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Dynamic Ant Colony Optimisation

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Abstract

Ant Colony optimisation has proved suitable to solve static optimisation problems, that is problems that do not change with time. However in the real world changing circumstances may mean that a previously optimum solution becomes suboptimal. This paper explores the ability of the ant colony optimisation algorithm to adapt from the optimum solution for one set of circumstances to the optimal solution for another set of circumstances. Results are given for a preliminary investigation based on the classical travelling salesman problem. It is concluded that, for this problem at least, the time taken for the solution adaption process is far shorter than the time taken to find the second optimum solution if the whole process is started over from scratch.

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References

  1. L. van Laarhoven and E. Aarts, Simulated Annealing: Theory and Applications, D Reidel Publishing Company: Dordecht, 1987, p. 186.

    Google Scholar 

  2. F. Glover, and M. Laguna, Tabu Search, Kluwer Academic Publishers: Boston, MA, 1997, p. 442.

    Google Scholar 

  3. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley: Reading, MA, 1989, p. 412.

    Google Scholar 

  4. M. Dorigo and G. Di Caro, “The ant colony optimization meta-heuristic,” in New Ideas in Optimization, edited by D. Corne, M. Dorigo, and F. Golver, McGraw-Hill, 1999, pp. 11–32.

  5. M. Dorigo, “Optimization, learning and natural algorithms, PhD Thesis,” Dipartimento di Elettronica, Politechico di Milano, Italy, 1992.

  6. M. Dorigo and L. Gambardella, “Ant colony system: A cooperative learning approach to the traveling salesman problem,” IEEE Transactions on Evolutionary Computing, vol. 1, pp. 53–66, 1997.

    Article  Google Scholar 

  7. M Dorigo and L. Gambardella, “Ant colonies for the traveling salesman problem,” Biosystems, vol. 43, pp. 73–81, 1997.

    Article  CAS  PubMed  Google Scholar 

  8. M. Dorigo, V. Maniezzo, and A. Colorni, “The ant system: Optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man and Cybernetics—Part B, vol. 26, pp. 29–41, 1996.

    Google Scholar 

  9. T. Stützle and M. Dorigo, “ACO algorithms for the traveling salesman problem,” in Evolutionary Algorithms in Engineering and Computer Science, edited by K. Miettinen, M. Makela, P. Neittaanmaki, and J. Periaux, Wiley.

  10. G. Reinelt TSPLIB95. Available from http://www.iwr.uni-heidelberg.de/iwr/comopt/soft/TSPLIB95/TSPLIB95.html

  11. D. Angus and T. Hendtlass, “Ant colony optimization applied to dynamically changing problem,” in Developments in Applied Artificial Intelligence. LNAI2358, Springer, 2002, pp. 618–627.

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Authors and Affiliations

  1. Centre for Intelligent Systems and Complex Processes, Swinburne University of Technology, VIC 3122, Australia

    Daniel Angus & Tim Hendtlass

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  1. Daniel Angus
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  2. Tim Hendtlass
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Correspondence to Daniel Angus.

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Angus, D., Hendtlass, T. Dynamic Ant Colony Optimisation. Appl Intell 23, 33–38 (2005). https://doi.org/10.1007/s10489-005-2370-8

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  • DOI: https://doi.org/10.1007/s10489-005-2370-8

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