Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Statistical dynamics of the Royal Road genetic algorithm
Theoretical Computer Science - Special issue on evolutionary computation
The ant colony optimization meta-heuristic
New ideas in optimization
A Graph-based Ant system and its convergence
Future Generation Computer Systems
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
A New Approach to Solve Permutation Scheduling Problems with Ant Colony Optimization
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modelling ACO: Composed Permutation Problems
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant colony optimization theory: a survey
Theoretical Computer Science
On the runtime analysis of the 1-ANT ACO algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
First steps to the runtime complexity analysis of ant colony optimization
Computers and Operations Research
Solution bias in ant colony optimisation: Lessons for selecting pheromone models
Computers and Operations Research
Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Ant Colony Optimization Algorithms for Shortest Path Problems
Network Control and Optimization
Running Time Analysis of ACO Systems for Shortest Path Problems
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Mathematical modeling and convergence analysis of trail formation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
An improved ant colony algorithm and simulation
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Runtime analysis of the 1-ANT ant colony optimizer
Theoretical Computer Science
Running time analysis of Ant Colony Optimization for shortest path problems
Journal of Discrete Algorithms
Runtime analysis of a simple ant colony optimization algorithm
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
A method for avoiding the feedback searching bias in ant colony optimization
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Evolutionary dynamics of ant colony optimization
MATES'12 Proceedings of the 10th German conference on Multiagent System Technologies
Countering the negative search bias of ant colony optimization in subset selection problems
Computers and Operations Research
A method for avoiding the searching bias in ACO deceptive problem solving
Web Intelligence and Agent Systems
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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.