Large-Scale Combinatorial Optimisation Algorithm

This article discusses large-scale combinatorial optimization problems, common in logistics and telecommunications. It highlights the XGain project, where multiple tasks (e.g., image analysis) are assigned to various processing devices, creating a complex optimization challenge. It explores different solution methods, including heuristic approaches like greedy algorithms, local search techniques (e.g., simulated annealing), and evolutionary strategies like genetic algorithms. Metaheuristics, such as ant colony optimization and particle swarm optimization, further improve efficiency. Divide-and-conquer strategies and AI-driven frameworks (e.g., HypOp and DISCO) offer innovative solutions. Finally, it emphasizes that the choice of method depends on problem characteristics, and hybrid approaches often yield superior results.