Contribution au développement de concepts et outils d’aide à la décision pour l’optimisation via le plongement dans un réseau d’interconnexion parallèle.

Abstract

In a world characterized by complex and interconnected challenges, effective decision-making is paramount for addressing issues spanning environmental sustainability, transportation infrastructure improvement, and medical innovation. However, the growing complexity of these problems often exceeds traditional reasoning capabilities. Decision support systems, leveraging artificial intelligence techniques, offer promising avenues for navigating these challenges. This thesis focuses on addressing one such complex problem, the Traveling Salesman Problem (TSP), which finds applications in logistics, network planning, and bioinformatics. Despite advancements in TSP-solving methods, scalability and adaptability to dynamic scenarios remain persistent challenges. This research proposes a parallel simulation via an interconnection network topologybased optimization tool integrating advanced artificial intelligence techniques to tackle these issues. The methodology includes hierarchical clustering representations, graph embeddings, and hybrid parallel-solving strategies. Key contributions include novel clustering algorithms tailored for TSP optimization, integration with parallel computing architectures, and experimental validation showcasing superior performance compared to traditional methods. The thesis outlines theoretical foundations, explores parallel computing architectures and graph embedding techniques with the best quality, and presents a comprehensive evaluation of the proposed methodology. The findings contribute to enhancing decision-making processes and offer a robust framework for addressing complex optimization challenges in dynamic real-world settings.