Dans la pratique, beaucoup de problèmes d'optimisation sont dynamiques : leur fonction objectif (ou fonction de coût) évolue au cours du temps. L'approche principalement adoptée dans la littérature consiste à adapter des algorithmes d'optimisation statique à l'optimisation dynamique, en compensant leurs défauts intrinsèques. Plutôt que d'emprunter cette voie, déjà largement explorée, l'objectif principal de cette thèse est d'élaborer un algorithme entièrement pensé pour l'optimisation dynamique. La première partie de cette thèse est ainsi consacrée à la mise au point d'un algorithme, qui doit non seulement se démarquer des algorithmes concurrents par son originalité, mais également être plus performant. Dans ce contexte, il s'agit de développer une métaheuristique d'optimisation dynamique. Deux algorithmes à base d'agents, MADO (MultiAgent algorithm for Dynamic Optimization) et MLSDO (Multiple Local Search algorithm for Dynamic Optimization), sont proposés et validés sur les deux principaux jeux de tests existant dans la littérature en optimisation dynamique : MPB (Moving Peaks Benchmark) et GDBG (Generalized Dynamic Benchmark Generator). Les résultats obtenus sur ces jeux de tests montrent l'efficacité des stratégies mises en oeuvre par ces algorithmes, en particulier : MLSDO est classé premier sur sept algorithmes évalués sur GDBG, et deuxième sur seize algorithmes évalués sur MPB. Ensuite, ces algorithmes sont appliqués à des problèmes pratiques en traitement de séquences d'images médicales (segmentation et recalage de séquences ciné-IRM cérébrales). A notre connaissance, ce travail est innovant, en ce sens que l'approche de l'optimisation dynamique n'avait jamais été explorée pour ces problèmes. Les gains de performance obtenus montrent l'intérêt d'utiliser les algorithmes d'optimisation dynamique proposés pour ce type d'applications / Many real-world problems are dynamic, i.e. their objective function (or cost function) changes over time. The main approach used in the literature is to adapt static optimization algorithms to dynamic optimization, compensating for their intrinsic defects. Rather than adopting this approach, already widely investigated, the main goal of this thesis is to develop an algorithm completely designed for dynamic optimization. The first part of this thesis is then devoted to the design of an algorithm, that should not only stand out from competing algorithms for its originality, but also perform better. In this context, our goal is to develop a dynamic optimization metaheuristic. Two agent-based algorithms, MADO (MultiAgent algorithm for Dynamic Optimization) and MLSDO (Multiple Local Search algorithm for Dynamic Optimization), are proposed and validated using the two main benchmarks available in dynamic environments : MPB (Moving Peaks Benchmark) and GDBG (Generalized Dynamic Benchmark Generator). The benchmark results obtained show the efficiency of the proposed algorithms, particularly : MLSDO is ranked at the first place among seven algorithms tested using GDBG, and at the second place among sixteen algorithms tested using MPB. Then, these algorithms are applied to real-world problems in medical image sequence processing (segmentation and registration of brain cine-MRI sequences). To our knowledge, this work is innovative in that the dynamic optimization approach had never been investigated for these problems. The performance gains obtained show the relevance of using the proposed dynamic optimization algorithms for this kind of applications

Les hydrolysats protéiques ont une haute valeur ajoutée pour des secteurs industriels variés, de par leurs propriétés nutritives, fonctionnelles et / ou nutraceutiques. Pour améliorer les propriétés des hydrolysats, l’ultrafiltration est utilisée. Cependant, le manque d’outils de modélisation lié à la complexité des mélanges est un verrou pour une mise en œuvre rationnelle du procédé. Ces travaux ont permis de valider une stratégie de prédiction basée sur des caractéristiques classiques des hydrolysats et un étalonnage expérimental de la membrane d’ultrafiltration. Cette méthode permet de prédire les rendements et enrichissements en fraction(s) ou peptide(s) cible(s), ainsi que la productivité du procédé. Le modèle global de prédiction de l’ultrafiltration obtenu est alors utilisé afin d’optimiser la mise en œuvre de ce procédé. La démarche d’optimisation consiste à maximiser l’enrichissement de fractions ou de peptides cibles en minimisant la consommation d’eau et la durée du procédé / Protein hydrolysates are high added value mixtures for various industrial areas, thanks to their nutritive, functional or nutraceutical properties. To enhance hydrolysates performances, fractionation processes such as ultrafiltration are used. However, the lack of tools to predict ultrafiltration performances is a major bottleneck for a rational implementation of the process. This research thesis work enables to validate a prediction strategy based on classical characteristics of hydrolysates and an experimental calibration of the membrane. Yields and enrichment factors in targeted peptides or fractions during ultrafiltration as well as the productivity of the process can be predicted. This global methodology of performances prediction is then used to optimize the implementation modes of ultrafiltration. The multiobjective optimization approach consists in maximizing the enrichment in targeted peptides or fractions while water consumption and / or process duration is minimized

Cette thèse s’intéresse à une problématique ayant de nombreuses applications pratiques, à savoir la comparaison automatique d’objets et l’évaluation de la similarité. Lorsque les objets sont modélisés par des graphes, ce problème de comparaison automatique d’objets se ramène à un problème d’appariement de graphes, c’est-à-dire, chercher une mise en correspondance entre les sommets des graphes permettant de retrouver le plus grand nombre de caractéristiques communes. Différentes classes existent allant de la plus restrictive à la plus générale. Dans la plus restrictive isomorphisme de (sous-) graphes, il s’agit de chercher un appariement exact entre les sommets des graphes de manière à prouver que les deux graphes possèdent une structure identique ou que l’un d’eux est inclus dans l’autre, un sommet étant apparié avec au plus un sommet. Dans la plus générale (appariement multivoque), l’objectif n’est plus de trouver un appariement exact mais le meilleur appariement, c’est-à-dire, celui qui préserve un maximum de sommets et d’arcs, un sommet pouvant être apparié à un ensemble de sommets. Nous nous intéressons au problème de la recherche du meilleur appariement multivoque, ce problème étant plus général que les problèmes d’appariement restrictifs. Sa résolution est clairement un défi tant par la difficulté du problème que par l’importance de ses applications. Pour relever ce défi, nous proposons d’étudier les capacités de l’optimisation par colonies de fourmis (ACO). Notre étude est menée dans deux contextes : un contexte statique, où le problème est figé, et un contexte dynamique, où les graphes à comparer, les contraintes à respecter ainsi que les critères définissant la qualité des appariements changent régulièrement de sorte que la solution doit être dynamiquement adaptée. Un premier objectif, de cette thèse, est de proposer l’algorithme ACO générique pour la résolution des problèmes d’appariement de graphes. Plusieurs points clés sont étudiés dans cet algorithme, à savoir : l’influence des paramètres sur la qualité des solutions construites, l’influence de la stratégie phéromonale et du facteur heuristique, et l’hybridation avec une technique de recherche locale. Un deuxième objectif est de proposer un algorithme ACO générique pour résoudre des problèmes d’optimisation dynamiques. L’algorithme proposé est appliqué et expérimenté à quelques problèmes dynamiques, à savoir : l’appariement de graphes, le problème du sac à dos multidimensionnel, et le voyageur de commerce / The thesis addresses the problematic of comparing objects and similarity measuring. If objects are described by graphs, so that measuring objects similarity turns into determining graph similarity, i.e., matching graph vertices to identify their common features and their differences. Different classes of graph matching have been proposed going on the most restrictive ones to the most general. In restrictive graph matching (graph or sub-graph isomorphism), the objective is to show graph equivalence or inclusion, a vertex in a graph may be matched with one vertex at most on the other graph. In general graph matching (multivalent matching), the goal is not yet to find an “exact” matching (a matching which preserves all vertices and edges), but to look for a “best” matching (a matching which preserves a maximum number of vertices and edges), a vertex in one graph may be matched with a set of vertices in the other graph. In our work, we consider the problem of searching the best multivalent matching which is a NP-hard optimization problem. More precisely, we propose to investigate the ability if the ant colony optimization meta-heuristic (ACO). Two cases are considered in our study: the static case where the problem remains invariant through time and the dynamic case where graphs to compare constrained to satisfy and the criterions defining matching quality may change over the time, so that solutions must be dynamically adapted to the changes. A first goal of this thesis is to propose a generic ACO algorithm for solving graph matching problems. Different key points, like the pheromonal strategy to be used, the heuristic factor and the use of a local search procedure, are studied. A second goal of this work is to propose a generic ACO algorithm for solving dynamic optimization problems. The proposed algorithm will be applied and experimentally studied on three different dynamic problems: graph matching problem, multi-dimensional knapsack problem and the travelling salesman problem

There is a vast literature on the distortionary effects of the tax-royalty system, while the effects of Production Sharing Contract (PSC) is largely understudied. Moreover, economic studies typically oversimplify the physics of the field and consequently end up with models which do not necessarily fully reflect the reality. In this study, we build a dynamic optimisation model which nests the physics of the reservoir and investigates the distortionary effects of a PSC.

The building sector has become the largest consumer of end use energy in the world, exceeding both the industry and the transportation sectors. Extensive types of energy saving techniques have been developed in the past two decades to mitigate the impact of buildings on the environment. Instead of the conventional active building environmental control approaches that solely rely on the mechanical air conditioning systems, increasing attention is given to the passive and mixed-mode approaches in buildings. This thesis aims to explore the integration of passive cooling approaches and active air conditioning approaches with different dehumidification features, by making effective use of the information on: 1) various dynamic response properties of the building system and mechanical plants, 2) diverse variations of the building boundary conditions over the whole operation process, 3) coupling effect and synergistic influence of the key operational parameters, and 4) numerous parameter conflicts in the integrated active-passive operation. These issues make the proposed integration a complex multifaceted process operation problem. In order to deal with these challenges, a systematic approach is developed by integrating a number of advanced building/system physical models and implementing well established advanced dynamic optimization algorithms. Firstly, a reduced-order model development and calibration framework is presented to generate differential-algebraic equations (DAE) based physical building models, by coupling with the high-order building energy simulations (i.e., EnergyPlus) and implementing MLE+ co-simulation programs in the Matlab platform. The reduced-order building model can describe the dynamic building thermal behaviors and address substantial time delay effects intrinsic in the building heat transfer and moisture migration. A calibration procedure is developed to balance the modelling complexity and the simulation accuracy. By making use of the advanced modeling and simulation features of EnergyPlus, the developed computational platform is able to handle real buildings with various geometric configurations, and offers the potential to cooperate with the dominant commercial building modeling software existing in the current AEC industry. Secondly, the physical model for the active air conditioning systems is developed, which is the other critical part for the dynamic optimization. By introducing and integrating a number of sub-models developed for specific building components, the model is able to specify the dynamic hygrothermal behavior and energy performance of the system under various operating conditions. Two representative air conditioning systems are investigated as the study cases: variable air volume systems (VAV) with mechanical dehumidification, and the desiccant wheel system (DW) with chemical dehumidification. The control variables and constraints representing the system operational characteristics are specified for the dynamic optimization. Thirdly, the integrated active-passive operations are formulated as dynamic optimization problems based on the above building and system physical models. The simultaneous collocation method is used in the solution algorithm to discretize the state and control variables, translating the optimization formulation into a nonlinear program (NLP). After collocation, the translated NLP problems for the daily integrated VAV/DW operation for a case zone have 1605/2181 variables, 1485/2037 equality constraints and 280/248 inequality constraints, respectively. It is found that IPOPT is able to provide the optimal solution within minutes using an 8-core 64-bit desktop, which illustrates the efficiency of the problem formulation. The case study results indicate that the approach can effectively improve the energy performance of the integrated active-passive operations, while maintaining acceptable indoor thermal comfort. Compared to the conventional local control strategies, the optimized strategies lead to remarkable energy saving percentages in different climate conditions: 29.77~48.76% for VAV and 27.85~41.33% for DW. The energy saving is contributed by the improvement of both the passive strategies (around 33%) and active strategies (around 67%). It is found that the thermal comfort constraint defined in the optimization also affects the energy saving. The total optimal energy consumption drops by around 3% if the value of the predicted percentage dissatisfied (PPD) limit is increased by one unit between 5~15%. It is also found that the fitted periodic weather data can lead to similar operation strategies in the dynamic optimization as the realistic data, and therefore can be a reasonable alternative when the more detailed realistic weather data is not available. The method described in the thesis can be generalized to supervise the operation design of building systems with different configurations.

Orientadores: Fernando Jose Von Zuben, Leandro Nunes de Castro / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-14T19:14:33Z (GMT). No. of bitstreams: 1 Franca_FabricioOlivettide_M.pdf: 2824607 bytes, checksum: 3de6277fbb2c8c3460d62b4d81d14f73 (MD5) Previous issue date: 2005 / Resumo: Esta dissertação propõe algoritmos bio-inspirados para a solução de problemas de otimização dinâmica, ou seja, problemas em que a superfície de otimização no espaço de busca sofre variações diversas ao longo do tempo. Com a variação, no tempo, de número, posição e qualidade dos ótimos locais, as técnicas de programação matemática tendem a apresentar uma acentuada degradação de desempenho, pois geralmente foram concebidas para tratar do caso estático. Algoritmos populacionais, controle dinâmico do número de indivíduos na população, estratégias de busca local e uso eficaz de memória são requisitos desejados para o sucesso da otimização dinâmica, sendo contemplados nas propostas de solução implementadas nesta dissertação. Os algoritmos a serem apresentados e comparados com alternativas competitivas presentes na literatura são baseados em funcionalidades e estruturas de processamento de sistemas imunológicos e de colônias de formigas. Pelo fato de considerarem todos os requisitos para uma busca eficaz em ambientes dinâmicos, o desempenho dos algoritmos imuno-inspirados se mostrou superior em todos os critérios considerados para comparação dos resultados dos experimentos. / Abstract: This dissertation proposes bio-inspired algorithms to solve dynamic optimization problems, i.e., problems for which the optimization surface on the search space suffers several changes over time. With such variation of number, position and quality of local optima, mathematical programming techniques may present degradation of performance, because they were usually conceived to deal with static problems. Population-based algorithms, dynamic control of the population size, local search strategies and an efficient memory usage are desirable requirements to a proper treatment of dynamic optimization problems, thus being incorporated into the solution strategies implemented here. The algorithms to be presented, and compared with competitive alternatives available in the literature, are based on functionalities and processing structures of immune systems and ant colonies. Due to the capability of incorporating all the requirements for an efficient search on dynamic environments, the immune-inspired approaches overcome the others in all the performance criteria adopted to evaluate the experimental results. / Mestrado / Engenharia de Computação / Mestre em Engenharia Elétrica

A mobile ad hoc network (MANET) is an infrastructure-less multi-hop network where each node communicates with other nodes directly or indirectly through intermediate nodes. Thus, all nodes in a MANET basically function as mobile routers participating in some routing protocol required for deciding and maintaining the routes. Since MANETs are infrastructure-less, self-organizing, rapidly deployable wireless networks, they are highly suitable for applications such as military tactical operations, search and rescue missions, disaster relief operations, and target tracking. Building such ad-hoc networks poses a significant technical challenge because of energy constraints and specifically in relation to the application of wireless network protocols. As a result of its highly dynamic and distributed nature, the routing layer within the wireless network protocol stack, presents one of the key technical challenges in MANETs. In particular, energy efficient routing may be the most important design criterion for MANETs since mobile nodes are powered by batteries with limited capacity and variable recharge frequency, according to application demand. In order to conserve power it is essential that a routing protocol be designed to guarantee data delivery even should most of the nodes be asleep and not forwarding packets to other nodes. Load distribution constitutes another important approach to the optimisation of active communication energy. Load distribution enables the maximisation of the network lifetime by facilitating the avoidance of over-utilised nodes when a route is in the process of being selected. Routing algorithms for mobile networks that attempt to optimise routes while at- tempting to retain a small message overhead and maximise the network lifetime has been put forward. However certain of these routing protocols have proved to have a negative impact on node and network lives by inadvertently over-utilising the energy resources of a small set of nodes in favour of others. The conservation of power and careful sharing of the cost of routing packets would ensure an increase in both node and network lifetimes. This thesis proposes simultaneously, by using an ant colony optimisation (ACO) approach, to optimise five power-aware metrics that do result in energy-efficient routes and also to maximise the MANET's lifetime while taking into consideration a realistic mobility model. By using ACO algorithms a set of optimal solutions - the Pareto-optimal set - is found. This thesis proposes five algorithms to solve the multi-objective problem in the routing domain. The first two algorithms, namely, the energy e±ciency for a mobile network using a multi-objective, ant colony optimisation, multi-pheromone (EEMACOMP) algorithm and the energy efficiency for a mobile network using a multi-objective, ant colony optimisation, multi-heuristic (EEMACOMH) algorithm are both adaptations of multi-objective, ant colony optimisation algorithms (MOACO) which are based on the ant colony system (ACS) algorithm. The new algorithms are constructive which means that in every iteration, every ant builds a complete solution. In order to guide the transition from one state to another, the algorithms use pheromone and heuristic information. The next two algorithms, namely, the energy efficiency for a mobile network using a multi-objective, MAX-MIN ant system optimisation, multi-pheromone (EEMMASMP) algorithm and the energy efficiency for a mobile network using a multi-objective, MAX- MIN ant system optimisation, multi-heuristic (EEMMASMH) algorithm, both solve the above multi-objective problem by using an adaptation of the MAX-MIN ant system optimisation algorithm. The last algorithm implemented, namely, the energy efficiency for a mobile network using a multi-objective, ant colony optimisation, multi-colony (EEMACOMC) algorithm uses a multiple colony ACO algorithm. From the experimental results the final conclusions may be summarised as follows:<ul><li> Ant colony, multi-objective optimisation algorithms are suitable for mobile ad hoc networks. These algorithms allow for high adaptation to frequent changes in the topology of the network. </li><li> All five algorithms yielded substantially better results than the non-dominated sorting genetic algorithm (NSGA-II) in terms of the quality of the solution. </li><li> All the results prove that the EEMACOMP outperforms the other four ACO algorithms as well as the NSGA-II algorithm in terms of the number of solutions, closeness to the true Pareto front and diversity. </li></ul> / Thesis (PhD)--University of Pretoria, 2010. / Computer Science / unrestricted

This thesis presents the work of two significant projects. In the first project, a suite of benchmark problems for grid energy management are presented which demonstrate several issues characteristic to the dynamic optimization of these systems. These benchmark problems include load following, cogeneration, tri-generation, and energy storage, and each one assumes perfect foresight of the entire time horizon. The Gekko Python package for dynamic optimization is introduced and two different solution methods are discussed and applied to solving these benchmarks. The simultaneous solve mode out-performs the sequential solve mode in each benchmark problem across a wide range of time horizons with increasing resolution, demonstrating the ability of the simultaneous mode to handle many degrees of freedom across a range of problems of increasing difficulty. In the second project, combined optimization of propulsion system design, flight trajectory planning and battery mass optimization is applied to solar-regenerative high-altitude long-endurance (SR-HALE) aircraft through a sequential iterative approach. This combined optimization approach yields an increase of 20.2% in the end-of-day energy available on the winter solstice at 35°N latitude, resulting in an increase in flight time of 2.36 hours. The optimized flight path is obtained by using nonlinear model predictive control to solve flight and energy system dynamics over a 24 hour period with a 15 second time resolution. The optimization objective is to maximize the total energy in the system while flying a station-keeping mission, staying within a 3 km radius and above 60,000 ft. The propulsion system design optimization minimizes the total energy required to fly the optimal path. It uses a combination of blade element momentum theory, blade composite structures, empirical motor and motor controller mass data, as well as a first order motor performance model. The battery optimization seeks to optimally size the battery for a circular orbit. Fixed point iteration between these optimization frameworks yields a flight path and propulsion system that slightly decreases solar capture, but significantly decreases power expended. Fully coupling the trajectory and design optimizations with this level of accuracy is infeasible with current computing resources. These efforts show the benefits of combining design and trajectory optimization to enable the feasibility of SR-HALE flight.

<p>The electric arc furnace (EAF) is a highly energy intensive process used to convert scrap metal into molten steel. The aim of this research is to develop a dynamic model of an industrial EAF process, and investigate its application for optimal EAF operation. This work has three main contributions; the first contribution is developing a model largely based on MacRosty and Swartz (2005) to meet the operation of a new industrial partner (ArcelorMittal Contrecoeur Ouest, Quebec, Canada). The second contribution is carrying out sensitivity analyses to investigate the effect of the scrap components on the EAF process. Finally, the third contribution includes the development of a constrained multi-rate extended Kalman filter (EKF) to infer the states of the system from the measurements provided by the plant.</p> <p>A multi-zone model is developed and discussed in detail. Heat and mass transfer relationships are considered. Chemical equilibrium is assumed in two of the zones and calculated through the minimization of the Gibbs free energy. The most sensitive parameters are identified and estimated using plant measurements. The model is then validated against plant data and has shown a reasonable level of accuracy.</p> <p>Local differential sensitivity analysis is performed to investigate the effect of scrap components on the EAF operation. Iron was found to have the greatest effect amongst the components present. Then, the optimal operation of the furnace is determined through economic optimization. In this case, the trade-off between electrical and chemical energy is determined in order to maximize the profit. Different scenarios are considered that include price variation in electricity, methane and oxygen.</p> <p>A constrained multi-rate EKF is implemented in order to estimate the states of the system using plant measurements. The EKF showed high performance in tracking the true states of the process, even in the presence of a parametric plant-model mismatch.</p> / Master of Applied Science (MASc)

Approved for public release; distribution in unlimited. / Spray cooling is a promising means of dissipating large steady state heat fluxes in high density power and electronic systems, such as thermophotovoltaic systems. The present study reports on the effectiveness of spray cooling in removing heat fluxes as high as 220 W/cm2. An experiment was designed to determine how the parameters of spray volumetric flow rate and droplet size influence the heat removal capacity of such a system. A series of commercially available nozzles were used to generate full cone water spray patterns encompassing a range of volumetric flow rates (3.79 to 42.32 L/h) and droplet Sauter mean diameters (17.4 to 35.5 micrometers). The non-flooded regime of spray cooling was studied, in which liquid spreading on the heater surface following droplet impact is the key phenomenon that determines the heat transfer rate. The experimental data established a direct proportionality of the heat flux with spray flow rate, and an inverse dependence on the droplet diameter. A correlation of the data was developed to predict heat flux as a function of the studied parameters over the range of values tested in this. / Lieutenant, United States Navy