Modeling the dynamics of ant colony optimization.
The dynamics of Ant Colony Optimization (ACO) algorithms is studied using a deterministic model that assumes an average expected behavior of the algorithms. The ACO optimization metaheuristic is an iterative approach, where in every iteration, artificial ants construct solutions randomly but guided by pheromone information stemming from former ants that found good solutions. The behavior of ACO algorithms and the ACO model are analyzed for certain types of permutation problems. It is shown analytically that the decisions of an ant are influenced in an intriguing way by the use of the pheromone information and the properties of the pheromone matrix. This explains why ACO algorithms can show a complex dynamic behavior even when there is only one ant per iteration and no competition occurs. The ACO model is used to describe the algorithm behavior as a combination of situations with different degrees of competition between the ants. This helps to better understand the dynamics of the algorithm when there are several ants per iteration as is always the case when using ACO algorithms for optimization. Simulations are done to compare the behavior of the ACO model with the ACO algorithm. Results show that the deterministic model describes essential features of the dynamics of ACO algorithms quite accurately, while other aspects of the algorithms behavior cannot be found in the model.
Merkle
Daniel
D
Institute AIFB, University of Karlsruhe, D-76128 Karlsruhe, Germany. merkle@aifb.uni-karlsruhe.de
Middendorf
Martin
M
D000465:Algorithms; D000818:Animals; D001000:Ants / Q000502:physiology*; D001522:Behavior, Animal; D008954:Models, Biological; D010675:Pheromones; D011157:Population Dynamics
D010675:Pheromones