Variation of ant colony optimization parameters for solving the travelling salesman problem

摘要

This paper describes the Ant Colony Optimization (ACO) algorithm for solving the Travelling Salesman Problem. ACO is a swarm intelligence approach where the agents (ants) communicate using a chemical substance called pheromone, which evaporates over time. This principle is used for finding the shortest possible route between cities based on previously investigated connections. The algorithm is evaluated to get results for a different number of cities corresponding to small, medium and, large problem instances. Accordingly, the problem size is varied to compare different results with the change in size of the ant colony and other parameters. The ant colony algorithm is also compared with other algorithms such as the Kohonen and the Christofides heuristic algorithms.