An Improved Ant Colony Optimization Algorithm Based on Immunization Strategy

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Abstract:

Ant colony optimization has been become a very useful method for combination optimization problems. Based on close connections between combination optimization and continuous optimization, nowadays some scholars have studied to apply ant colony optimization to continuous optimization problems, and proposed some continuous ant colony optimizations. To improve the performance of those continuous ant colony optimizations, here the principles of evolutionary algorithm and artificial immune algorithm have been combined with the typical continuous Ant Colony Optimization, and the adaptive Cauchi mutation and thickness selection are used to operate the ant individual, so a new Immunized Ant Colony Optimization is proposed.

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Periodical:

Advanced Materials Research (Volumes 490-495)

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66-70

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Online since:

March 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] M. Dorigo, V. Maniezzo, and A. Colorni, Ant System: Optimization by a colony of cooperaing agents, IEEE Trans. on SMC, 1996, pp.29-41.

DOI: 10.1109/3477.484436

Google Scholar

[2] Daniel Angus, Ant Colony Optimisation: From Biological Inspiration to an Algorithmic Framework, Technical Report No. TR013, Swinburne University of Technology, Melbourne, Australia, (2010).

Google Scholar

[3] M. Dorigo, and C. Blumb, Ant colony optimization theory: A survey, Theoretical Computer Science, vol. 344, 2009, pp.243-278.

DOI: 10.1016/j.tcs.2005.05.020

Google Scholar

[4] M. Dorigo, and T. Stutzle, Ant Colony Optimization, Cambridge: MIT Press, (2008).

Google Scholar

[5] E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford: Oxford University Press, (1999).

DOI: 10.1093/oso/9780195131581.001.0001

Google Scholar