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Swarm Intelligence (SI) is a young field of study from which solutions to complex problems have been proposed based on how some natural organisms (e.g. ants, bees and others) achieve many of their daily tasks through simple sets of interactions. This thesis proposes two models for area exploration and coverage based on SI principles. These two models present a novel approach based on the combination of: ants’ pheromones, in order to keep track of visited places; and bird flocks or fish schooling, so as to move and collaborate. An implementation of both models was done in order to simulate and evaluate both the emergent behavior of the agents as well as their area exploration and coverage performance. Based on the outcome of the simulations it is concluded that both models are able to perform the exploration and coverage task and that one model is better than the other.

Depuis les années quarante, la consommation énergétique mondiale n'a cessé d'augmenter. Cette énergie étant majoritairement d'origine fossile, il en résulte une augmentation globale de température terrestre. De ce fait, il est devenu urgent de réduire les émissions de gaz à effet de serre pour stopper le changement climatique. Dans ce contexte, le développement de la production d'électricité à partir d'énergie solaire concentrée par voie thermodynamique est une solution prometteuse. Les efforts de recherche visent à rendre cette technologie plus efficace et plus compétitive économiquement. Dans ce but, ce manuscrit présente une méthode de conception optimale pour les centrales solaires à récepteur central. Elle tire parti des méthodes développées depuis de nombreuses années par le groupe de recherche StaRWest, regroupant notamment des chercheurs des laboratoires RAPSODEE (Albi), LAPLACE (Toulouse) et PROMES (Odeillo). Couplant des algorithmes de Monte Carlo à hautes performances et des algorithmes stochastiques d'optimisation, le code de calcul implémentant cette méthode permet la conception et l'optimisation d'installations solaires. Il est utilisé pour mettre en évidence les potentialités d'un type de centrales à récepteur central peu répandu : les centrales à réflecteur secondaire, également appelées centrales de type "beam down". / Since the early 40's, world energy consumption has grown steadly. While this energy mainly came from fossil fuel, its use has included an increase in temperatures. It has become urgent to reduce greenhouse gas emissions to halt climate change. In this context, the development of concentrated solar power (CSP) is a promising solution. The scientific community related to this topic has to focus on efficiency enhancement and economic competitiveness of CSP technologies. To this end, this thesis aims at providing an optimal design method applied to central receiver power plants. It takes advantage of methods developed over many years by the research group StaRWest. Both RAPSODEE (Albi), LAPLACE (Toulouse) and PROMES (Odeillo) researchers take an active part in this group. Coupling high performance Monte Carlo algorithms and stochastic optimization methods, the code we developed allows an optimal design of concentrated solar systems. This code is used to highlight the potential of an uncommon type of central receiver plants: reflective towers, also called "beam down" central receiver systems.

This doctoral thesis is focused on metrics and the criteria for socio-technical system diagnostics, which is a high profile topic for companies wanting to ensure the best in product quality. More and more customers are requiring suppliers to prove reliability in the production and supply quality of products according to given specifications. Consequently the ability to produce quality goods corresponding to customer requirements has become a fundamental condition in order to remain competitive. The thesis firstly lays out the basic strategies and rules which are prerequisite for a successful working company in order to ensure provision of quality goods at competitive costs. Next, methods and tools for planning are discussed. Planning is important in its impact on budget, time schedules, and necessary sourcing quantification. Risk analysis is also included to help define preventative actions, and reduce the probability of error and potential breakdown of the entire company. The next part of the thesis deals with optimisation problems, which are solved by Swarm based optimisation. Algorithms and their utilisation in industry are described, in particular the Vehicle routing problem and Travelling salesman problem, used as tools for solving specialist problems within manufacturing corporations. The final part of the thesis deals with Qualitative modelling, where solutions can be achieved with less exact quantitative information of the surveyed model. The text includes qualitative algebra descriptions, which discern only three possible values – positive, constant and negative, which are sufficient in the demonstration of trends. The results can also be conveniently represented using graph theory tools.