Dans cette thèse, nous nous intéressons à l'étude des capacités de la méta heuristique d'optimisation par colonie de fourmis (Ant Colony Optimization - ACO) pour résoudre des problèmes d’optimisation combinatoire multi-objectif. Dans ce cadre, nous avons proposé une taxonomie des algorithmes ACO proposés dans la littérature pour résoudre des problèmes de ce type. Nous avons mené, par la suite, une étude expérimentale de différentes stratégies phéromonales pour le cas du problème du sac à dos multidimensionnel mono-objectif. Enfin,nous avons proposé un algorithme ACO générique pour résoudre des problèmes d'optimisation multi-objectif. Cet algorithme est paramétré par le nombre de colonies de fourmis et le nombre de structures de phéromone considérées. Il permet de tester et de comparer, dans un même cadre,plusieurs approches. Nous avons proposé six variantes de cet algorithme dont trois présentent de nouvelles approches et trois autres reprennent des approches existantes. Nous avons appliqué et comparé ces variantes au problème du sac à dos multidimensionnel multi-objectif / In this thesis, we investigate the capabilities of Ant Colony Optimization (ACO) metaheuristic to solve combinatorial and multi-objective optimization problems. First, we propose a taxonomy of ACO algorithms proposed in the literature to solve multi-objective problems. Then, we studydifferent pheromonal strategies for the case of mono-objective multidimensional knapsackproblem. We propose, finally, a generic ACO algorithm to solve multi-objective problems. Thisalgorithm is parameterised by the number of ant colonies and the number of pheromonestructures. This algorithm allows us to evaluate and compare new and existing approaches in thesame framework. We compare six variants of this generic algorithm on the multi-objectivemultidimensional knapsack problem

Dealing with optimization problems with more than one objective has been an important research area in evolutionary computation. The class of multi-objective problems (MOPs) is an important one because multi-objectivity exists in almost all aspects of human life; whereby there usually exist several compromises in each problem. Multi-objective evolutionary algorithms (MOEAs) have been applied widely in many real-world problems. This is because (1) they work with a population during the course of action, which hence offer more flexible control to find a set of efficient solutions, and (2) real-world problems are usually black-box where an explicit mathematical representation is unknown. However, MOEAs usually require a large amount of computational effort. This is a sub- stantial challenge in bringing MOEAs to practice. This thesis primarily aims to address this challenge through an investigation into issues of scalability and the balance between exploration and exploitation. These have been outstanding research challenges, not only for MOEAs, but also for evolutionary algorithms in general. A distributed framework of local models using explicit niching is introduced as an overarching umbrella to solve multi-objective optimization problems. This framework is used to address the two-part question about first, the role of communication messages and second, the role of explicit niching in distributed evolutionary multi-objective optimization. The concept behind the framework of local models is for the search to be conducted locally in different areas of the decision search space, which allows the local models to be distributed on different processing nodes. During the optimization process, local models interact (exchange messages) with each other using rules inspired from Particle Swarm Optimization (PSO). Hence, the hypothesis of this work is that running simultaneously several search engines in different local areas is better for exploiting local information, while exchanging messages among those diverse engines can provide a better exploration strategy. For this framework, as the models work locally, they gain access to some global knowledge of each other. In order to validate the proposed framework, a series of experiments on a wide range of test problems was conducted. These experiments were motivated by the following studies which in their totality contribute to the verification of our hypothesis: (1) studying the performance of the framework under different aspects such as initialization, convergence, diversity, scalability, and sensitivity to the framework's parameters, (2) investigating interleaving guidance in both the decision and objective spaces, (3) applying local models using estimation of distributions, (4) evaluating local models in noisy environments and (5) the role of communication messages and explicit niching in distributed computing. The experimental results showed that: (1) the use of local models increases the chance of MOEAs to improve their performance in finding the Pareto optimal front, (2) interaction strategies using PSO rules are suitable for controlling local models, and that they also can be coupled with specialization in order to refine the obtained non-dominated set, (3) estimation of distribution improves when coupled with local models, (4) local models work well in noisy environments, and (5) the communication cost in distributed systems with local models can be reduced significantly by using summary information (such as the direction information naturally determined by local models) as the communication messages, in comparison with conventional approaches using descriptive information of individuals. In summary, the proposed framework is a successful step towards efficient distributed MOEAs.

El control de las propiedades acústicas de los cristales de sonido (CS) necesita del estudio de la distribución de dispersores en la propia estructura y de las propiedades acústicas intrínsecas de dichos dispersores. En este trabajo se presenta un estudio exhaustivo de diferentes distribuciones, así como el estudio de la mejora de las propiedades acústicas de CS constituidos por dispersores con propiedades absorbentes y/o resonantes. Estos dos procedimientos, tanto independientemente como conjuntamente, introducen posibilidades reales para el control de la propagación de ondas acústicas a través de los CS. Desde el punto de vista teórico, la propagación de ondas a través de estructuras periódicas y quasiperiódicas se ha analizado mediante los métodos de la dispersión múltiple, de la expansión en ondas planas y de los elementos finitos. En este trabajo se presenta una novedosa extensión del método de la expansión en ondas planas que permite obtener las relaciones complejas de dispersión para los CS. Esta técnica complementa la información obtenida por los métodos clásicos y permite conocer el comportamiento evanescente de los modos en el interior de las bandas de propagación prohibida del CS, así como de los modos localizados alrededor de posibles defectos puntuales en CS. La necesidad de medidas precisas de las propiedades acústicas de los CS ha provocado el desarrollo de un novedoso sistema tridimensional que sincroniza el movimiento del receptor y la adquisición de señales temporales. Los resultados experimentales obtenidos en este trabajo muestran una gran similitud con los resultados teóricos. La actuación conjunta de distribuciones de dispersores optimizadas y de las propiedades intrínsecas de éstos, se aplica para la generación de dispositivos que presentan un rango amplio de frecuencias atenuadas. Se presenta una alternativa a las barreras acústicas tradicionales basada en CS donde se puede controlar el paso de ondas a su través. Los resultados ayudan a entender correctamente el funcionamiento de los CS para la localización de sonido, y para el guiado y filtrado de ondas acústicas. / Romero García, V. (2010). On the control of propagating acoustic waves in sonic crystals: analytical, numerical and optimization techniques [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8982 / Palancia