An ant colony optimization algorithm based on dynamic evaporation rate fitting

The study of swarm intelligence is more and more popular, much study have been done on swarm intelligence such as ACO (Ant Colony Optimization), and many applications also have been made in the field of combinatorial optimization. However, when solving combinatorial optimization problems, especially these problems with large scale, slow convergence and easy to fall into stagnation still restraint algorithm to be much more widely used. This paper presents the DERFACO (An ACO Algorithm Based on Dynamic Evaporation Rate Fitting) algorithm, using a mechanism of dynamic evaporation rate, which can achieve better balance between solution efficiency and solution quality, avoiding algorithm falling into local optimal. Experiments show that the DERFACO algorithm has better performance, its convergence rate increase by 12% or more. Furthermore, the DERFACO on other classic TSP instances also shows good performance.