The problem of scheduling a parallel program presented by a weighted directed acyclic graph (DAG) to the set of homogeneous processors for minimizing the completion time of the program has been extensively studied as academic optimization problem which occurs in optimizing the execution time of parallel algorithm with parallel computer.In this paper, we propose an application of the Ant Colony Optimization (ACO) to a multiprocessor scheduling problem (MPSP). In the MPSP, no preemption is allowed and each operation demands a setup time on the machines. The problem seeks to compose a schedule that minimizes the total completion time.We therefore rely on heuristics to find solutions since solution methods are not feasible for most problems as such. This novel heuristic searching approach to the multiprocessor based on the ACO algorithm a collection of agents cooperate to effectively explore the search space.A computational experiment is conducted on a suit of benchmark application. By comparing our algorithm result obtained to that of previous heuristic algorithm, it is evince that the ACO algorithm exhibits competitive performance with small error ratio.

Vehicular Ad hoc Networks (VANET) exhibit highly dynamic behavior with high mobility and random network topologies. The performance of Transmission Control Protocols in such wireless ad hoc networks is plagued by a number of problems: frequent link failures, scalability, multi-hop data transmission and data loss. To address these VANET routing issues, I have used the ideas from swarm intelligence. The Ant Colony Optimization (ACO), which is a branch of swarm intelligence, is the main source of my inspiration. I have designed an ant-based routing algorithm which addresses routing issues prevalent in VANETs: adaptivity, robustness and scalability. One attractive feature of ACO is that they provide multiple routes from source to destination, resulting in more robust network. In this work, together with ACO, I have used the ideas from zone routing protocols to develop my algorithm: Mobility Aware Zone based Ant Colony Optimization Routing for VANET that exhibits locality and scalability.

Network resiliency pertains to the vulnerability of telecommunication networks in the case of failures and malicious attacks. With the increasing capacity catering of network for the booming multi-services in Next Generation Networks (NGNs), reducing recovery time and improving capacity efficiency while providing high quality and resiliency of services has become increasingly important for the future network development. Providing network resiliency means to rapidly and accurately reroute the traffic via diversely routed spare capacity in the network when a failure takes down links or nodes in the working path. Planning and optimization for NGNs require an efficient algorithm for spare capacity allocation (SCA) that assures restorability with a minimum of total capacity. This dissertation aims to understand and advance the state of knowledge on spare capacity allocation in network resiliency for telecommunication core networks. Optimal network resiliency design for restorability requires considering: network topology, working and protection paths routing and spare capacity allocation. Restorable networks should be highly efficient in terms of total capacity required for restorability and be able to support any target level of restorability. The SCA strategy is to decide how much spare capacity should be reserved on links and to pre-plan protection paths to protect traffic from a set of failures. This optimal capacity allocation problem for survivable routing is known as NP-complete. To expose the problem structure, we propose a model of the SCA problem using a matrix-based framework, named Distributed Resilience Matrix (DRM) to identify the dependencies between the working and protection capacities associated with each pair of links and also to capture the local capacity usage information in a distributed control environment. In addition, we introduce a novel ant-based heuristic algorithm, called Friend-or-Foe Resilient (FoF-R) ant-based routing algorithm to find the optimal protection cycle (i.e., two node-disjoint paths between a source-destination node pair) and explore the sharing ability among protection paths using a capacity headroom-dependent attraction and repulsion function. Simulation results based on the OMNeT++ and AMPL/CPLEX tools show that the FoF-R scheme with the DRM structure is a promising approach to solving the SCA problem for survivable routing and it gives a good trade off between solution optimality and computation speed. Furthermore, for the SCA studies of survivable networks, it is also important to be able to differentiate between network topologies by means of a robust numerical measure that indicates the level of immunity of these topologies to failures of their nodes and links. Ideally, such a measure should be sensitive to the existence of nodes or links, which are more important than others, for example, if their failure causes the network’s disintegration. Another contribution in this dissertation is to introduce an algebraic connectivity metric, adopted from the spectral graph theory, namely the 2nd smallest eigenvalue of the Laplacian matrix of the network topology, instead of the average nodal degree, to characterize network robustness in studies of the SCA problem. Extensive simulation studies confirm that this metric is a more informative parameter than the average nodal degree for characterizing network topologies in network resiliency studies.

This thesis presents an application of the Ant Colony algorithm for optimizing filter placement and sizing on a radial distribution system to reduce power losses and keep the effective harmonic voltage values and the total harmonic distortion (THD) within prescribed limits. First, a harmonic load flow (HLF) algorithm is performed to demonstrate the effect of harmonic sources on total power loss. Then the Ant Colony algorithm is used in conjunction with HLF to place a selection of filter sizes available at each possible location so that both power loss and THD are minimized. As a result the optimal adjustment of location and size of the filter are determined. Results of computational experiments on standard test systems are presented to demonstrate improvement and effectiveness of using the filters at the optimal location. The methodology used can be easily extended to different distribution network configurations. / Master Thesis

Vehicular Ad hoc Networks (VANET) exhibit highly dynamic behavior with high mobility and random network topologies. The performance of Transmission Control Protocols in such wireless ad hoc networks is plagued by a number of problems: frequent link failures, scalability, multi-hop data transmission and data loss. To address these VANET routing issues, I have used the ideas from swarm intelligence. The Ant Colony Optimization (ACO), which is a branch of swarm intelligence, is the main source of my inspiration. I have designed an ant-based routing algorithm which addresses routing issues prevalent in VANETs: adaptivity, robustness and scalability. One attractive feature of ACO is that they provide multiple routes from source to destination, resulting in more robust network. In this work, together with ACO, I have used the ideas from zone routing protocols to develop my algorithm: Mobility Aware Zone based Ant Colony Optimization Routing for VANET that exhibits locality and scalability.

Combinatorial optimisation problems (COPs) pervade human society: scheduling, design, layout, distribution, timetabling, resource allocation and project management all feature problems where the solution is some combination of elements, the overall value of which needs to be either maximised or minimised (i.e., optimised), typically subject to a number of constraints. Thus, techniques to efficiently solve such problems are an important area of research. A popular group of optimisation algorithms are the metaheuristics, approaches that specify how to search the space of solutions in a problem independent way so that high quality solutions are likely to result in a reasonable amount of computational time. Although metaheuristic algorithms are specified in a problem independent manner, they must be tailored to suit each particular problem to which they are applied. This thesis investigates a number of aspects of the application of the relatively new Ant Colony Optimisation (ACO) metaheuristic to different COPs.The standard ACO metaheuristic is a constructive algorithm loosely based on the foraging behaviour of ant colonies, which are able to find the shortest path to a food source by indirect communication through pheromones. ACO’s artificial pheromone represents a model of the solution components that its artificial ants use to construct solutions. Developing an appropriate pheromone representation is a key aspect of the application of ACO to a problem. An examination of existing ACO applications and the constructive approach more generally reveals how the metaheuristic can be applied more systematically across a range of COPs. The two main issues addressed in this thesis are biases inherent in the constructive process and the systematic selection of pheromone representations.The systematisation of ACO should lead to more consistently high performance of the algorithm across different problems. Additionally, it supports the creation of a generalised ACO system, capable of adapting itself to suit many different combinatorial problems without the need for manual intervention.

O desenvolvimento e aperfeiçoamento de métodos de otimização são pontos de profundo interesse em todas as áreas de pesquisa. Tais técnicas muitas vezes envolvem a aquisição de métodos de controle novos ou melhores, o que está diretamente ligado a duas tarefas importantes: a escolha de formas eficientes de monitoramento do processo e a obtenção de modelos confiáveis para a variável de interesse a partir de dados experimentais. Graças às suas diversas vantagens, os sensores óticos vêm sendo amplamente aplicados na primeira tarefa. Uma vez que é possível a utilização de vários tipos de espectroscopia através deste tipo de sensor, modelos capazes de lidar com dados espectrais estão se tornando cada vez mais atraentes. A segunda tarefa, por sua vez, depende não só de quais preditores são utilizados na construção do modelo, mas também de quantos. Como a qualidade do modelo depende também do número de variáveis selecionadas, é importante desenvolver métodos que identifiquem aqueles que explicam o máximo possível da variabilidade dos dados. O método de otimização Colônia de Formigas (ACO) aparece como uma ferramenta bastante útil na seleção de variáveis, podendo-se encontrar muitas variações desse algoritmo na literatura. O propósito deste trabalho é desenvolver métodos de seleção de variáveis com base no algoritmo ACO, conceitos estatísticos e testes de hipóteses. Para isso, diversos critérios de decisão foram implementados nas etapas do algoritmo referentes à atualização de trilha de feromônios (C1) e à seleção de modelos (C2). A fim de estudar estas modificações, foram realizados dois estudos de caso: o primeiro na área de bioprocessos e o segundo na área de caracterização de alimentos. Ambos os estudos mostraram que, em geral, os modelos com menores erros são obtidos utilizando-se métricas dos componentes do modelo, tal como o tamanho do intervalo de confiança de cada parâmetro e o teste-t de hipóteses. Além disso, a modificação do critério de seleção de modelos parece não interferir significativamente no resultado final do algoritmo. Por último, foi feito um estudo da aplicação dessas versões do ACO no campo de caracterização de combustíveis, mais especificamente diesel, associando-se duas análises espectroscópicas para predição do conteúdo de enxofre. Algumas das versões desenvolvidas mostraram-se superior ao algoritmo ACO utilizado como base para este trabalho, proposto por Ranzan (2014), e todas os versões forneceram melhores resultados na quantificação de enxofre que aqueles obtidos por PCR. Dessa forma, comprova-se a potencialidade de métricas implementadas no algoritmo ACO, associadas à espectroscopia, na seleção de preditores significativos. / The development and improvement of optimization methods are points of deep interest in all areas of research. These techniques are often related to the acquisition of new or better control methods, which are directly attached to two importante tasks: choosing efficient forms of process monitoring and obtaining reliable models for the monitored variable from experimental data. Due to their several advantagens, optical sensors are being widely applied in the first task. Since several types of spectroscopy are possible through this type of sensor, models capable of dealing with spectral data are becoming increasingly attractive. The second task depends not only on which predictors are used in the model, but also on how many. Since the quality of the model depends on the number of selected variables, it is important to develop methods that identify those that explain the greater amount of data variability as possible, without compromising the reliability of the model. The Ant Colony Optimization is an important tool for variable selection, being possible to find a lot of variations of this method in literature. The purpose of this work is to develop a method of variable selection based on the Ant Colony Optimization (ACO) algorithm, statistical concepts and hypothesis testing. For this purpose, several decision criteria for trail update (C1) and model selection (C2) were implemented within the routine. In order to study these modifications, two case study was conducted: one related to bioprocess monitoring and another one envolving the characterization of food products. Both studies showed that, in general, the models with the lowest errors were obtained through the use of model component metrics, such as the length of the confidence interval associated with each parameter and the t hypothesis test. Besides, the modification of the model selection criterion doesn’t seem to affect the algorithm final result. Finally, the aplicattion of these methods in the field of fuels characterization, specifically diesel fuel, was studied, associating two spectroscopical analyses in order to predict the sulfur content. Some of the new developed methods appeared to be better than the ACO algorithm used as basis in this work, proposed by Ranzan (2014), and all methods showed better results than those from the models constructed by PCR. Thus, it is proved the high potencial of using different metrics within ACO algorithm, associated with spectroscopy, in order to select significative predictors.

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O desenvolvimento e aperfeiçoamento de métodos de otimização são pontos de profundo interesse em todas as áreas de pesquisa. Tais técnicas muitas vezes envolvem a aquisição de métodos de controle novos ou melhores, o que está diretamente ligado a duas tarefas importantes: a escolha de formas eficientes de monitoramento do processo e a obtenção de modelos confiáveis para a variável de interesse a partir de dados experimentais. Graças às suas diversas vantagens, os sensores óticos vêm sendo amplamente aplicados na primeira tarefa. Uma vez que é possível a utilização de vários tipos de espectroscopia através deste tipo de sensor, modelos capazes de lidar com dados espectrais estão se tornando cada vez mais atraentes. A segunda tarefa, por sua vez, depende não só de quais preditores são utilizados na construção do modelo, mas também de quantos. Como a qualidade do modelo depende também do número de variáveis selecionadas, é importante desenvolver métodos que identifiquem aqueles que explicam o máximo possível da variabilidade dos dados. O método de otimização Colônia de Formigas (ACO) aparece como uma ferramenta bastante útil na seleção de variáveis, podendo-se encontrar muitas variações desse algoritmo na literatura. O propósito deste trabalho é desenvolver métodos de seleção de variáveis com base no algoritmo ACO, conceitos estatísticos e testes de hipóteses. Para isso, diversos critérios de decisão foram implementados nas etapas do algoritmo referentes à atualização de trilha de feromônios (C1) e à seleção de modelos (C2). A fim de estudar estas modificações, foram realizados dois estudos de caso: o primeiro na área de bioprocessos e o segundo na área de caracterização de alimentos. Ambos os estudos mostraram que, em geral, os modelos com menores erros são obtidos utilizando-se métricas dos componentes do modelo, tal como o tamanho do intervalo de confiança de cada parâmetro e o teste-t de hipóteses. Além disso, a modificação do critério de seleção de modelos parece não interferir significativamente no resultado final do algoritmo. Por último, foi feito um estudo da aplicação dessas versões do ACO no campo de caracterização de combustíveis, mais especificamente diesel, associando-se duas análises espectroscópicas para predição do conteúdo de enxofre. Algumas das versões desenvolvidas mostraram-se superior ao algoritmo ACO utilizado como base para este trabalho, proposto por Ranzan (2014), e todas os versões forneceram melhores resultados na quantificação de enxofre que aqueles obtidos por PCR. Dessa forma, comprova-se a potencialidade de métricas implementadas no algoritmo ACO, associadas à espectroscopia, na seleção de preditores significativos. / The development and improvement of optimization methods are points of deep interest in all areas of research. These techniques are often related to the acquisition of new or better control methods, which are directly attached to two importante tasks: choosing efficient forms of process monitoring and obtaining reliable models for the monitored variable from experimental data. Due to their several advantagens, optical sensors are being widely applied in the first task. Since several types of spectroscopy are possible through this type of sensor, models capable of dealing with spectral data are becoming increasingly attractive. The second task depends not only on which predictors are used in the model, but also on how many. Since the quality of the model depends on the number of selected variables, it is important to develop methods that identify those that explain the greater amount of data variability as possible, without compromising the reliability of the model. The Ant Colony Optimization is an important tool for variable selection, being possible to find a lot of variations of this method in literature. The purpose of this work is to develop a method of variable selection based on the Ant Colony Optimization (ACO) algorithm, statistical concepts and hypothesis testing. For this purpose, several decision criteria for trail update (C1) and model selection (C2) were implemented within the routine. In order to study these modifications, two case study was conducted: one related to bioprocess monitoring and another one envolving the characterization of food products. Both studies showed that, in general, the models with the lowest errors were obtained through the use of model component metrics, such as the length of the confidence interval associated with each parameter and the t hypothesis test. Besides, the modification of the model selection criterion doesn’t seem to affect the algorithm final result. Finally, the aplicattion of these methods in the field of fuels characterization, specifically diesel fuel, was studied, associating two spectroscopical analyses in order to predict the sulfur content. Some of the new developed methods appeared to be better than the ACO algorithm used as basis in this work, proposed by Ranzan (2014), and all methods showed better results than those from the models constructed by PCR. Thus, it is proved the high potencial of using different metrics within ACO algorithm, associated with spectroscopy, in order to select significative predictors.

O desenvolvimento e aperfeiçoamento de métodos de otimização são pontos de profundo interesse em todas as áreas de pesquisa. Tais técnicas muitas vezes envolvem a aquisição de métodos de controle novos ou melhores, o que está diretamente ligado a duas tarefas importantes: a escolha de formas eficientes de monitoramento do processo e a obtenção de modelos confiáveis para a variável de interesse a partir de dados experimentais. Graças às suas diversas vantagens, os sensores óticos vêm sendo amplamente aplicados na primeira tarefa. Uma vez que é possível a utilização de vários tipos de espectroscopia através deste tipo de sensor, modelos capazes de lidar com dados espectrais estão se tornando cada vez mais atraentes. A segunda tarefa, por sua vez, depende não só de quais preditores são utilizados na construção do modelo, mas também de quantos. Como a qualidade do modelo depende também do número de variáveis selecionadas, é importante desenvolver métodos que identifiquem aqueles que explicam o máximo possível da variabilidade dos dados. O método de otimização Colônia de Formigas (ACO) aparece como uma ferramenta bastante útil na seleção de variáveis, podendo-se encontrar muitas variações desse algoritmo na literatura. O propósito deste trabalho é desenvolver métodos de seleção de variáveis com base no algoritmo ACO, conceitos estatísticos e testes de hipóteses. Para isso, diversos critérios de decisão foram implementados nas etapas do algoritmo referentes à atualização de trilha de feromônios (C1) e à seleção de modelos (C2). A fim de estudar estas modificações, foram realizados dois estudos de caso: o primeiro na área de bioprocessos e o segundo na área de caracterização de alimentos. Ambos os estudos mostraram que, em geral, os modelos com menores erros são obtidos utilizando-se métricas dos componentes do modelo, tal como o tamanho do intervalo de confiança de cada parâmetro e o teste-t de hipóteses. Além disso, a modificação do critério de seleção de modelos parece não interferir significativamente no resultado final do algoritmo. Por último, foi feito um estudo da aplicação dessas versões do ACO no campo de caracterização de combustíveis, mais especificamente diesel, associando-se duas análises espectroscópicas para predição do conteúdo de enxofre. Algumas das versões desenvolvidas mostraram-se superior ao algoritmo ACO utilizado como base para este trabalho, proposto por Ranzan (2014), e todas os versões forneceram melhores resultados na quantificação de enxofre que aqueles obtidos por PCR. Dessa forma, comprova-se a potencialidade de métricas implementadas no algoritmo ACO, associadas à espectroscopia, na seleção de preditores significativos. / The development and improvement of optimization methods are points of deep interest in all areas of research. These techniques are often related to the acquisition of new or better control methods, which are directly attached to two importante tasks: choosing efficient forms of process monitoring and obtaining reliable models for the monitored variable from experimental data. Due to their several advantagens, optical sensors are being widely applied in the first task. Since several types of spectroscopy are possible through this type of sensor, models capable of dealing with spectral data are becoming increasingly attractive. The second task depends not only on which predictors are used in the model, but also on how many. Since the quality of the model depends on the number of selected variables, it is important to develop methods that identify those that explain the greater amount of data variability as possible, without compromising the reliability of the model. The Ant Colony Optimization is an important tool for variable selection, being possible to find a lot of variations of this method in literature. The purpose of this work is to develop a method of variable selection based on the Ant Colony Optimization (ACO) algorithm, statistical concepts and hypothesis testing. For this purpose, several decision criteria for trail update (C1) and model selection (C2) were implemented within the routine. In order to study these modifications, two case study was conducted: one related to bioprocess monitoring and another one envolving the characterization of food products. Both studies showed that, in general, the models with the lowest errors were obtained through the use of model component metrics, such as the length of the confidence interval associated with each parameter and the t hypothesis test. Besides, the modification of the model selection criterion doesn’t seem to affect the algorithm final result. Finally, the aplicattion of these methods in the field of fuels characterization, specifically diesel fuel, was studied, associating two spectroscopical analyses in order to predict the sulfur content. Some of the new developed methods appeared to be better than the ACO algorithm used as basis in this work, proposed by Ranzan (2014), and all methods showed better results than those from the models constructed by PCR. Thus, it is proved the high potencial of using different metrics within ACO algorithm, associated with spectroscopy, in order to select significative predictors.