A Inteligência de Enxame (Swarm Intelligence) é uma área de estudos que busca soluções para problemas de otimização utilizando-se de técnicas computacionais inspiradas no comportamento social emergente encontrado na biologia. A metaheurística Particle Swarm Optimization (PSO) é relativamente nova e foi inspirada no comportamento social de bandos de pássaros. PSO tem apresentado bons resultados em alguns trabalhos recentes de otimização discreta, apesar de ter sido concebido originalmente para a otimização de problemas contínuos. Este trabalho trata o Problema de Atribuição de Tarefas - Task Assignment Problem (TAP), e apresenta uma aplicação: o problema de alocação de táxis e clientes, cujo objetivo da otimização está em minimizar a distância percorrida pela frota. Primeiramente, o problema é resolvido em um cenário estático, com duas versões do PSO discreto: a primeira abordagem é baseada em codificação binária e a segunda utiliza permutações para codificar as soluções. Os resultados obtidos mostram que a segunda abordagem é superior à primeira em termos de qualidade das soluções e tempo computacional, e é capaz de encontrar as soluções ótimas para o problema nas instâncias para as quais os valores ótimos são conhecidos. A partir disto, o algoritmo é adaptado para a otimização do problema em um ambiente dinâmico, com a aplicação de diferentes estratégias de resposta às mudanças. Os novos resultados mostram que a combinação de algumas abordagens habilita o algoritmo PSO a obter boas soluções ao longo da ocorrência de mudanças nas variáveis de decisão problema, em todas as instâncias testadas, com diferentes tamanhos e escalas de mudança. / Swarm Intelligence searches for solutions to optimization problems using computational techniques inspired in the emerging social behavior found in biology. The metaheuristic Particle Swarm Optimization (PSO) is relatively new and can be considered a metaphor of bird flocks. PSO has shown good results in some recent works of discrete optimization, despite it has been originally designed for continuous optimization problems. This paper deals with the Task Assignment Problem (TAP), and presents an application: the optimization problem of allocation of taxis and customers, whose goal is to minimize the distance traveled by the fleet. The problem is solved in a static scenario with two versions of the discrete PSO: the first approach that is based on a binary codification and the second one which uses permutations to encode the solution. The obtained results show that the second approach is superior than the first one in terms of quality of the solutions and computational time, and it is capable of achieving the known optimal values in the tested instances of the problem. From this, the algorithm is adapted for the optimization of the problem in a dynamic environment, with the application of different strategies to respond to changes. The new results show that some combination of approaches enables the PSO algorithm to achieve good solutions along the occurrence of changes in decision variables problem, in all instances tested, with different sizes and scales of change.

Este trabalho insere-se na área de pesquisa de sistemas de transporte inteligente e mobilidade urbana buscando um cenário onde a infraestrutura rodoviária é capaz de monitorar um tráfego exclusivo de veículos inteligentes que não dependem de motoristas para serem guiados. A principal contribuição do trabalho é o desenvolvimento de uma solução matemática para otimizar o gerenciamento microscópico centralizado do tráfego de veículos inteligentes em trechos (segmentos) de rodovia. Para isto é apresentado um modelo de otimização baseado em Programação Linear Inteira Mista (MILP), que determina um plano ótimo de trajetórias individuais dos veículos em uma evolução de tráfego. O objetivo é reduzir o tempo de viagem individualmente e assegurar fluidez do tráfego. O modelo considera componentes essenciais do sistema dinâmico viário como topografia da pista, regras de trânsito e a curva de aceleração máxima de cada veículo. São contempladas várias situações de tráfego, tais como ultrapassagens, inclinação na pista, obstáculos e redutores de velocidade. Os resultados indicaram uma média de 20,5 segundos para o cálculo de um cenário com 6 veículos e 11 intervalos de tempo. Como o modelo MILP não tem solução em tempo computacional aceitável para aplicação real, também é proposto um algoritmo de simulação baseado em heurísticas o qual busca reduzir esse tempo de cálculo em detrimento da otimalidade da solução. O algoritmo reproduz o comportamento de um motorista que tenta manter sempre um valor de velocidade escolhido previamente, e por isso é forçado a ultrapassar outros veículos quando obstruído ao longo do trajeto. O resultado do algoritmo tem importância adicional, pois serve de referência para resolver o problema da prioridade nas ultrapassagens. Também são propostos novos indicadores para a avaliação microscópica de qualidade de tráfego. Finalmente, são apresentados resultados de testes em simulações a fim de avaliar e validar o modelo e o algoritmo. / This work focus on the research area of intelligent transportation systems and urban mobility. It considers a scenario where the roadside infrastructure is capable of monitoring traffic composed by 100% of intelligent vehicles that do not rely on drivers to be guided. The main contribution of this work is the development of a mathematical solution to optimize the centralized management of intelligent microscopic vehicular traffic in parts (segments) of highway. Therefore an optimization model based on Mixed Integer Linear Programming (MILP) is presented. The model determines individual trajectories plans of vehicles in a traffic evolution. The objective is to reduce the travel time individually and ensure traffic flow. The model considers essential components of the dynamic highway system, such as, topography of the lane, traffic rules and acceleration curve for each vehicle. Many traffic situations are considered, such as, overtaking, slopes, obstacles and speed reducers. The results indicated an average of 20.5 seconds to calculate a scenario with 6 vehicles and 11 time intervals. As the MILP model has no solution in acceptable computational time for real application, it is proposed an algorithm based on heuristic simulation which seeks to reduce the computation time at the expense of optimality of the solution. The algorithm reproduces the behavior of a driver who always tries to maintain a preselected velocity value, and is therefore forced to overtake other vehicles when blocked along the path. The result of the algorithm has additional importance because it serves as a reference for solving the problem of priority when overtaking. New indicators for microscopic evaluation of quality traffic are also proposed. Finally, test results are presented on simulations to evaluate and validate the model and algorithm.

Esta tese apresenta um novo protocolo de roteamento voltado para as Redes Tolerantes a Atrasos que exibem comportamentos complexos e dinâmicos. O protocolo proposto chama-se Cultural GrAnt (do inglês Cultural Greedy Ant) uma vez que este utiliza um sistema híbrido composto por um Algoritmo Cultural (AC) e uma versão gulosa da meta-heurística de Otimização por Colônia de Formigas (ACO). No Cultural GrAnt, o ACO representa o espaço populacional de um AC e utiliza uma regra de transição gulosa de modo a intensificar bons caminhos já encontrados ou explorar novos caminhos através da seleção, dentre um conjunto de candidatos, dos nós encaminhadores de mensagens mais promissores. A principal motivação para o uso do ACO é tirar proveito da sua busca baseada em população de indivíduos e da adaptação da sua estrutura de aprendizado. O AC obtém informações durante o processo evolucionário e as utiliza para guiar a população e, então, acelerar o aprendizado enquanto provê soluções mais eficientes. Considerando informações de funções heurísticas, concentração de feromônio e conhecimentos armazenados no espaço de crenças do AC, o protocolo Cultural GrAnt inclui três módulos: roteamento; escalonamento; e gerenciamento de buffer. Esse é o primeiro protocolo de roteamento que emprega ACO e AC de modo a: inferir os melhores encaminhadores de mensagens através de informações oportunistas sobre a conectividade social entre os nós; determinar os melhores caminhos que uma mensagem deve seguir para eventualmente alcançar o seu destino final, enquanto limita o número de replicações e descartes de mensagens na rede; determinar a ordem de escalonamento das mensagens; e gerenciar o espaço de armazenamento do buffer dos nós. O protocolo Cultural GrAnt é comparado com os protocolos Epidêmico e PROPHET em dois cenários de mobilidade distintos: um modelo de movimento baseado em atividades, onde simula-se o dia-a-dia de pessoas em suas atividades de trabalho, lazer e descanso; e um modelo de movimento baseado em comunidades de pessoas. Os resultados de simulações obtidos através do simulador ONE mostram que em ambos os cenários, o protocolo Cultural GrAnt alcança uma taxa mais alta de entrega de mensagens, uma replicação menor de mensagens e um número menor de mensagens descartadas se comparado com os protocolos Epidêmico e PROPHET. / This work presents a new routing protocol for complex and dynamic Delay Tolerant Networks (DTN). The proposed protocol is called Cultural GrAnt (Greedy Ant), as it uses a hybrid system composed of a Cultural Algorithm and a greedy version of the Ant Colony Optimization (ACO) metaheuristic. In Cultural GrAnt, ACO represents the population space of the cultural algorithm and uses a greedy transition rule to either exploit previously found good paths or explore new paths by selecting, among a set of candidates, the most promising message forwarders. The main motivation for using ACO is to take advantage of its population-based search and adaptive learning framework. Conversely, CA gathers information during the evolutionary process and uses it to guide the population and thus accelerate learning while providing more efficient solutions. Considering information from heuristic functions, pheromone concentration, and knowledge stored in the CA belief space, the Cultural GrAnt protocol includes three modules: routing, scheduling, and buffer management. To the best of our knowledge, this is the first routing protocol that employs both ACO and CA to infer the best message forwarders using opportunistic information about social connectivity between nodes, determine the best paths a message must follow to eventually reach its destination while limiting message replications and droppings, and perform message transmission scheduling and buffer space management. Cultural GrAnt is compared to the Epidemic and PROPHET protocols in two different mobility scenarios: an activity-based movement model, which simulates the daily lives of people in their work, leisure and rest activities; and a community-based movement model. Simulation results obtained by the ONE simulator show that, in both scenarios, Cultural GrAnt achieves a higher delivery ratio, lower message replication, and fewer dropped messages than Epidemic and PROPHET.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2015-01-27T14:37:37Z No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertação - Jonathan Cardoso Silva - 2014.pdf: 3627934 bytes, checksum: 4b576bd42a95d94493a78b1e54c2d64a (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-01-28T12:39:13Z (GMT) No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertação - Jonathan Cardoso Silva - 2014.pdf: 3627934 bytes, checksum: 4b576bd42a95d94493a78b1e54c2d64a (MD5) / Made available in DSpace on 2015-01-28T12:39:13Z (GMT). No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertação - Jonathan Cardoso Silva - 2014.pdf: 3627934 bytes, checksum: 4b576bd42a95d94493a78b1e54c2d64a (MD5) Previous issue date: 2014-02-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The operation planning of hydrothermal systems is a complex, dynamic, stochastic, nonlinear and interconnected problem. In this study, we consider that this problem must tackle two objectives simultaneously: minimize thermoelectric generation (by maximizing the use of hydroelectric plants) and maximize water reservoirs’ level of hydroelectric plants. This dissertation presents the application of some multiobjective meta-heuristics, using a set of eight actual plants from Brazilian interconnected system in three periods of medium-term planning. The algorithms used were of two types: those based on particle swarms (MOPSO , MOPSO-TVAC , SMPSO, MOPSO-CDR and MOPSO-DFR) and evolutionary algorithms (SPEA2 and MOEAD/DRA). The results from previous studies, made with single objective techniques, were inserted in the initial population of the algorithms and compared with those simulations with normal initialization. We observed that MOPSO-CDR outperformed the other algorithms in the test scenarios while, in some cases, MOPSO has also generated competitive results. / O problema do planejamento da operação de sistemas hidrotérmicos é complexo, dinâmico, estocástico, interconectado e não linear. Este problema é tratado de modo atender a dois objetivos simultaneamente: maximizar a geração elétrica nas usinas hidrelétricas (ou minimizar o custo com a complementação da geração por termelétricas) e maximizar o nível dos reservatórios de água das hidrelétricas. Este trabalho apresenta a aplicação de algumas meta-heurísticas multiobjetivo a este problema, utilizando um conjunto de oito usinas reais do Sistema Interligado Nacional em três períodos de planejamento de médio prazo. Os algoritmos utilizados foram de dois tipos: os baseadas em enxames de partículas (MOPSO, MOPSO-TVAC,SMPSO, MOPSO-CDR e MOPSO-DFR) e os algoritmos evolucionários (SPEA2 e MOEAD/DRA). Foram realizados testes com a inserção de resultados de estudos anteriores com técnicas de único objetivo na população inicial dos algoritmos e comparados com os testes com inicialização normal. Observou-se que o algoritmo MOPSO-CDR obtém os melhores resultados nos cenários de testes utilizados, competindo em alguns casos com os resultados do MOPSO.

The maintenance schedule of generators in power plants needs to match the electricity demand and needs to ensure the reliability of the power plant at a minimum cost of operation. In this study, a comparison is made between the modified generator maintenance scheduling model and the classic generator maintenance scheduling model using the reliability objective functions. Both models are applied to a 21-unit test system, and the results show that the modified generator maintenance scheduling model gives better and more reliable solutions than the regular generator maintenance scheduling model. The better results of the modified generator maintenance scheduling model are due the modified and additional constraints in the modified generator maintenance scheduling model. Due to the reliable results of the modified generator maintenance scheduling model, a robust model is formulated using the economic cost objective function. The model includes modified crew and maintenance window constraints, with some additional constraints such as the relationship constraints among the variables. To illustrate the robustness of the formulated GMS model, the maintenance of the Arnot power plant in South Africa is scheduled with open-loop and closed-loop controllers. Both controllers satisfy all the constraints but the closed-loop results are better than the open-loop results. AFRIKAANS : Die onderhoudskedule vir kragopwekkers (OSK) in kragstasies moet kan voorsien in die vraag na elektrisiteit en moet die betroubaarheid van die kragstasie teen ’n minimum operasiekoste verseker. In hierdie studie word die betroubaarheidsdoelwitfunksie gebruik om ’n gewysigde onderhoudskeduleringsmodel vir kragopwekkers te vergelyk met die konvensionele onderhoudskeduleringsmodel. Beide modelle word toegepas op 'n 21-eenheid-toetsstelsel, en die resultate toon dat die gewysigde model ’n beter en meer betroubare oplossing bied as die konvensionele model. Die beter resultate van die gewysigde model is die gevolg van die gewysigde en bykomende beperkings in die gewysigde model. As gevolg van die betroubare resultate van die gewysigde onderhoudskeduleringsmodel word die koste-ekonomie-doelwitfunksie gebruik om ’n robuuste model te formuleer. Die model sluit gewysigde bemanning- en onderhoudvensterbeperkings in, met ’n paar bykomende beperkings soos die verhoudingsbeperkings tussen die veranderlikes. Om die robuustheid van die geformuleerde OSK-model te illustreer word die instandhouding van die Arnot kragstasie in Suid-Afrika geskeduleer met oop- en geslotelus-beheerders. Beide beheerders voldoen aan al die beperkinge, maar die geslotelusresultate is beter as die ooplusresultate. / Dissertation (MSc)--University of Pretoria, 2011. / Electrical, Electronic and Computer Engineering / Unrestricted

This diploma thesis deals with the path planning by swarm intelligence. In the theoretical part it describes the best known methods of swarm intelligence (Ant Colony Optimization, Bee Swarm Optimization, Firefly Swarm Optimization and Particle Swarm Optimization) and their application for path planning. In the practical part particle swarm optimization is selected for the design and implementation of path planning in the C#.

In this thesis, we use evolutionary robotics techniques to automatically design and synthesise<p>behaviour for groups of simulated and real robots. Our contribution will be on<p>the design of non-trivial individual and collective behaviour; decisions about solitary or<p>social behaviour will be temporal and they will be interdependent with communicative<p>acts. In particular, we study time-based decision-making in a social context: how the<p>experiences of robots unfold in time and how these experiences influence their interaction<p>with the rest of the group. We propose three experiments based on non-trivial real-world<p>cooperative scenarios. First, we study social cooperative categorisation; signalling and<p>communication evolve in a task where the cooperation among robots is not a priori required.<p>The communication and categorisation skills of the robots are co-evolved from<p>scratch, and the emerging time-dependent individual and social behaviour are successfully<p>tested on real robots. Second, we show on real hardware evidence of the success of evolved<p>neuro-controllers when controlling two autonomous robots that have to grip each other<p>(autonomously self-assemble). Our experiment constitutes the first fully evolved approach<p>on such a task that requires sophisticated and fine sensory-motor coordination, and it<p>highlights the minimal conditions to achieve assembly in autonomous robots by reducing<p>the assumptions a priori made by the experimenter to a functional minimum. Third, we<p>present the first work in the literature to deal with the design of homogeneous control<p>mechanisms for morphologically heterogeneous robots, that is, robots that do not share<p>the same hardware characteristics. We show how artificial evolution designs individual<p>behaviours and communication protocols that allow the cooperation between robots of<p>different types, by using dynamical neural networks that specialise on-line, depending on<p>the nature of the morphology of each robot. The experiments briefly described above<p>contribute to the advancement of the state of the art in evolving neuro-controllers for<p>collective robotics both from an application-oriented, engineering point of view, as well as<p>from a more theoretical point of view. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Several of our technological breakthroughs are influenced by types of behavior and structures developed in the natural world, including the emulation of swarm in- telligence and the engineering of artificial synapses that function like the human mind. Much like these breakthroughs, this report examines emerging behaviors across swarms of non-communicating, adaptive units that evade obstacles while find- ing a path, to present a swarming algorithm premised on a class of local rule sets re- sulting in a Unmanned Aerial Vehicle (UAV) group navigating together as a unified swarm. Primarily, this method’s important quality is that its rules are local in nature. Thus, the exponential calculations which can be supposed with growing number of drones, their states, and potential tasks are remedied. To this extent, the study tests the algorithmic rules in experiments to replicate the desired behavior in a bounded virtual space filled with simulated units. Simultaneously, in the adaptation of natural flocking rules the study also introduces the rule sets for goal seeking and uncertainty evasion. In effect, the study succeeds in reaching and displaying the desired goals even as the units avoid unknown before flight obstacles and inter-unit collisions with- out the need for a global centralized command nor a leader based hierarchical system.

Indiana University-Purdue University Indianapolis (IUPUI) / As our population ages, treating bone and joint ailments is becoming increasingly important. Both osteoporosis, a bone disease characterized by a decreased density of mineral in bone, and osteoarthritis, a joint disease characterized by the degeneration of cartilage on the ends of bones, are major causes of decreased movement ability and increased pain. To combat these diseases, many treatments are offered, including drugs and exercise, and much biomedical research is being conducted. However, how can we get the most out of the research we perform and the treatment we do have? One approach is through computational analysis and mathematical modeling. In this thesis, quantitative methods of analysis are applied in different ways to two systems: osteoporosis and osteoarthritis. A mouse model simulating osteoporosis is treated with salubrinal and knee loading. The bone and cell data is used to formulate a system of differential equations to model the response of bone to each treatment. Using Particle Swarm Optimization, optimal treatment regimens are found, including a consideration of budgetary constraints. Additionally, an in vitro model of osteoarthritis in chondrocytes receives RNA silencing of Lrp5. Microarray analysis of gene expression is used to further elucidate the mode of regulation of ADAMTS5, an aggrecanase associated with cartilage degradation, by Lrp5, including the development of a mathematical model. The math model of osteoporosis reveals a quick response to salubrinal and a delayed but substantial response to knee loading. Consideration of cost effectiveness showed that as budgetary constraints increased, treatment did not start until later. The quantitative analysis of ADAMTS5 regulation suggested the involvement of IL1B and p38 MAPK. This research demonstrates the application of quantitative methods to further the usefulness of biomedical and biomolecular research into treatment and signaling pathways. Further work using these techniques can help uncover a bigger picture of osteoarthritis's mode of action and ideal treatment regimens for osteoporosis.

Indiana University-Purdue University Indianapolis (IUPUI) / A gradient free function optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized in parameter identification of the electrochemical model of a Lithium-Ion battery having a LiCoO2 chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24-hr over-discharged battery, and over-charged battery. It is important for a battery management system to have these parameters changes fully captured in a bank of battery models that can be used to monitor battery conditions in real time. In this work, PSO methodology has been used to identify four electrochemical model parameters that exhibit significant variations under severe operating conditions. The identified battery models were validated by comparing the model output voltage with the experimental output voltage for the stated operating conditions. These identified conditions of the battery were then used to monitor condition of the battery that can aid the battery management system (BMS) in improving overall performance. An adaptive estimation technique, namely multiple model adaptive estimation (MMAE) method, was implemented for this purpose. In this estimation algorithm, all the identified models were simulated for a battery current input profile extracted from the hybrid pulse power characterization (HPPC) cycle simulation of a hybrid electric vehicle (HEV). A partial differential algebraic equation (PDAE) observer was utilized to obtain the estimated voltage, which was used to generate the residuals. Analysis of these residuals through MMAE provided the probability of matching the current battery operating condition to that of one of the identified models. Simulation results show that the proposed model based method offered an accurate and effective fault diagnosis of the battery conditions. This type of fault diagnosis, which is based on the models capturing true physics of the battery electrochemistry, can lead to a more accurate and robust battery fault diagnosis and help BMS take appropriate steps to prevent battery operation in any of the stated severe or abusive conditions.