Improved Ant Colony Algorithm

The core enhancements encompass three strategic improvements:(1) Initially, we prioritize the algorithm's search capability to explore the solution space broadly. As the iteration progresses, we maintain a delicate equilibrium between exploration and exploitation, ensuring continued diversity while honing in on promising solutions. To achieve this, we integrate the hyperbolic tangent function, fine-tuning the ACO algorithm's behavior to adaptively adjust its search strategy across iterations.(2) Recognizing the tendency of heuristic algorithms to converge prematurely into local optima, we devise a max-min ant colony strategy. This innovation fortifies the algorithm's ability to avoid entrapment in suboptimal solutions, promoting more extensive search landscape exploration and enhancing the chances of discovering globally optimal or near-optimal routes. (3) Lastly, we embed a 2-opt local search algorithm within our framework. This refinement enables the algorithm to iteratively refine its solutions by adjusting the path nodes, leading to quicker convergence while maintaining solution quality. The early convergence judgments facilitated by this integration allow for timely termination of unpromising search directions, further optimizing computational efficiency.