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Background: Ant colony algorithm has emerged recently as a new meta- heuristic method, which is inspired from the behaviours of real ants for solving NP-hard problems. However, the classical ant colony algorithm also has its defects of stagnation and premature. This paper aims at remedying these problems.

Results: In this paper, we propose an adaptive ant colony algorithm that simulates the behaviour of biological immune system. The solutions of the problem are much more diversified than traditional ant colony algorithms.

Conclusion: The proposed method for improving the performance of traditional ant colony algorithm takes into account the polarization of the colonies, and adaptively adjusts the distribution of the solutions obtained by the ants. This makes the solutions more diverse so as to avoid the stagnation and premature phenomena.


This article was originally published in the journal BMC Bioinformatics.

© 2006 Qin et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.