The ant colony optimization meta-heuristic
New ideas in optimization
A Graph-based Ant system and its convergence
Future Generation Computer Systems
Modeling the dynamics of ant colony optimization
Evolutionary Computation
An Ant Algorithm with a New Pheromone Evaluation Rule for Total Tardiness Problems
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
A New Approach to Solve Permutation Scheduling Problems with Ant Colony Optimization
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Studies On The Dynamics Of Ant Colony Optimization Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ant colony optimization theory: a survey
Theoretical Computer Science
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
A method for avoiding the searching bias in ACO deceptive problem solving
Web Intelligence and Agent Systems
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The behaviour of Ant Colony Optimization (ACO) algorithms is studied on optimization problems that are composed of different types of subproblems. Numerically exact results are derived using a deterministic model for ACO that is based on the average expected behaviour of the artificial ants. These computations are supplemented by test runs with an ACO algorithm on the same problem instances. It is shown that different scaling of the objective function on isomorphic sub-problems has a strong influence on the optimization behaviour of ACO. Moreover, it is shown that ACOs behaviour on a subproblem depends heavily on the type of the other subproblems. This is true even when the subproblems are independent in the sense that the value of the objective function is the sum of the qualities of the solutions of the subproblems. We propose two methods for handling scaling problems (pheromone update masking and rescaling of the objective function) that can improve ACOs behaviour. Consequences of our findings for using ACO on real-world problems are pointed out.