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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.

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.

Mémoire (M.Inf.)-- Université du Québec à Chicoutimi, 2002. / Document électronique également accessible en format PDF. CaQCU

This thesis deals with the implementation of the metaheuristic algorithms into the Drools Planner. The Drools Planner is an open source tool for solving optimization problems. This work describes design and implementation of Ant colony optimization metaheuristics in the Drools Planner. Evaluation of the algorithm results is done by Drools Planner benchmark with different kinds of optimization problems.

A Wireless Sensor Network (WSN) is a group of spatially dispersed sensors that monitor the physical conditions of the environment and collect data at a central location. Sensor deployment is one of the main design aspects of WSNs as this a ffects network coverage. In general, WSN deployment methods fall into two categories: planned deployment and random deployment. This thesis considers planned sensor deployment of a Mobile Wireless Sensor Network (MWSN), which is defined as selectively deciding the locations of the mobile sensors under the given constraints to optimize the coverage of the network. Metaheuristic algorithms are powerful tools for the modeling and optimization of problems. The Krill Herd Algorithm (KHA) is a new nature-inspired metaheuristic algorithm which can be used to solve the sensor deployment problem. A Multi-Agent System (MAS) is a system that contains multiple interacting agents. These agents are autonomous entities that interact with their environment and direct their activity towards achieving speci c goals. Agents can also learn or use their knowledge to accomplish a mission. Multi-agent systems can solve problems that are very difficult or even impossible for monolithic systems to solve. In this work, a modification of KHA is proposed which incorporates MAS to obtain a Multi-Agent Krill Herd Algorithm (MA-KHA). To test the performance of the proposed method, five benchmark global optimization problems are used. Numerical results are presented which show that MA-KHA performs better than the KHA by finding better solutions. The proposed MA-KHA is also employed to solve the sensor deployment problem. Simulation results are presented which indicate that the agent-agent interactions in MA-KHA improves the WSN coverage in comparison with Particle Swarm Optimization (PSO), the Firefly Algorithm (FA), and the KHA. / Graduate

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / O paradigma de classificaÃÃo baseada em comitÃs tem recebido considerÃvel atenÃÃo na literatura cientÃfica em anos recentes. Neste contexto, redes neurais supervisionadas tÃm sido a escolha mais comum para compor os classificadores base dos comitÃs. Esta dissertaÃÃo tem a intenÃÃo de projetar e avaliar comitÃs de classificadores obtidos atravÃs de modificaÃÃes impostas a algoritmos de aprendizado nÃo-supervisionado, tais como as redes Fuzzy ART e SOM, dando origem, respectivamente, Ãs arquiteturas ARTIE (ART in Ensembles) e MUSCLE (Multiple SOM Classifiers in Ensembles). A sintonia dos parÃmetros e a seleÃÃo dos atributos das redes neurais que compÃem as arquiteturas ARTIE e MUSCLE foram tratados por otimizaÃÃo metaheurÃstica, a partir da proposiÃÃo do algoritmo I-HPSO (Improved Hybrid Particles Swarm Optimization). As arquiteturas ARTIE e MUSCLE foram avaliadas e comparadas com comitÃs baseados nas redes Fuzzy ARTMAP, LVQ e ELM em 12 conjuntos de dados reais. Os resultados obtidos indicam que as arquiteturas propostas apresentam desempenhos superiores aos dos comitÃs baseados em redes neurais supervisionadas.

Ces travaux ont été menés dans le cadre du projet européen FACETS-ITN. Nous avons contribué à la simulation de cellules corticales grâce à des données expérimentales d'électrophysiologie comme référence et d'un circuit intégré neuromorphique comme simulateur. Les propriétés intrinsèques temps réel de nos circuits neuromorphiques à base de modèles à conductance, autorisent une exploration détaillée des différents types de neurones. L'aspect analogique des circuits intégrés permet le développement d'un simulateur matériel temps réel à l'échelle du réseau. Le deuxième objectif de cette thèse est donc de contribuer au développement d'une plate-forme mixte - matérielle et logicielle - dédiée à la simulation de réseaux de neurones impulsionnels. / This work has been supported by the European FACETS-ITN project. Within the frameworkof this project, we contribute to the simulation of cortical cell types (employingexperimental electrophysiological data of these cells as references), using a specific VLSIneural circuit to simulate, at the single cell level, the models studied as references in theFACETS project. The real-time intrinsic properties of the neuromorphic circuits, whichprecisely compute neuron conductance-based models, will allow a systematic and detailedexploration of the models, while the physical and analog aspect of the simulations, as opposedthe software simulation aspect, will provide inputs for the development of the neuralhardware at the network level. The second goal of this thesis is to contribute to the designof a mixed hardware-software platform (PAX), specifically designed to simulate spikingneural networks. The tasks performed during this thesis project included: 1) the methodsused to obtain the appropriate parameter sets of the cortical neuron models that can beimplemented in our analog neuromimetic chip (the parameter extraction steps was validatedusing a bifurcation analysis that shows that the simplified HH model implementedin our silicon neuron shares the dynamics of the HH model); 2) the fully customizablefitting method, in voltage-clamp mode, to tune our neuromimetic integrated circuits usinga metaheuristic algorithm; 3) the contribution to the development of the PAX systemin terms of software tools and a VHDL driver interface for neuron configuration in theplatform. Finally, it also addresses the issue of synaptic tuning for future SNN simulation.

This work has been supported by the European FACETS-ITN project. Within the frameworkof this project, we contribute to the simulation of cortical cell types (employingexperimental electrophysiological data of these cells as references), using a specific VLSIneural circuit to simulate, at the single cell level, the models studied as references in theFACETS project. The real-time intrinsic properties of the neuromorphic circuits, whichprecisely compute neuron conductance-based models, will allow a systematic and detailedexploration of the models, while the physical and analog aspect of the simulations, as opposedthe software simulation aspect, will provide inputs for the development of the neuralhardware at the network level. The second goal of this thesis is to contribute to the designof a mixed hardware-software platform (PAX), specifically designed to simulate spikingneural networks. The tasks performed during this thesis project included: 1) the methodsused to obtain the appropriate parameter sets of the cortical neuron models that can beimplemented in our analog neuromimetic chip (the parameter extraction steps was validatedusing a bifurcation analysis that shows that the simplified HH model implementedin our silicon neuron shares the dynamics of the HH model); 2) the fully customizablefitting method, in voltage-clamp mode, to tune our neuromimetic integrated circuits usinga metaheuristic algorithm; 3) the contribution to the development of the PAX systemin terms of software tools and a VHDL driver interface for neuron configuration in theplatform. Finally, it also addresses the issue of synaptic tuning for future SNN simulation.

Le Système Électrique est soumis ces dernières années à plusieurs évolutions, depuis la dérégulationdu marché d'énergie à l'intégration de plus en plus importante de Générateurs Dispersés (GED). Ainsi,dans le cadre du concept de Smart Grid, les nouvelles technologies de l'information et de lacommunication (NTIC) offrent de nouvelles perspectives pour la gestion et l'exploitation des réseauxde distribution.Dans ce contexte, de nouveaux outils sont étudiés. Encore appelés Fonctions Avancéesd’Automatisation (FAA), le but principal de ces outils est d’utiliser tous les composants du réseau dedistribution de manière coordonnée en vue de les rendre plus actifs, flexibles et d’augmenter leurefficacité opérationnelle. Dans notre cas, nous avons étudié les fonctions associées à la reconfigurationen régime normal, du réglage de la tension et l’hybridation de ces deux derniers, tout en tenant comptede la présence des GED. En partant du comportement physique inhérent aux composants du réseau,plusieurs modèles ont été proposés. Certains sont tirés de la théorie des graphes et d’autres sur l’outilpuissant de la reformulation mathématique pour « convexifier » nos modèles. Cette modélisationadoptée répond à la fois à la nécessité de prendre en compte tous les moyens de réglages qui peuventêtre discrets (prises des transformateurs avec régleurs en charge ou des gradins de condensateurs),binaires (état de connectivité des composants) et continues (puissance réactive de la DG) et par lechoix des outils et des algorithmes d'optimisation mixte. En effet, la complexité de ces problèmes sonttelles que nous avons exploré à la fois des algorithmes méta-heuristiques (ACF : Algorithme desColonies de Fourmis) que déterministes (Décomposition de Benders Généralisée, Algorithme duBranch and Cut). / The Electric Power System is undergoing a lot of evolutions in recent years, including the energymarket deregulation and the increasing integration of Dispersed Generators (DG). Therefore, withinthe framework of Smart Grid concept, the New Information and Communication Technologies (NICT)provide new perspectives to manage and operate distribution networks.In this context, new tools, called Advanced Distribution Automation functions (ADA, are beingstudied). The main objective of these tools is to use all the distribution network components in acoordinated manner to make them more active and flexible, in addition to increasing their operationalefficiency. In our case, we studied the functions associated with the reconfiguration problem, thevoltage control problem and the hybridization of these two, while taking into account the presence ofthe DG. Based on the inherent components of network physical models, several models have beenproposed. Some are derived from the graph theory and others use powerful mathematicalreformulation to make our models convex. The adopted models answer to the necessity of taking intoaccount all regulation means, which can be discrete (On Load Tap-Changer and capacitor banks),binary (components connectivity such as lines or transformers) and continuous (DG reactive power ),and by the choice of tools and algorithms of mixed optimization. Indeed, the complexity of theseproblems is such that we have explored both algorithms: meta-heuristic (ACA, Ant Colony Algorithm)and deterministic (Generalized Benders Decomposition, Branch and Cut Algorithm).