Ant Colony Optimization with environment changes: An application to GPS surveying

We propose a variant on the well-known Ant Colony Optimization (ACO) general framework where we introduce the environment to play an important role during the optimization process. Together with diversification and intensification, the environment is introduced with the aim of avoiding the search to get stuck at local optima. In this work, the environment is simulated by means of the Logistic map, that is used in ACO for perturbing the update of the pheromone trails. Our preliminary experiments show that our environmental ACO (eACO), with variable environment, outperforms the standard ACO on a set of instances of the GPS Surveying Problem (GSP).