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This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB06)

Open Access Research

An improved ant colony algorithm with diversified solutions based on the immune strategy

Ling Qin1, Yi Pan2, Ling Chen13 and Yixin Chen4*

Author Affiliations

1 Department of Computer Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210096, China

2 Department of Computer Science, Georgia State University, 34 Peachtree Street, Suite 1450, Atlanta, GA 30302-4110, USA

3 Department of Computer Science, Yangzhou University, Yangzhou, 225009, China

4 Department of Computer Science and Engineering, Washington University in St. Louis, St Louis, MO 63130, USA

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BMC Bioinformatics 2006, 7(Suppl 4):S3  doi:10.1186/1471-2105-7-S4-S3

Published: 12 December 2006

Abstract

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.