A Graph-based Ant system and its convergence
Future Generation Computer Systems
Modeling the dynamics of ant colony optimization
Evolutionary Computation
When Model Bias Is Stronger than Selection Pressure
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Modelling ACO: Composed Permutation Problems
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization
Ant Algorithms: Theory and Applications
Programming and Computing Software
Structural advantages for ant colony optimisation inherent in permutation scheduling problems
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Ant colony optimization theory: a survey
Theoretical Computer Science
Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Solution bias in ant colony optimisation: Lessons for selecting pheromone models
Computers and Operations Research
A short convergence proof for a class of ant colony optimizationalgorithms
IEEE Transactions on Evolutionary Computation
Search bias in ant colony optimization: on the role of competition-balanced systems
IEEE Transactions on Evolutionary Computation
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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One of the obstacles in applying ant colony optimization (ACO) to the combinatorial optimization is that the search process is sometimes biased by algorithm features such as the pheromone model and the solution construction process. Due to such searching bias, ant colony optimization cannot converge to the optimal solution for some problems. In this paper, we define a new type of searching bias in ACO named feedback bias taking the k-cardinality tree problem as the test instance. We also present a method for avoiding the feedback searching bias. Convergence analysis of our method is also given. Experimental results confirm the correctness of our analysis and show that our method can effectively avoid the searching bias and can ensure the convergence for the problem.