A Method for Avoiding the Feedback Searching Bias in Ant Colony Optimization
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.
Keywordsant colony optimization deceptive problems K-cardinality tree problem solution convergence
Unable to display preview. Download preview PDF.
- 1.Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press (2004)Google Scholar
- 5.Blum, C., Sampels, M.: Ant colony optimization for FOP shop scheduling: A case study on different pheromone representation. In: Proceedings of Congress on Evolutionary Computation 2002, vol. 2, pp. 1558–1563 (2002)Google Scholar
- 18.Blum, C.: Theoretical and practical aspects of ant colony optimization. Dissertations in Artificial Intelligence, vol. 2463. Akademische Verlagsgesellschaft Aka CmbH, Berlin (2004)Google Scholar