-Objective Optimisation (EMOO)” algoritmes om teiken funksies, wat egte situasies in kommunikasie netwerke voorstel, te optimiseer. Hierdie sluit funksies in wat ”Network Engineering” teiken funksies in kern, metro en wireless sensor netwerke optimiseer. Die hoof doelwitte van hierdie tesis is dus drievuldig. RWA vir IP backbone netwerke ”Routing and Wavelength Assignment (RWA)” is ’n probleem wat al menigte kere in die optiese navorsings kringe aangespreek is. Belangstelling in hierdie veld het onlangs ontstaan a.g.v. die aanvraag na die optimisering van routering in die opkomende generasie van veelvuldige vlak netwerke waar data netwerke in ’n vlak ho¨er as ’n ”Dense Wavelength Division Multiplexing (DWDM)” netwerk gele is. Ons formuleer die RWA as beide ’n enkele and veelvuldige teiken optimiserings probleem wat opgelos word deur ’n 2-stap oplossing waar (1) ’n stel roetes gevind word deur genetiese optimisering te gebruik en (2) ’n grafiek kleuring benadering geimplementeer word om golflengtes aan hierdie roetes toe te ken. Die eksperimentele resultate van beide optimiserings gevalle vertoon die impak van (1) die koste on wat gebruik word wat die ekwalente fitness funksie definieer , (2) die algoritmiese oplossing wat gebruik word en (3) die topologie van die netwerk op die werkverrigting van die RWA prosedure i.t.v. roete kwaliteit en golflengte toekenning. Optimisering van AWG Metro netwerk ’n ”Arrayed Waveguide Grating (AWG)” is ’n toestel wat gebruik kan word as ’n multipleksor of demultipleksor in WDM sisteme. Dit kan ook gebruik word as ’n val-en-inplaas element of selfs ’n golflengte router. Kennis word ingestel na hoe die hardeware en sagteware parameters van ’n AWG ingestel kan word om die deurset tempo te maksimeer en vertragings te minimiseer. Ons neem ’n multi-teiken optimiserings benadering vir multi diens, AWG gebaseerde, enkel skakel, metro WDM netwerke aan. Deur ’n vooraf voorgestelde multi teiken optimiserings model as ”benchmark” te gebruik, stel ons ’n aantal EMOO oplossings voor en vergelyk ons hul effektiwiteit deur hul impak op die werkverrigting wat deur die AWG optimiserings proses bereik kan word, te vergelyk. Simulasie modelle wys dat (1) verskillende EMOO algoritmes verskillende werkverrigtings patrone kan vertoon en (2) dat goeie netwerk beplanning en werking oplossings vir ’n wye verskeidenheid van verkeer gevalle kan plaasvind a.g.v ’n EMOO algoritme wat reg gekies word. ”Wireless Sensor Network” Topologie Optimisering WSNs is al gebruik om belangrike funksies te verrig in ’n aantal toepassings waar menslike beheer nie konstant beskikbaar is nie, of kan wees nie. Voorbeelde van sulke gevalle is oorlog gebiede, seismiese metings waar aaneenlopende inspeksie en meting nodig is, omgewings meting, militˆere operasies en bewaking. Navorsing en praktiese toepassing het getoon dat daar ’n aanvraag na die optimisering van die topologie van sulke sensors is, gebaseer op gronde van die feit dat die posisie waar die sensor beland, die effektiwiteit van die sensor kan affekteer. Ons formuleer die probleem van uitleg optimisering as ’n veelvuldige vlak optimiserings probleem wat bestaan uit die maksimering van beide die bedekkings area en die leeftyd van die wireless sensor netwerk. Ons stel verskillende algoritmiese, evolutionˆere, veelvuldige vlak oplossings voor en vergelyk hul werkverrigting i.t.v Pareto oplossings. Simulasie modelle wys dat die Pareto oplossings wat gevind word lei na verskillende werkverrigtings patrone en uitleg tipes.
Zuiani, Federico. "Multi-objective optimisation of low-thrust trajectories". Thesis, University of Glasgow, 2015. http://theses.gla.ac.uk/6311/.
Pełny tekst źródłaKirkland, Oliver. "Multi-objective evolutionary algorithms for data clustering". Thesis, University of East Anglia, 2014. https://ueaeprints.uea.ac.uk/51331/.
Pełny tekst źródłaPraharaj, Blake. "AIMOS| Automated Inferential Multi-Objective Optimization System". Thesis, Southern Connecticut State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10249184.
Pełny tekst źródłaMany 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.
Li, Yinjiang. "Robust multi-objective optimisation in electromagnetic design". Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/415498/.
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