An Adaptive Ant Colony Optimization for Solving Oblivious Routing Problem

In this paper, we propose an Adaptive Ant Colony Optimization to address the shortcomings of traditional ant colony algorithms. The main contributions of this paper are as follows: Adaptive Ant Colony Optimization (ADACO) improves the state transition rule, which allows the algorithm to adaptively adjust with population evolution, thereby accelerating convergence. The 2-opt operator is used to locally optimize better ant paths, improving the solution quality. The adaptive pheromone update rule improves search efficiency and prevents the algorithm from falling into local optima or premature stagnation.