Abstract
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.
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Chen, B., Chen, L. (2012). A Method for Avoiding the Feedback Searching Bias in Ant Colony Optimization. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_25
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DOI: https://doi.org/10.1007/978-3-642-30976-2_25
Publisher Name: Springer, Berlin, Heidelberg
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